    [House Hearing, 118 Congress]
    [From the U.S. Government Publishing Office]

                            AI INNOVATION EXPLORED:
                         INSIGHTS INTO AI APPLICATIONS
                       IN FINANCIAL SERVICES AND HOUSING

    =======================================================================

                                    HEARING

                                   before the

                        COMMITTEE ON FINANCIAL SERVICES

                         U.S. HOUSE OF REPRESENTATIVES

                        ONE HUNDRED EIGHTEENTH CONGRESS

                                 SECOND SESSION

                                   __________

                                 JULY 23, 2024

                                   __________

                               Serial No. 118-104

           Printed for the use of the Committee on Financial Services

        [GRAPHIC(S) NOT AVAILABLE IN TIFF FORMAT]

                                www.govinfo.gov

                                       _______

                     U.S. GOVERNMENT PUBLISHING OFFICE

    56-909 PDF                   WASHINGTON : 2026

                     HOUSE COMMITTEE ON FINANCIAL SERVICES

                   PATRICK McHENRY, North Carolina, Chairman

    FRENCH HILL, Arkansas, Vice          MAXINE WATERS, California, Ranking
        Chairman                             Member
    FRANK D. LUCAS, Oklahoma             SYLVIA R. GARCIA, Texas, Vice
    PETE SESSIONS, Texas                     Ranking Member
    BILL POSEY, Florida                  NYDIA M. VELAZQUEZ, New York
    BLAINE LUETKEMEYER, Missouri         BRAD SHERMAN, California
    BILL HUIZENGA, Michigan              GREGORY W. MEEKS, New York
    ANN WAGNER, Missouri                 DAVID SCOTT, Georgia
    ANDY BARR, Kentucky                  STEPHEN F. LYNCH, Massachusetts
    ROGER WILLIAMS, Texas                AL GREEN, Texas
    TOM EMMER, Minnesota                 EMANUEL CLEAVER, Missouri
    BARRY LOUDERMILK, Georgia            JAMES A. HIMES, Connecticut
    ALEXANDER X. MOONEY, West Virginia   BILL FOSTER, Illinois
    WARREN DAVIDSON, Ohio                JOYCE BEATTY, Ohio
    JOHN W. ROSE, Tennessee              JUAN VARGAS, California
    BRYAN STEIL, Wisconsin               JOSH GOTTHEIMER, New Jersey
    WILLIAM R. TIMMONS, IV, South        VICENTE GONZALEZ, Texas
        Carolina                         SEAN CASTEN, Illinois
    RALPH NORMAN, South Carolina         AYANNA PRESSLEY, Massachusetts
    DANIEL MEUSER, Pennsylvania          STEVEN HORSFORD, Nevada
    SCOTT FITZGERALD, Wisconsin          RASHIDA TLAIB, Michigan
    ANDREW R. GARBARINO, New York        RITCHIE TORRES, New York
    YOUNG KIM, California                NIKEMA WILLIAMS, Georgia
    BYRON DONALDS, Florida               WILEY NICKEL, North Carolina
    MIKE FLOOD, Nebraska                 BRITTANY PETTERSEN, Colorado
    MICHAEL LAWLER, New York
    ZACHARY NUNN, Iowa
    MONICA DE LA CRUZ, Texas
    ERIN HOUCHIN, Indiana
    ANDREW OGLES, Tennessee
                        Matthew Hoffmann, Staff Director

                             C  O  N  T  E  N  T  S

                                  ----------

                             Tuesday, July 23, 2024

                                                                       Page

                               OPENING STATEMENTS

    Hon. Patrick T. McHenry, Chairman of the Committee on Financial
      Services, a U.S. Representative from North Carolina............     1
    Hon. Maxine Waters, Ranking Member of the Committee on Financial
      Services, a U.S. Representative from California................     2

                                   STATEMENTS

    Hon. French Hill, Chairman of the Subcommittee on Digital Assets,
      Financial Technology, and Inclusion, a U.S. Representative from
      Arkansas.......................................................     4
    Hon. Stephen F. Lynch, Ranking Member of the Subcommittee on
      Digital Assets, Financial Technology, and Inclusion, a U.S.
      Representative from Massachusetts..............................     4

                                   WITNESSES

    Mr. Vijay Karunamurthy, Chief Technology Officer, Scale AI.......     5
        Prepared Statement...........................................     8
    Mr. Ondrej Linda, Senior Director, Personalization AI, Zillow....    16
        Prepared Statement...........................................    18
    Ms. Elizabeth Osborne, Chief Operations Officer, Great Lakes
      Credit Union...................................................    22
        Prepared Statement...........................................    24
    Mr. Frederick Reynolds, Deputy General Counsel for Regulatory
      Legal and Chief Compliance Officer, FIS Global.................    36
        Prepared Statement...........................................    38
    Ms. Lisa Rice, President and CEO, National Fair Housing Alliance
      (NFHA).........................................................    43
        Prepared Statement...........................................    45
    Mr. John Zecca, Executive Vice President, Global Chief Legal,
      Risk and Regulatory Officer, NASDAQ............................    78
        Prepared Statement...........................................    80

                                    APPENDIX

                  ADDITIONAL MATERIAL SUBMITTED FOR THE RECORD

    Hon. Maxine Waters:
        American Bankers Association.................................   138
        National Association of Residential Property Owners (NARPM)..   148
        U.S. Chambers of Congress....................................   150

                     RESPONSES TO QUESTIONS FOR THE RECORD

    Written responses to questions for the record from Mr. Vijay
      Karunamurthy
        Representative Maxine Waters.................................   154
    Written responses to questions for the record from Mr. Ondrej
      Linda
        Representative Maxine Waters.................................   157
    Written responses to questions for the record from Ms. Elizabeth
      Osborne
        Representative Maxine Waters.................................   158
    Written responses to questions for the record from Mr. Frederick
      Reynolds
        Representative Maxine Waters.................................   160

                                  LEGISLATION

    H.Res.------, Expressing the sense of the House of
      Representatives with respect to the use of artificial
      intelligence in the financial services and housing industries..   161
    H.R. 5808, the "Preventing Deep Fake Scams Act"..................   165
    H.R. 7781, the "Artificial Intelligence Practices, Logistics,
      Actions, and Necessities Act"..................................   170
    H.R. ------, a bill To require the Federal financial agencies to
      carry out a study and report on standardized descriptions for
      vendor-provided artificial intelligence systems, and for other
      purposes.......................................................   174

                            AI INNOVATION EXPLORED:
                         INSIGHTS INTO AI APPLICATIONS
                       IN FINANCIAL SERVICES AND HOUSING

                                  ----------

                             Tuesday, July 23, 2024

                         U.S. House of Representatives,
                               Committee on Financial Services,
                                                        Washington, DC.

        The committee met, pursuant to notice, at 10:04 a.m., in
    room 2128, Rayburn House Office Building, Hon. Patrick McHenry
    [chairman of the committee] presiding.
        Present: Representatives McHenry, Lucas, Sessions, Posey,
    Luetkemeyer, Huizenga, Wagner, Barr, Williams of Texas, Hill,
    Emmer, Loudermilk, Davidson, Rose, Steil, Timmons, Norman,
    Meuser, Fitzgerald, Kim, Donalds, Flood, Lawler, Nunn, De La
    Cruz, Houchin, Ogles, Waters, Velazquez, Sherman, Lynch, Green,
    Himes, Foster, Beatty, Vargas, Gottheimer, Gonzalez, Casten,
    Pressley, Horsford, Tlaib, Garcia, Williams of Georgia, and
    Nickel.
        Chairman McHenry. The committee will come to order.
        Without objection, the chair is authorized to declare
    recess of the committee at any time.
        The hearing is titled, ``AI Innovation Explored: Insights
    into AI Applications in Financial Services and Housing.''
        Without objection, all members will have 5 legislative days
    within which to submit extraneous materials to the chair for
    inclusion in the record
        I will now recognize myself for 4 minutes for an opening
    statement.

     OPENING STATEMENT OF HON. PATRICK T. McHENRY, CHAIRMAN OF THE
      COMMITTEE ON FINANCIAL SERVICES, A U.S. REPRESENTATIVE FROM
                             NORTH CAROLINA

        I want to thank our witnesses for being here today. This
    hearing is an historic milestone for the committee. Today we
    will examine use cases of artificial intelligence in financial
    services and housing. At its best, artificial intelligence
    holds the promise to enhance, not replace human progress.
    Unfortunately, its novelty and perceived risks have delayed
    adoption in many sectors of our economy.
        In January, Ranking Member Waters and I formed a bipartisan
    Working Group to explore the use cases of AI within our
    committee's jurisdiction. I appreciate the ranking member and
    her staff for their participation efforts in this endeavor. I
    also want to thank Working Group co-leads, Congressman Hill,
    the vice chair of the full committee, and Congressman Lynch for
    their leadership. This Working Group builds on the success of
    the Task Force on Artificial Intelligence in previous
    Congresses. The bipartisan Working Group embarked on a month's
    long fact-finding mission, including six roundtables and a trip
    to Massachusetts Institute of Technology (MIT), which I was
    pleased to be a part of, and countless other meetings. Members
    engaged with regulators, technologists, market participants,
    and consumer advocates to gain a deeper understanding of how AI
    is currently impacting our financial system, as well as how it
    may do so in the future. This was a comprehensive process,
    which is outlined in the bipartisan report released last week.
    This collaborative approach will benefit Congress as we work to
    keep pace with this evolving technology.
        To be sure, AI is not new in the financial services
    industry. Financial firms have used algorithms for decades,
    whether it is for analyzing large datasets or for trading.
    However, new developments in generative AI, or GenAI, have
    excited the imaginations of many. The financial services
    industry, one of the most highly regulated in America, is a
    clear entry point as policymakers attempt to tackle the thorny
    questions AI presents. We should be leery of rushing
    legislation. It is far better we get this right rather than to
    be first. In other words, policymakers should not measure twice
    and cut once. We want to get this right. American financial
    regulations are technology neutral and should remain that way.
        To be clear, the use of GenAI does not absolve a firm from
    complying with existing consumer protection laws. At the same
    time, our regulators must ensure that they are equipped to take
    on this new technological frontier. This committee should
    examine whether current regulation needs to be clarified and
    carefully consider if targeted legislation to close regulatory
    caps may be needed. Today we will discuss the importance of
    quality data, credit access model creation, model risk
    management, cybersecurity, human touch points, and a whole lot
    more. This hearing will advance the conversation on the use of
    AI in financial services and cement this committee's leadership
    on artificial intelligence policy.
        I will close with this. GenAI is here. We cannot put it
    back in the box. It will become more widely adopted to the
    point that it is embedded in our everyday lives. We cannot
    allow the fear of the unknown to thwart the United States' role
    as a hub for technological innovation. We should embrace it, we
    should be at the forefront of it, and we should ensure that all
    technologists want to be a part of the American ecosystem, our
    rule of law, and our regulatory structures. Far greater than
    the risks associated from AI itself are the risks of allowing
    foreign competitors and adversaries to lead the development,
    adoption, and terms of use. The United States should be at the
    forefront and remain at the forefront as we are right now. I
    yield back.
        I will now recognize the ranking member of the full
    committee, the gentlewoman from California, Ms. Waters, for 4
    minutes.

     OPENING STATMENT OF HON. MAXINE WATERS, RANKING MEMBER OF THE
      COMMITTEE ON FINANCIAL SERVICES, A U.S. REPRESENTATIVE FROM
                               CALIFORNIA

        Ms. Waters. Thank you, Mr. Chairman. Good morning. I would
    like to start by applauding and thanking Chairman McHenry for
    working with me to establish the committee's bipartisan Working
    Group on Artificial Intelligence. After months of roundtables
    moderated by Representatives Hill and Lynch with regulators and
    experts, we released a report detailing the implications of AI,
    including newer generative AI models on financial services and
    housing. I am pleased that this report lays out several
    recommendations for the committee to better protect consumers
    and our markets as AI is increasingly adopted. For example, the
    report makes clear that this committee must lead the House in
    overseeing AI, and that we must ensure our regulators enforce
    existing laws, including antidiscrimination laws and policies,
    so that any benefits of AI are broadly shared.
        Today's bipartisan hearing, the Working Group, and the
    report successfully build off the work this committee started
    under my leadership as chair when I stood up in Congress with
    the first ever Task Force on AI in 2019, chaired by Mr. Foster.
    This task force held more than a dozen hearings, exploring the
    potential risks and benefits of AI. This task force is part of
    a long list of efforts by Democrats to oversee new technologies
    and to ensure they are developed in the best interests of
    consumers.
        As companies forge ahead with AI, it is more important than
    ever that this committee and Congress not only continue this
    kind of oversight, but prioritize its review of AI and
    diversity, equity, and inclusion. As we know, AI is built by
    humans and relies on data that may reflect bias and systemic
    inequities, or perpetuate discrimination. For instance, people
    of color looking to secure mortgage loans have been denied or
    are overcharged significantly as a result of discriminatory
    algorithms. However, responsibly crafted, AI may help expand
    financial access for consumers, aspiring homeowners, renters,
    and businesses, while removing barriers to economic mobility.
        For this reason, I am pleased that this hearing is also
    considering my draft legislation, which would better inform
    consumers when products and services incorporate AI. The data
    is used to train AI-based decisions and provide regulators with
    the origin of the data used by AI. The Treasury issued this
    recommendation in its report entitled, ``Managing AI-Specific
    Cybersecurity Risk in the Financial Sector'' would benefit from
    a standardized description, similar to the food nutrition
    label, for vendor-provided AI systems and data providers.'' I
    am also pleased that we are considering Representative
    Pettersen's legislation, Preventing Deep Fake Scams Act, in
    this hearing, which sets up a task force to examine how banks
    and credit unions can protect themselves and their customers
    and members from fraud associated with AI.
        Through efforts like these, we can build more transparent
    and equitable systems, as well as trust and safety in an
    increasing AI-driven world, so I look forward to today's
    discussion, and I yield back.
        Chairman McHenry. The gentlelady yields back. The chair
    recognizes the chairman of the Subcommittee on Digital Assets,
    Financial Technology, and Inclusion, the gentleman from
    Arkansas, Mr. Hill, who is co-lead of the Task Force on AI as
    well. He will be recognized for 1 minute.

    STATEMENT OF HON. FRENCH HILL, CHAIRMAN OF THE SUBCOMMITTEE ON
      DIGITAL ASSETS, FINANCIAL TECHNOLOGY, AND INCLUSION, A U.S.
                      REPRESENTATIVE FROM ARKANSAS

        Mr. Hill. Thanks, Mr. Chairman. I am proud to be the co-
    chair with my good friend Mr. Lynch of Massachusetts of this
    Working Group on Artificial Intelligence. I see Mr. Lynch got
    the wardrobe memo today. Good. Dressing very sharp today.
        Tackling this complex issue in a bipartisan manner is not
    easy, but it is critically important. Leading up to this
    hearing, as the chairman said, we had six roundtable
    discussions and an outstanding deliberative visit to the
    academic experts at MIT. We also have a Working Group that Dr.
    Foster and I serve on behalf of Speaker Johnson and Minority
    Leader Jeffries at the House-wide level, demonstrating the
    importance this House has in making sure that we get AI right.
    We need to be innovative, and we need to be leaders in the
    world. Instead of being reactionary in our policymaking,
    Congress should take a measured approach to AI that seeks first
    to understand this rapidly evolving technology while exploring
    potential gaps in the law or supervision. We will begin that
    work today. I yield back.
        Chairman McHenry. The gentleman yields back. The chair
    recognizes the ranking member of the Subcommittee on Digital
    Assets, Financial Technology, and Inclusion, the gentleman from
    Massachusetts, Mr. Lynch, co-chair of the AI Working Group, for
    1 minute.

       STATEMENT OF HON. STEPHEN F. LYNCH, RANKING MEMBER OF THE
       SUBCOMMITTEE ON DIGITAL ASSETS, FINANCIAL TECHNOLOGY, AND
          INCLUSION, A U.S. REPRESENTATIVE FROM MASSACHUSETTS

        Mr. Lynch. Thank you, Mr. Chairman, and to the ranking
    member. Thank you for holding this important hearing. I was
    honored, as the gentleman from Arkansas mentioned, to co-lead
    the Working Group on Artificial Intelligence alongside my
    friend Mr. Hill. In June, we led the Working Group on a visit
    to MIT. Through this work, we had thoughtful conversations with
    industry leaders, academics, and technologists to learn about
    the policy implications of AI.
        The explosion of AI adoption across industries has raised
    serious questions about implications for financial access,
    employment, data privacy, and intellectual property. While AI
    has the potential to expand access to financial products, we
    need to ensure AI models are explainable, transparent, and do
    not lead to bias. Last, it is critical that the U.S. remain a
    leader in AI safety and security, and I look forward to
    continued collaboration with my colleagues as we further
    explore policy actions that are needed. I yield back.
        Chairman McHenry. The gentleman yields back. I want to
    thank the co-chairs of the Working Group for their leadership.
    The day we spent at MIT was fascinating, deeply informative,
    and I would commend that to other committee members who were
    not able to attend that day. I appreciate Mr. Hill, Mr. Lynch
    for your leadership, and also, Mr. Lynch wanted to show off his
    native Boston. Neither French nor I, in particular, came away
    with an accent. So thank you for that.
        Ms. Waters. Excuse me, Mr. Chairman. Did you mention Sylvia
    Garcia was there?
        Chairman McHenry. Oh yes. Yes. Yes, and Ms. Garcia was
    there, but I was just thanking them for that. Let me recognize
    the testimony of our distinguished panel. We have a large panel
    today, so I am going to introduce everybody at once, then we
    will go one by one, and you will have 5 minutes for an oral
    presentation of your testimony.
        We recognize today Mr. Vijay Karunamurthy, chief technology
    officer at Scale AI; Dr. Ondrej Linda, senior director of
    personalization AI at Zillow; Ms. Elizabeth Osborne, the chief
    operations officer at Great Lakes Credit Union; Mr. Frederick
    Reynolds, deputy general counsel for regulation and legal and
    chief compliance officer at FIS Global; Ms. Lisa Rice,
    president and CEO of the National Fair Housing Alliance; Mr.
    John Zecca, executive vice president and global chief legal,
    risk, and regulatory officer at Nasdaq. Thank you for taking
    time to be here today. Each of you will be recognized for 5
    minutes for an oral presentation of your testimony, and without
    objection, your written testimony will be made a part of the
    record.
        I will note that we have House floor votes that are
    imminent, and I will consult with the ranking member on the
    appropriate time for us to break, but we are going to try to
    get through as much as we possibly can so we can get to member
    questions.
        With that, Mr. Karunamurthy.

      STATEMENT OF VIJAY KARUNAMURTHY, CHIEF TECHNOLOGY OFFICER,
                                SCALE AI

        Mr. Karunamurthy. Chairman McHenry, Ranking Member Waters,
    and members of the House Financial Services Committee, thank
    you for the opportunity to be here today. It is an honor to
    discuss the exciting role that AI is playing in the financial
    services and housing sectors, industry trends in adopting AI,
    and recommendations for the safe deployment of AI. My name is
    Vijay Karunamurthy, and I am the field chief technology officer
    at Scale AI.
        Scale is helping the world's leading technology companies
    build, deploy, and evaluate artificial intelligence. Since
    2016, Scale's mission has been to build the data foundry for
    AI. Every day we work with the leading automotive companies
    building high-quality computer vision datasets, nearly every
    generative AI company to ensure that their large language
    models, or LLMs, have the highest quality data, the world's
    most prominent companies applying artificial intelligence, and
    the U.S. Government to bring the best-in-class commercial AI
    technology to the Department of Defense and other agencies.
    Underpinning all of this is our work on AI tests and evaluation
    to ensure that our customer's AI is safe to deploy. Our years
    of work on the frontlines of nearly every major AI innovation
    gives us a unique view on how to build and deploy high quality
    and safe AI systems.
        AI has the potential to deliver tremendous benefits for
    society, but this can only happen if it is deployed safely and
    responsibly. The financial services industry has long
    recognized the value of AI and has begun to adopt the
    technology back in 1982, with the housing sector following
    shortly thereafter. Due to the long history of its use, Federal
    and State regulators have also incorporated many aspects of AI
    into existing regulations for those sectors. In November 2022,
    the AI world changed with the release of Chat Generative Pre-
    trained Transformer (ChatGPT), and today, nearly every sector
    of our economy is actively looking to harness the power of
    generative AI. Based on our role in the AI ecosystem, I want to
    highlight three important trends that we are seeing to most
    successfully deploy of AI in the financial services and housing
    sectors.
        First, foundational AI elements are critical. At a high
    level, AI is only as good as the data that it is trained on.
    High-quality data produces high-quality outputs. As the leading
    companies in the world deploy AI systems, they should ensure
    that their data strategies and budgets are in place so that
    they can turn raw data into AI-ready data, which means that is
    curated, transformed, and annotated so that AI systems can
    leverage it.
        Secondly, fine-tuned proprietary data is a game changer.
    The leading commercial models today can perform at a master's
    degree or a Ph.D. level. However, companies need not only
    master's and Ph.D. level capabilities, but also industry-
    specific expert level capabilities that only come from years of
    institutional knowledge and data within the company. This is
    where proprietary data comes in. Training models with the
    company's data gives AI the institutional knowledge that allows
    the enterprise to derive the most value from it. For example,
    JPMorgan reportedly has roughly 150 petabytes of proprietary
    data, or 150 times the entirety of ChatGPT's initial training
    data. Harnessing the power of that data would create incredible
    insights for this bank to better serve its customers because
    this data is directly relevant. If companies want to harness
    the power of AI for their own applications, they must use the
    best-in-class commercial models and leverage their data to
    build the highest quality AI systems.
        Lastly, deploying AI must be done safely and responsibly.
    Test and evaluation is the best way to ensure that AI is safe
    to deploy because it entails robust testing of the AI system to
    better understand the potential vulnerabilities and
    comprehensive evaluations to produce a holistic picture of the
    AI system as a whole. For this reason, the U.S. Government has
    long recognized the importance of developing the standards,
    frameworks, and methodologies that underpin test and
    evaluation. As companies leverage AI systems, they need to
    consistently test and monitor them to ensure that they remain
    safe to deploy.
        Given the amount of time that both sectors have used AI,
    new regulations may not be necessary. However, a thorough and
    comprehensive gap analysis should be conducted to confirm
    whether or not these regulations apply. If gaps exist, we must
    fill them. We believe that should be done with risk-based
    sector specific regulations, but AI safety metrics are still
    needed. In practice, this may be as simple as regulators
    stating that AI is held to the same standards as humans, but it
    must be government's role to establish these metrics.
        Lastly, government and industry should collaborate to
    ensure that the workforce of the future is ready for this
    technology. If we do not train our workforce, we will not reach
    the limitless potential of AI.
        AI is the most promising technological innovation of our
    time, but it will only reach its full potential if it is
    deployed in a safe, responsible, and thoughtful manner. This is
    skills mission. Thank you again for the opportunity to be here
    today, and I look forward to your questions.

        [Prepared statement of Mr. Karunamurthy follows:]

        [GRAPHIC(S) NOT AVAILABLE IN TIFF FORMAT]

        Chairman McHenry. Thank you for your testimony. We will now
    recognize Dr. Linda for 5 minutes.

    STATEMENT OF ONDREJ LINDA, SENIOR DIRECTOR, PERSONALIZATION AI,
                                 ZILLOW

        Mr. Linda. Thank you, Chairman Henry, Ranking Member
    Waters, Congressman Hill, Congressman Lynch, and members of the
    committee for holding this hearing on artificial intelligence
    (AI) and for your work over the past month to examine issues
    related to AI. I also want to acknowledge and thank the other
    witnesses, including Lisa Rice, for her leadership and
    dedication to housing fairness and equity.
        My name is Ondrej Linda, and I am Zillow's senior director
    of AI. My expertise is in developing and deploying large-scale
    AI systems in leading AI organizations to create new
    experiences to empower homebuyers, renters, and sellers. For
    the past several years, I have also been deeply involved in
    spearheading responsible AI practices at Zillow. Zillow is the
    leading real estate marketplace, and with over 200 million
    monthly unique visitors comes a tremendous responsibility to
    lead the way in innovating responsible AI technology.
        Today I would like to discuss how Zillow uses AI to help
    consumers on their housing journey, how we work to identify,
    reduce, and mitigate bias and discrimination, and the promise
    we see when it comes to AI's ability to address housing equity
    and access in this country.
        Home searching and buying processes are complicated and
    confusing and often fraught with emotions. In fact, according
    to 2022 Zillow consumer survey, over half of homebuyers
    reported they actually cried during the process. Since that
    study was conducted, affordability and supply challenges have
    led to homebuyer sentiments sitting at an all-time low. Zillow
    is focused on helping consumers navigate this daunting market
    and home-buying process by empowering them with information. In
    fact, Zillow's first product, Zestimate, was an AI-driven
    valuation model that informs people for free of their home's
    potential estimated worth. Before Zestimate, homebuyers and
    sellers had virtually no public access to pricing estimates.
        Traditional online home listings, which show only two-
    dimensional images, have not evolved for decades, and we have
    changed that. Our AI-powered 3D home tours and immersive floor
    plans is increasing transparency and opening up homes to people
    who cannot tour in person. Thanks to AI, consumers on Zillow
    can get personalized recommendation that expands their search
    to a broader set of housing inventory. AI tools could also play
    a role in decreasing appraisal bias and unlocking wealth for
    people of color by allowing more objective and efficient
    approaches to understanding home valuation, including in rural
    areas. We are examining how AI can help to modernize existing
    industry frameworks with the aim of decreasing costs,
    streamlining paperwork, and reducing legacy access burdens in
    the housing market.
        At Zillow, we believe that AI can be a great tool to help
    more people get a home and reduce bias and discrimination, but
    only if used responsibly. I am proud to be part of an
    organization that has adopted and publicly shared ethical AI
    principles to guide our work and elevate industry standards,
    fairness, safety, accountability, transparency, and
    inclusiveness. To deliver on these promises, we recently open
    sourced our Fair Housing Classifier. That means that civil
    rights groups, tech companies, and others in the real estate
    industry can adopt it for free and collaborate with us to help
    improve it.
        At Zillow, we are constantly thinking about the next
    innovation and how we can have a positive impact on people's
    lives. As we speak, Zillow's entire technology and product
    organization is at our longstanding Hack Week. They are
    evaluating and experimenting with new technologies, including
    AI, to unlock new opportunities. Just down the street at FHFA,
    Zillow is participating in a tech sprint on generative AI in
    housing finance. We truly believe that these public-private
    partnership and cross sector collaborations will be key in
    harnessing the power of AI to make housing more accessible to
    all.
        Again, I want to thank the committee for holding this
    hearing and your focus on this topic. We look forward to
    working with all elected officials to identify ways to create a
    pathway to AI innovation in housing that help inform, protect,
    and empower customers. I look forward to your questions.

        [Prepared statement of Mr. Linda follows:]

        [GRAPHIC(S) NOT AVAILABLE IN TIFF FORMAT]

        Chairman McHenry. Thank you for your testimony. After this
    testimony of Ms. Osborne, we will recess the committee for
    floor votes. For the panel, that should take until about 11:15
    till we get back.
        So with that, I will recognize Ms. Osborne for 5 minutes.

    STATEMENT OF ELIZABETH OSBORNE, CHIEF OPERATIONS OFFICER, GREAT
                           LAKES CREDIT UNION

        Ms. Osborne. Thank you. Good morning, Chairman McHenry,
    Ranking Member Waters, and members of the committee. I am
    Elizabeth Osborne, the chief operations officer for Great Lakes
    Credit Union. We were founded in 1938 and are a low-income
    designated, Housing & Urban Development (HUD)-certified
    financial cooperative in the Chicago land market and
    surrounding areas. We serve over 90,000 members in that market.
    At the heart of our mission is the credit union philosophy of
    people helping people. At Great Lakes Credit Union (GLCU), that
    includes state-of-the-art technology and a range of financial
    solutions in place for one goal: to enable our members to live
    life on their terms.
        Credit unions like mine are committed to using AI safely,
    securely, and with the goal of helping members meet their
    financial needs. AI enables credit unions to compete more
    efficiently with larger banks and non-bank companies in the
    financial services arena. AI has not altered credit unions'
    historical role as relationship lenders committed to
    maintaining a close bond with the communities they serve.
        AI usage at credit unions generally falls into three
    categories: underwriting support, combating fraud, and customer
    service, which is the area we have focused on at GLCU. As part
    of our commitment to innovation to our members, GLCU entered
    into a partnership with interface.ai and launched Olive, our
    virtual conversational AI assistant, with the goal to provide
    our members with an enhanced, easy-to-use, and voice-enabled
    virtual system. Olive is able to handle a wide range of member
    inquiries and requests such as account balance, transaction
    history, transfers between accounts, and more. Olive is
    available 24/7 through multiple channels, including phone and
    chat.
        Since the introduction of Olive, GLCU has realized
    remarkable results in terms of member satisfaction, member call
    center performance, and employee engagement. For example, Olive
    consistently handles over 60 percent of total inbound calls
    during business hours and over 75 percent of all calls after
    business hours, this compared to less than 25 percent handling
    rate with our previous telephone banking solution which did not
    include AI technology. Olive has increased the number of calls
    fully serviced by the virtual assistant by over 200 percent
    since launch. Olive provides recommended responses and advice
    to call center employees when a member requires special
    assistance or prefers to speak with an employee. An inherent
    benefit of Olive is that it has enabled the credit union to
    elevate the job description and level for call center agents as
    their role moved from providing transactional support for
    members to providing more consultative advisory support. This
    shift has resulted in a higher pay grade for staff and a better
    career path for our call center employees.
        GLCU management is continuously working to improve and
    expand Olive's capabilities and features. One of our main goals
    this year is to upgrade Olive to speak Spanish to better serve
    our Spanish-speaking members, which represent a large
    percentage of the GLCU membership base. In addition, GLCU is
    also exploring additional methods to leverage AI to improve
    operations and services. One tool is Microsoft Power Automate,
    which enables the credit union to automate repetitive and
    manual tasks. By using Power Automate, staff can save time and
    resources, reduce errors and delays, and increase productivity
    and accuracy.
        As policymakers grapple to legislate and regulate in this
    emerging environment, it is important to recognize many
    existing laws are technology agnostic and still apply. As with
    other technologies, consumer financial protections and
    antidiscrimination rules continue to have broad applicability,
    whether the decisionmaking is human or AI. Existing regulation
    requires credit unions to adopt robust practices to ensure that
    the use of new technology does not jeopardize safety or the
    rights of individual members. It is encouraging to see
    recognition of the broad applicability of existing fair lending
    and consumer financial protection laws in the bipartisan
    Working Group report.
        In conclusion, AI is an important tool for credit unions,
    helps us to enhance service to our members, and provides
    services that economies of scale may not otherwise allow us to
    do. At GLCU, Olive is a strategic partner that helps us fulfill
    the underlying mission of a credit union. Our use of Olive is a
    prime example of how credit unions can effectively deploy the
    use of AI to improve the lives of our members.
        I thank you for this opportunity to appear here today and
    welcome any questions you may have.

        [Prepared statement of Ms. Osborne follows:]

        [GRAPHIC(S) NOT AVAILABLE IN TIFF FORMAT]

        Chairman McHenry. Thank you for your testimony. Pursuant to
    the chair's previous announcement, the committee stands in
    recess. I encourage members to return immediately after votes.
    With that, we recess.
        [Recess.]
        Chairman McHenry. The committee will come to order. We will
    continue with the testimony from our panel.
        Next up in the order is Mr. Reynolds. You are now
    recognized for 5 minutes. I feel like it is icing the kicker
    for like the final three. The first three, you got off easy.
    Sorry. So Mr. Reynolds, you are recognized for 5 minutes.

      STATEMENT OF FREDERICK REYNOLDS, DEPUTY GENERAL COUNSEL FOR
       REGULATORY LEGAL AND CHIEF COMPLIANCE OFFICER, FIS GLOBAL

        Mr. Reynolds. Thank you, Chairman McHenry, Ranking Member
    Waters, Congressman Hill and Congressman Lynch, and members of
    the committee. My name is Frederick Reynolds, and I am deputy
    general counsel and chief compliance officer for FIS. During my
    time as a Federal prosecutor, as the deputy director for
    Financial Crimes Enforcement Network (FinCEN), and now in the
    private sector, I have had the opportunity to see firsthand how
    new and emerging technologies impacted the financial sector and
    national security, the two of which are inextricably linked.
    Artificial intelligence, if responsibly utilized and regulated,
    can have a significant and positive impact in modernizing
    products and services, increasing financial access and
    inclusion, and enhancing ethical standards and customer
    outcomes. However, it also presents, in particular, generative
    AI, substantial risks.
        FIS is a financial technology company that delivers
    technology solutions to major financial institutions around the
    globe. Our operations power the global economy, managing money
    at rest, money in motion, and money at work. Given the vital
    role that we play in financial services, we are acutely
    conscious of our significant responsibility to ensure that our
    products and services are both safe and leading edge.
        One area that FIS is currently focused on is the impact of
    technologies like GenAI and Web3 on fraud prevention, identity
    verification, and existing customer due diligence procedures.
    As these technologies become more widely adopted, our
    traditional means of assessing trust, i.e., identity, are less
    effective through anonymous or pseudo-anonymous online
    identities and deepfakes. This, coupled with an increased
    ability to avoid the formal financial system, puts the overall
    customer due diligence (CDD) and anti-money laundering (AML)
    programs at risk, recognizing that the current AML controls,
    which require full knowledge of a user's legal identity as a
    proxy for trust, may be at risk of becoming ineffective. It is
    imperative that the industry look to next-generation CDD
    identity controls to take advantage of GenAI and Web3, but also
    protect against their misuse. This requires us to rethink how
    to define identity and create solutions, such as digital
    identity or zero-knowledge identity proofs that focus on
    behavior and trust within the financial ecosystem rather than
    easily fake data elements.
        As chief compliance officer, I have a responsibility to
    safeguard FIS and its customers by ensuring adherence to laws,
    regulations, and ethical standards. This commitment extends to
    FIS' advancements within AI. Our priority is clear: to deliver
    products that our clients can trust, avoiding high-risk and
    uncertain solutions that do not meet legal, regulatory, and
    ethical standards.
        FIS is continuously exploring ways to enhance what we can
    offer to our clients, and we deeply understand the unique
    challenges that our clients, both large and small, face and the
    opportunities that AI can bring to these businesses. We are
    embracing innovation but taking measured steps toward it to
    deliver real value to those we serve. We pride ourselves on
    robust risk management programs to ensure that we are
    effectively managing risk, while, at the same time, allowing us
    to be agile and innovate. As we further integrate AI and GenAI
    into our operations, our approach remains grounded in six core
    principles--transparency, explainability and auditability,
    accuracy and accountability, privacy and data protection,
    security--all of which must be rooted in fairness and human
    values.
        We believe the current regulatory regime governing
    financial services are robust enough to support the responsible
    adoption of AI technologies. Consumer protection,
    antidiscrimination, and financial market regulations provide a
    solid foundation for using AI in financial services. These
    regulations ensure that AI technologies are used ethically and
    responsibly. Still, considering we are so early in the
    innovation curve of GenAI, it is imperative that we continually
    reassess regulations to ensure that they allow for innovation
    in a risk-thoughtful manner. The one area, however, we do
    believe that congressional action is critical is updating our
    current customer identification program (CIP) and CDD regime
    and the development of a digital identity system.
        The AI regulatory landscape is, more broadly, not as stable
    and not as defined as it could be. Globally, the regulatory
    environment for AI is uncertain, with each country adopting a
    unique approach based on varying risk considerations. While
    consistent global standards are ideal, it is imperative to our
    ability to innovate that we have at a minimum a consistent
    Federal approach to AI regulation. Fragmented regulation across
    States will introduce complexity, burden innovation, and impede
    the positive impact that can come with AI technology adoption.
    We urge policymakers to adopt a cohesive regulatory framework
    that not only promotes innovation, but also ensures robust
    compliance with regulatory requirements.
        Thank you once again for having myself and FIS at this
    hearing and look forward to further discussions and questions.
    Thank you.

        [Prepared statement of Mr. Reynolds follows:]

        [GRAPHIC(S) NOT AVAILABLE IN TIFF FORMAT]

        Chairman McHenry. Thank you. Ms. Rice, you are now
    recognized for 5 minutes.

       STATEMENT OF LISA RICE, PRESIDENT AND CEO, NATIONAL FAIR
                        HOUSING ALLIANCE (NFHA)

        Ms. Rice. Chairman McHenry, Ranking Member Waters, and
    distinguished members of the House Financial Services
    Committee, thank you for the opportunity to share information
    about NFHA's work regarding the application of artificial
    intelligence in the housing and financial services sectors.
    NFHA is the Nation's only national civil rights agency solely
    dedicated to eliminating all forms of discrimination. As a
    trade association for more than 170 fair housing agencies
    located throughout the country, NFHA works to support survivors
    of discrimination and build resilient, inclusive, well-
    resourced communities where everyone can thrive.
        AI holds great promise for improving systems, democratizing
    housing and credit opportunities, lowering costs, and
    increasing productivity. It also holds great dangers for
    perpetuating bias, spreading misinformation, and generating
    other harms. For NFHA, AI and technology are the new civil and
    human rights frontier. How we develop and use these powerful
    systems will dictate whether people will have fair access to
    critical opportunities in a manner that is safe, or whether
    people will continue to be presented with prohibitive systems
    that can generate inaccurate decisions that consumers do not
    understand.
        Multiple enforcement actions, including those brought by
    NFHA, as well as research conducted by many stakeholders, show
    there are multiple systems in use today that produce harmful
    impacts. Credit scoring and insurance scoring systems that rely
    on outdated, unrepresentative, or inaccurate data result in
    consumers being denied for the credit and other services they
    need to purchase a home, obtain insurance, or secure rental
    housing. Bias systems also result in consumers paying higher
    costs unnecessarily. For example, one study revealed that Black
    and Latino borrowers are being overcharged by risk-based
    pricing systems to the tune of $765 million per year. The
    Casualty Actuarial Society acknowledged that algorithmic bias
    can manifest in systems used in the insurance sector, including
    underwriting, pricing, and claims models. Tenant screening
    selection and dynamic pricing systems can also perpetuate bias
    and generate decisions that are inexplicable.
        Automated valuation models, or AVMs, can also generate
    bias. With support from Zillow, NFHA hosted a hackathon just
    last year designed to explore these challenges. The teams
    participating in our hackathon identified several structural
    barriers. In fact, because of these systemic flaws, in some
    cases, AVMs had only a 15 percent rate of accuracy in
    predominantly Black neighborhoods.
        While AI can pose perils that we must not ignore, it can
    also hold great benefits. We can use AI to increase data
    accuracy, provide guidance to consumers on how to appropriately
    access risk and credit, or enable lenders to use rental housing
    and cash-flow data to underwrite consumers. We can also use AI
    to increase fairness. Just a few months ago, NFHA and FairPlay
    AI released important research explaining how distribution
    matching, a technique that allows multifaceted optimization,
    can increase fairness in mortgage loan originations for Black
    and Latino applicants by up to 15 percent and reduce pricing
    disparities for the same groups by up to 20 percent with no
    tradeoff in model accuracy.
        To ensure AI will be used to benefit society, NFHA urges
    Congress to pass comprehensive legislation that includes the
    following: shoring up civil and human rights protections;
    providing full internet access for all communities; providing
    the public access to data, particularly for important research;
    ensuring consumers have agency over their own data; supporting
    the use of privacy-enhancing technologies; assuring systems are
    safe, accurate, and effective; requiring entities to provide
    accurate notices to consumers; providing funding to promote
    important research; ensuring Federal employees are well trained
    and agencies have sufficient resources to provide oversight and
    enforce the Nation's laws; and providing for human
    alternatives. The recent global information technology (IT)
    outage that halted airline services and crippled banking
    institutions provides a clear example why there must be a human
    or manual alternative.
        Thank you so much for the opportunity to testify, and I
    look forward to answering your questions.

        [Prepared statement of Ms. Rice follows:]

        [GRAPHIC(S) NOT AVAILABLE IN TIFF FORMAT]

        Chairman McHenry. Thank you, Ms. Rice. Mr. Zecca, 5
    minutes.

    STATEMENT OF JOHN ZECCA, EXECUTIVE VICE PRESIDENT, GLOBAL CHIEF
               LEGAL, RISK AND REGULATORY OFFICER, NASDAQ

        Mr. Zecca. Thank you, Chairman McHenry and Ranking Member
    Waters, and all the members of the committee. On behalf of
    Nasdaq, I appreciate the opportunity to testify on Nasdaq's use
    and governance of artificial intelligence technology. From our
    founding 50 years ago, when we became the world's first
    electronic exchange, Nasdaq has continued our role as a
    technology leader in the capital markets. Our technology powers
    more than 130 markets around the world. We help the banking
    community fight financial crime. At Nasdaq, our vision is to be
    the trusted fabric of the world's financial system, and our
    purpose is to advance economic progress for all. Our solutions
    enhance liquidity by powering robust markets, ensure
    transparency by leveraging data and analytics to fuel
    decisionmaking by corporates and investors, and protect
    integrity by managing risks, surveilling markets, enhancing
    compliance, and fighting crime. Right now, AI applications are
    enabling a fairer and more efficient and resilient financial
    system.
        Today, I wanted to share a few examples of how we are
    harnessing AI's potential at NASDAQ. Our anti-financial crime
    business is leveraging AI to more effectively than ever before
    fight criminal organizations engaged in smuggling, drugs,
    terrorism, and money laundering. An estimated $3.1 trillion in
    illicit funds flowed through the world's financial system in
    2023, and last year alone, losses from fraud totaled $485
    billion globally. To address this massive problem, our cloud-
    based platform is used by thousands of financial institutions
    of all asset sizes to dramatically improve the effectiveness of
    their financial crime risk management programs. After all,
    there is a money trail behind every act of terrorism or human
    trafficking. Financial crimes are not victimless crimes. We
    must be more innovative than the criminals who are also using
    AI.
        To further protect the integrity of our markets and our
    market surveillance business, we use machine learning to detect
    insider trading, market manipulation, and fraud. AI will also
    enhance effectiveness as we analyze billions of transactions
    and events every day. Meanwhile, innovation continues as we
    recently introduced Dynamic Midpoint Extended Life Order (M-
    ELO), the first exchange AI-powered order type, and we are also
    deploying AI in our market data, corporate governance, and
    index businesses. These products are a reflection of a robust
    governance framework centered around our responsible AI
    principles. Governance is centrally administered, eliminating
    the AI sprawl risk of more decentralized approaches. Leading
    this effort is an executive steering committee that includes
    our CEO and other senior members of our leadership team. The
    program is aligned with a U.S. National Institute of Standards
    and Technology AI risk management framework.
        As Congress and agencies look to develop AI-related laws
    and regulations, we ask them to consider the following:
    Existing regulations and regulatory structures should be
    leveraged where possible. Like prior technological innovations,
    the adoption of AI technology does not necessarily demand
    sweeping regulatory changes. New regulations should avoid
    focusing on specific technology itself and, instead, be
    proportionate to the benefits, risks, and harms of the AI
    application. For example, the regulation of an AI tool to
    detect drug trafficking should not be the same as the
    regulation of the identical AI tool if it is used to approve
    apartment rental applications.
        The regulatory environment should endeavor to be flexible
    and allow for innovation. We support sandboxes to promote
    innovation and believe the regulatory environment should be
    transparent with a feedback loop between government, academia,
    industry, and the public, and, most importantly, the
    development of industry standards is critical. AI-specific
    regulation should be consistent and harmonized. Regulators
    should coordinate across borders. While we oppose the creation
    of a central regulator, we support leveraging National
    Institute of Standards & Technology (NIST) to coordinate across
    the government. It is also important to avoid a patchwork of
    differing State AI laws that could stifle innovation and harm
    the competitiveness of the United States globally. We want to
    enable the use of AI, not hinder it.
        Nasdaq is committed to using AI in a responsible, ethical,
    and transparent manner. We look forward to working with the
    committee and regulators to foster a supportive and balanced
    regulatory environment for AI. We believe that AI is a
    promising technology that can bring significant benefit to our
    industry and the wider economy. I would be happy to answer any
    questions you may have.

        [Prepared statement of Mr. Zecca follows:]

        [GRAPHIC(S) NOT AVAILABLE IN TIFF FORMAT]

        Chairman McHenry. Well, thank you for your testimony. This
    has been a great panel, and I will now recognize myself for 5
    minutes for purposes of questions.
        As I said in my opening, this committee has been at the
    forefront of examining how AI is impacting the financial
    services and housing industries, both during my chairmanship
    and the chairmanship of Ms. Waters, and I want to use today's
    hearing to continue examining the range of benefits and
    potential risks of AI, specifically GenAI and its challenges
    through some use cases. Mr. Zecca, how do you envision AI
    evolving over the next decades to not only enhance market
    efficiency and risk management, but also to foster innovation
    and accessibility, accessibility in particular of our capital
    markets?
        Mr. Zecca. Well, I think that is an important question, how
    does it democratize the markets, and I do think there are a
    couple of opportunities. For one, I think through the uses, as
    I mentioned, of analytics and data, there is the opportunity to
    develop products, whether it is exchange-traded fund (ETF)
    products, unique products that could be available at lower cost
    to Main Street investors, and I think that is a true
    opportunity for them to diversify. Similarly, I think you will
    see more of the analytics being used for investment advice, and
    again, targeting communities that have not been able to pay for
    individual tailored financial advice. So I think there are
    great opportunities there.
        In the market, I think you will see unique order types
    coming, but the part that I am always interested in, you know,
    being the head of regulation is the surveillance end, and I do
    think AI can be a force multiplier for us in monitoring
    markets, looking for aberrant activity. Just think about the
    billions of trades that are going through the market. If you
    can have AI sort of do the triage, then the analysts can focus
    on a much more developed case.
        Chairman McHenry. Excellent. Excellent. Well, Mr.
    Karunamurthy, I do not think we can overstate your testimony,
    and particularly, you talk about the need for high-quality
    data, and I do not think we can overstate how vital that is. So
    how does Scale AI ensure the quality and accuracy of data,
    particularly as it relates to input data, as well as model
    structures?
        Mr. Karunamurthy. Thank you, Chairman McHenry. This is an
    important part of our work, working with industry partners, and
    so we are happy to spend some time talking about it. The work
    that goes into making data AI ready is really fundamentally
    looking at the quality of the data that is going into these
    models. Do we have diverse geographic data, do we have data
    from multiple different perspectives on a topic, and also
    helping the models to understand what are authoritative sources
    of data. So where these models have been trained on a range of
    different perspectives about current events or financial
    markets, helping these models understand what are
    authoritative, sometimes proprietary internal sources of data
    that can better address customer questions. By paying attention
    to the data and how that data is shifting over time, you get a
    better sense of how these AI systems will perform. It is a
    constant process of observation about where new data is coming
    in from, how the models can use more authoritative sources of
    data and then how that data can be improved over time.
        Chairman McHenry. Now, Mr. Reynolds, I know that FIS is
    focused on the impact technologies like GenAI, blockchain, the
    deployment of blockchain technology, on fraud prevention,
    identity verification, existing customer due diligence
    procedures. Would you highlight the opportunities that GenAI
    and blockchain technology, the challenges that it poses, and
    the opportunities it has also presented?
        Mr. Reynolds. Yes, thank you, Chairman. When you look at
    GenAI, obviously one of the foundational points about GenAI is
    it is incredibly creative, and it really levels the playing
    field, both in terms of opportunities to enhance the system,
    but then attacks on the system. If you look at attacks on the
    system, one of the risks of GenAI is it can very creatively
    come up with alternative identities. It can fake data, and it
    can fake data elements. So one of the ways that I think we have
    to look at GenAI and the potential blockchain is to understand
    is there an opportunity to create, for instance, a digital
    identity that a consumer can have, if it is on the blockchain,
    it becomes immutable. So it becomes a digital identity, which,
    over time, as it exists within the system, it can gain
    permissions based on what it does in the system where it is.
    That can sit on a blockchain, and it could be something that
    can allow consumers much greater access to financial services
    because that sort of passport, so to speak, allows them into
    the financial system.
        I think, though, again, we have to be very careful with
    generative AI because in our current system that is really
    reliant on basic identity elements like date of birth, address,
    social security number, it is very easy to fake now with GenAI,
    and I do think it puts our current CIP system somewhat at risk.
        Chairman McHenry. Okay. Mr. Zecca and Mr. Reynolds, you are
    talking about reducing the cost structure, which gains a huge
    advantage for market participants. I think that is an
    interesting outcome, and thank you for mentioning it. We will
    now recognize the ranking member, Ms. Waters, for 5 minutes.
        Ms. Waters. Well, thank you very much, Mr. Chairman. I
    would like to address this question to Ms. Rice. We know that
    third-party tenant screenings and rent-setting companies and
    the data they use are often the primary basis for landlord
    decisions about who has access to housing and how much renters
    will pay. Numerous lawsuits have highlighted how technologies
    have used inaccurate or discriminatory data that may not even
    be connected to the applicant or use data that artificially
    inflates rents. For example, the company, RealPage, which
    offers rent-setting algorithms to landlords, is currently being
    investigated by the Justice Department for potentially
    anticompetitive practices and sued by several States' attorneys
    general for allegedly using private data to artificially
    inflate rents and grow profits. What kind of rules or
    restrictions on data and transparency should be established for
    these and other types of third-party companies that affect
    housing access and affordability?
        Ms. Rice. Ranking Member Waters, thank you for the
    question. As it relates to tenant screening selection systems,
    which can often use data that can result in biased outcomes,
    data like criminal records history, credit scores, data like
    whether or not an eviction has ever been filed against a
    person, that kind of data that is highly correlated to race can
    often result in discriminatory outcomes and can generate
    decisions that are, well, that data may not necessarily be
    predictive as to whether or not a person is going to be a good
    tenant or pay their rent on time.
        So systems that utilize data that is highly correlated to
    race, like criminal records, that type of data should really be
    constrained and limited in models. We think that when a model
    is using data that serves as a proxy for race or gender, that
    there should be higher protocols and standards placed around
    those models, and they should be regularly monitored and tested
    for discriminatory outcomes. They also need to be subject to
    our antidiscrimination laws.
        When it comes to dynamic pricing models, those also can
    present prohibitive barriers for consumers. For example,
    consumers who are using housing choice vouchers can often be
    denied housing opportunities because the price-setting
    algorithms are not predictable. You can go to an apartment
    complex in the morning and get one rental rate for a unit and
    go to the apartment complex in the afternoon and get a
    completely different rental rate that can exceed HUD's fair
    market values. So we have to have tight controls on these types
    of technologies that can, as I said, disproportionately
    discriminate against survivors of domestic violence, who are
    disproportionately women, people of color, people who have
    disabilities, and other protected classes.
        Ms. Waters. So when you say, ``tight control,'' you mean
    legislation?
        Ms. Rice. Yes, I do.
        Ms. Waters. That would absolutely have the guardrails that
    are needed in order to protect the consumers?
        Ms. Rice. Yes.
        Ms. Waters. All right. Thank you very much. I have another
    question that I would like to ask Mr. Karunamurthy. The
    complexity within AI models is not easily explainable or
    observable, can result in unfair bias, discriminatory outcomes
    that impact customers' lives. I think transparency of the
    underlying data of an AI system can help address challenges in
    explainability, bias, discrimination, and even compliance with
    regulatory standards.
        In fact, I led a delegation to Silicon Valley and met with
    several University of California (UC), Berkeley scientists who
    warned that not being able to understand the outcomes of
    particular AI models is one of the most alarming outcomes and
    something that must be addressed. With increased availability
    of generative AI more financial services companies rely on
    third-party vendors for high-quality training, data for AI
    models. Your company, Scale AI, which provides data labeling
    services to companies has a unique and important role in this
    ecosystem.
        This is a long question, and I have overrun my time. So I
    think I am going to have to bring it to your attention to get
    an answer following this committee.
        Ms. Waters. Thank you, and I yield back.
        Chairman McHenry. Thank you, Ranking Member. We will now go
    to the gentleman from Arkansas, co-lead of this taskforce, Mr.
    Hill, for 5 minutes.
        Mr. Hill. Thank you, Chairman. Thank you, panel. Thanks for
    your indulgence this morning as we went and voted.
        Over the course of our many early morning roundtables, the
    committee heard from companies about how they are using
    artificial intelligence to lower compliance costs, better
    detect fraud, and enhance customer service, just to name a few.
    We also heard from the supervisors from HUD to the Public
    Company Accounting Oversight Board (PCAOB) to Securities and
    Exchange Commission (SEC) and the bank supervisors that they
    were all being principles-based in their looking at how to
    supervise AI. Taking this sort of technology-first approach to
    understanding AI use cases in the financial sector, in my view,
    has been central to our Working Group's approach. The problem
    with policy-first approach previously taken by the committee is
    that technology changes so quickly that we are stuck talking
    about yesterday's policy issues while the actual technologies
    and the use cases move right ahead, and that is sort of the
    challenge I have seen in other jurisdictions like in Europe's
    approach to AI.
        So let me start out and turn to that European approach.
    Foreign jurisdictions have taken and seek remarkably different
    approaches to regulating AI. Mr. Reynolds, you and Mr. Zecca
    both work at companies with a global presence. Mr. Reynolds,
    how would you assess the different approaches being
    contemplated, such as the comprehensive good housekeeping seal
    of approval approach that Europe is taking? What do you think
    about that?
        Mr. Reynolds. Thank you, Congressman, for the question. It
    is very challenging as a global company to deal with such
    differing approaches. In the U.S., we have taken a very open
    approach, and I think that has allowed a significant amount of
    innovation. In my remarks, I noted that we strongly believe in
    good regulation and good regulatory behavior, and that is a
    strength of our company. So certainly I am a fan of following
    the rules as the chief compliance officer, and I think it is
    important.
        I do think with the European approach, it tends to be
    fairly restrictive, and so I do think from the European
    approach, it can stifle innovation and make it harder, I think,
    for companies to adopt this new technology and I think it
    creates an unlevel playing field across different geographies
    because the technology that will work well in the United States
    may not work very well in Europe----
        Mr. Hill. Thanks.
        Mr. Reynolds [continuing]. and it may be very different in
    Asia.
        Mr. Hill. Mr. Zecca, a comment along that line?
        Mr. Zecca. I agree with that. I think when you look at the
    AI regulation in Europe, which is just going into effect now,
    it has been a 2-year process, and if you look at where they
    started to where they are now, it has changed a lot, and it is
    not only because there were comments. It is because the
    underlying technology has changed in the last few years.
        Mr. Hill. Yes. Kind of hard----
        Mr. Zecca. So the question is----
        Mr. Hill [continuing]. It is kind of hard to decide what
    you are----
        Mr. Zecca. Right. So now they have frozen it in time----
        Mr. Hill. Yes.
        Mr. Zecca [continuing]. in a way, and that is----
        Mr. Hill. So that also speaks to another key point I think
    we heard in our roundtables, which is that there is this
    presumption I picked up in the roundtables of
    ``noncompliance.'' In other words, there is this assumption
    that if you are deploying AI internally for internal compliance
    purposes or marketing purposes, that somehow you are oblivious
    to your existing supervisory, regulatory, or legal obligations
    under Federal law, and, Lisa, we have talked about this. They
    suspect that you are not in compliance, which, generally, I am
    going to stipulate that is probably not true but how do we have
    people show their homework effectively?
        Mr. Reynolds, let me go back to you just because you work
    with so many community banks, and then maybe we will get a
    credit union comment there but people are trying to comply with
    fair credit reporting, fair lending, right, when they use AI.
    They do not miss that obligation. They still have the
    obligation.
        Mr. Reynolds. Yes, absolutely, Congressman. I think
    compliance is really at the forefront of certainly all of our
    customers' minds. It is in the forefront of our mind. The first
    thing we did in AI actually is to set up a governance council
    to ensure that we were following the laws, following the
    regulations, and really following ethical standards for AI, and
    making sure all of our use cases followed all the regulatory
    standards and ethical standards. So I think that is critical,
    and I do not think anybody loses sight of that. I think the key
    is really auditability.
        Mr. Hill. Yes.
        Mr. Reynolds. We make sure that we are very clear as to how
    we are using it, why we are using it, what controls we have in
    place, and we openly share that with all our members.
        Mr. Hill. I think that is good. I mean, that is what we
    heard from supervisors: show us your homework on how you got
    there. Let us hear it from a credit union perspective on that.
        Ms. Osborne. Yes. Thank you, Congressman Hill. I agree with
    Mr. Reynolds' testimony and what he stated here. As a financial
    institution, we are subject to a myriad of regulations, so when
    we look at technologies, AI or not, and providers, the same
    rules apply. So any expectations we have of staff to ensure
    that we are adhering to compliance standards, any rules and
    regulations, that would need to be built into AI solutions as
    well and then the expectation with third parties, which we
    leverage for services like this at an asset size like Great
    Lakes Credit Union, we then would develop with them reporting
    and monitoring.
        Mr. Hill. Thank you. I appreciate that response, and I
    yield back the balance of my time. Mr. Chairman, thank you.
        Chairman McHenry. We will recognize the gentleman from
    California, Mr. Sherman, for 5 minutes.
        Mr. Sherman. It is natural that we focus on the AI issues
    that confront us now and in the next few years, but I want to
    take a minute to say that we need to research and develop and
    deploy systems that will monitor for and prevent self-
    awareness, volition, survival instinct, and ambition in
    superhuman computers and artificial intelligence. The last time
    there was a higher level of intelligence introduced on this
    planet is when our ancestors said hello to Neanderthal. We then
    said goodbye to Neanderthal, and I do not look forward to being
    the Neanderthal of this century. We are not actually spending
    any money on a problem, and this problem will not affect us
    until the latter half of this century on preventing self-
    awareness and ambition in artificial intelligence.
        Now to the issues of today. When I first looked at AI, I
    said, great, decisions will be made without bias and without
    racism because I could not imagine a machine being racist and
    then I looked at a study that came out of an elite university,
    and they looked at all the factors that causes a student to be
    successful at the university, and they concluded that the
    number one factor was being named ``Jared,'' that being named
    ``Jared'' apparently correlates to success at that university.
    It does not correlate with, hopefully, reducing and eliminating
    bias in our society. This is not entirely new. We had
    statistical analysis. Then we had algorithms. We had computers.
    Now we had artificial intelligence. At each stage, we face new
    decisionmaking mechanisms that deserve our attention, but it is
    not new in-kind. We have made this jump before.
        I am very excited about our ability to monitor for money
    laundering and monitor transactions, and particularly in the
    capital market signs of insider trading, and I am particularly
    concerned that crypto will allow us to see everything and
    understand nothing. We will know all the transactions and we
    will not know who is making them.
        Let us see. The one other thing that we have is whether it
    is hard and fast rules imposed on an employee or whether it is
    a computer or AI. Clearly a prior criminal record is not a
    positive, a prior eviction is not a positive, but if you are
    dealing face-to-face the old way, you can overcome that
    perhaps, whereas, if you have AI and it gives so many bad
    points for a criminal record and so many bad points for an
    eviction, then the computer makes the decision without the
    intervening human appeal.
        Ms. Osborne, if AI will first be deployed by the giant
    financial institutions, how critical is it for a community
    financial institution or, like, a credit union to have access
    to affordable technology solutions and to have that in a way
    that is consistent with your compliance applications, all the
    while competing with much larger institutions?
        Ms. Osborne. Yes. So thank you for your question,
    Congressman. In terms of our usage of technologies and partners
    to aid us in providing loans and decisioning to members, we,
    through our vendor or third-party management requirements,
    these are regulatory requirements that are across the board,
    the same regardless of the size of the financial institution.
    Those standards are followed to ensure that we have a rigorous
    approach when looking at third parties. So when thinking about
    the different types of factors that would ensure there is not
    bias or decisioning for that, that would be unfair to one, we
    would then ensure that our technology is applying that same
    rules to that as well, and that we have the appropriate checks
    and balances just like we would for a human.
        Mr. Sherman. I yield back the remaining second of my time.
        Chairman McHenry. The gentleman from Oklahoma, Mr. Lucas,
    is recognized for 5 minutes.
        Mr. Lucas. Thank you, Mr. Chair.
        Chairman McHenry. Also the chair of the Science Committee.
        Mr. Lucas. Thank you, Mr. Chairman. Advances in AI systems
    have led to a wide range of use cases with the potential to
    benefit nearly all aspects of our society. With the rapid pace
    of technological progress in artificial intelligence, it is
    essential that we encourage this innovation here in the United
    States. The best way to reduce potential risks associated with
    AI is to ensure that advancements in this field are made with
    American values. We should not rush to regulate or put up
    roadblocks that will impede American leadership and have a
    detrimental impact on safety, security, and transparency. I
    appreciate Chairman McHenry for putting this hearing together
    so that we can hear from experts on the use cases of this
    technology.
        Dr. Linda, you discussed how Zillow is in the early stages
    of integrating AI tools into real estate transactions. How do
    you see this technology simplifying the house-buying process?
        Mr. Linda. Thank you for the question, Congressman. We do
    ask our real estate partner agents how do they spend their
    days, how effective they are at their work, and what they told
    us is that they spend most of their time doing mundane,
    repetitive tasks. That is where we see that AI has a very big
    role to play, functioning as a trusted co-pilot to really
    automating tasks. We recently experimented with AI co-
    summarization, which is a great way that we can help agents
    spend their time with the customer and really putting the
    humans at the center of the real estate transaction. The more
    we do that, the more we can enable customers, and understand
    the process and eventually buy a home.
        Mr. Lucas. Most agencies have existing authority to oversee
    the outcomes of AI systems within their perspective
    jurisdictions. Dr. Linda, could you discuss how AI tools are
    deployed within current regulatory structures and how the
    policymakers can avoid impeding novel use cases for these
    systems?
        Mr. Linda. Thank you for the question, Congressman. I
    think, you know, Zillow has been at AI development on behalf of
    our customers since 2006, and the whole time, Fair Housing and
    Equal Credit Opportunity Act has been our guiding principles.
    We constantly look at how this regulation can be applied the
    same way we have been applying to our business and our human
    operators as well to AI. We do not see any difference in there.
    As a specific example, we recently open sourced some of our
    work that enables you to use recent large language models and
    make them fair housing compliant and aware of the regulation.
    So from that standpoint, we feel that exploring how existing
    regulation can be applied to AI is the way that we can make
    progress.
        Mr. Lucas. Mr. Zecca, you discussed how AI allows for an
    extraordinary amount of data to be analyzed. Could you
    elaborate on how Nasdaq leverages this technology to track
    fraud and manipulation and how this information is communicated
    to market regulators?
        Mr. Zecca. Absolutely. So if you think about in the
    financial crime area, you know, the massive data, you are
    looking at activity that can go across accounts, people who
    have common names, a trail that may end up in a manual Google
    search. So it is really about tying the threads together to
    build a case, and AI can do a lot of that work for the
    investigator. My experience with bank compliance groups is they
    want to comply, but they are strapped and there is a lot of
    information being thrown at them, so we have seen 30, 35
    percent increases in productivity because of the use of AI. So
    then those cases, when they are built, could be a report to the
    government. Alternatively, in the market surveillance, where we
    do that, we would either prosecute the case ourselves or refer
    it to the SEC.
        Mr. Lucas. Continuing with you, Mr. Zecca, how
    transformative do you view AI to be in our capital markets, and
    how can our handling of this technology influence our status of
    having the most robust and liquid capital markets in the world?
        Mr. Zecca. Well, I think that is the core question,
    Congressman, how do we maintain our lead in the capital market
    is one of the great American advantages, and we certainly would
    not want to do anything that hinders that. I think it is
    important to remember that the financial sector is heavily
    regulated and the regulators who oversee them are very
    knowledgeable about the market, and I think that piece is
    important when you marry that with AI knowledge for the
    oversight.
        I think where it is going to make the most difference, I do
    think it is going to democratize products because you are going
    to be able to offer things at a cheaper price. I think the
    information over the long run is going to be more accurate and
    more digestible, and we will see unique trading opportunities
    that do not exist now that we cannot even imagine. As I
    mentioned, we introduced the first AI order type, which had a
    fill rate improvement. We went through the SEC process, but I
    think there will be many more.
        Mr. Lucas. With that, Mr. Chairman, I yield back the
    balance of my time.
        Chairman McHenry. We will now recognize the gentleman from
    Massachusetts, Mr. Lynch, co-chair of the Working Group on AI.
        Mr. Lynch. Thank you, Mr. Chairman. Mr. Zecca, I know you
    are suggesting that democratization is going to develop. So Sam
    Altman, I think he reported that GPT-3 cost $100 million, then
    GPT-4 was $400 million. Now they are saying GPT-5 is going to
    be somewhere between $2 and $2-and-a-half billion. For these
    large language models, it does look like because of the costs
    and the energy required, that there will just be a few big
    firms that will control that market. You are talking about
    democratization, but it does not look that way. It looks like a
    few big players that have sort of organic access to massive
    data sets, that those might dominate and that would not be
    truly democratic, you know. Push back on that, if you will.
        Mr. Zecca. Sure. Well, I think it is always something we
    have to consider, how the concentration is happening. When you
    break down the large language models, there are open-source
    ones, there are non-open source, so there are quite a few
    providers.
        Mr. Lynch. Yes.
        Mr. Zecca. Then in many cases, larger companies, in
    particular, are using their own data sources. Of course,
    remember we are talking about the large language model piece,
    but there is also the code writing side of it, which is
    different, which is----
        Mr. Lynch. Sure. That is right.
        Mr. Zecca. Yes.
        Mr. Lynch. And training.
        Mr. Zecca. So I think there are a lot of use cases where it
    is not going to be quite so concentrated. Even where it is
    concentrated--remember, the data lake, the model you are
    training it on--but then there is a lot of customization in
    what you are looking for. Let us say in the trading side, if
    you are a trading firm, you are looking for some advantage that
    makes your algorithm unique.
        Mr. Lynch. I guess what I was getting at is just the
    exponential increase in costs, and there will be fewer and
    fewer people that can do that if the costs continue to escalate
    at the current rate. It is just exponential, but I appreciate
    your opinion on that. By the way, Mr. Chairman and Ranking
    Member, I am very grateful for all the experts here. I know a
    bunch of you are frequent flyers to our roundtables, and you
    have appeared at these hearings. So I am very, very grateful at
    the diversity of perspective here and the value that you bring
    to these hearings. I am grateful because we got a lot to learn.
        Mr. Reynolds, I know you were at FinCEN for a while. These
    smaller firms they are acquiring, they are not building their
    own data sets or models. They are acquiring them AI-for-
    service. On the one hand, it is great because with the help of
    AI, they might be able to compete with some of these big firms.
    On the other hand, they are going to be relying on a third
    party for monitoring, for compliance, and for monitoring
    possible bias in their AI systems. How do we reconcile that, or
    do you think most of the firms now that provide AI as a service
    are able to properly monitor what is going on in these small
    banks or smaller financial firms?
        Mr. Reynolds. Yes. Thank you, Congressman. It is a great
    question and one that we think about quite a bit. As I
    mentioned, we have a very strong governance process around our
    models and around our AI. So when we deploy that technology to
    customers of ours, which are predominantly banks or brokerages,
    capital markets players, we obviously are very conscious that
    we are sort of the first level of defense. So we think of
    ourselves, I guess by analogy, as the belt and the bank is sort
    of the suspenders in that you have both the bank itself having
    an obligation to look at those tools and we provide them
    information so they can make their assessment to ensure that it
    is operating as designed without bias. We also are very focused
    on that ourselves to ensure that it is. One of the ways that we
    really do that is to make sure that a human is either in the
    process or on the process, and I think that is critical for the
    technology at this stage.
        One of the great things is AI can learn, GenAI can learn,
    so hopefully, as it learns about mistakes it makes, you can
    correct those and it will not do that in the future, but to
    really do that, you have to make sure that you have human
    beings in the process to ensure those basic concepts of human
    fairness that go into these decisions.
        Mr. Lynch. Thank you very much, Mr. Chairman. I yield back.
        Chairman McHenry. Thank you. Thank you for your leadership
    as well. The gentleman from Missouri, Mr. Luetkemeyer, is
    recognized for 5 minutes.
        Mr. Luetkemeyer. Thank you, Mr. Chairman. Ms. Osborne, at
    our National Security and Illicit Finance roundtable, a
    panelist shared that his firm has seen a 450-percent increase
    year-over-year in AI-powered deepfake attacks against financial
    institutions. As generative AI compounds in its ability to
    manipulate video and voice are better trained, this problem
    will only get worse. I am concerned that the small community
    institutions, such as Mr. Lynch referenced here a minute ago,
    like yours are going to bear the brunt of these attacks. Do you
    share these concerns?
        Ms. Osborne. Thank you for your question, Congressman. At
    Great Lakes Credit Union and across the credit union industry,
    we prioritize safety and security for our members. Deepfakes
    are absolutely a concern. It is something that is top of mind.
    What we have done at Great Lakes Credit Union with our virtual
    assistants, because that is one risk that we have to monitor
    and manage, too, is we recently introduced voice
    authentication. Voice authentication is one additional method
    of authentication for our members to use. We were very careful
    and considerate in the rollout of that method of authentication
    because we needed to ensure that it provides additional layers
    of controls for our members.
        The way voice authentication works with Olive, our virtual
    agent, is it is two-factor, right? So it is multi-factor
    authentication (MFA) in partnership with voice recognition. The
    member has to enroll in the program. That is the first piece.
    Then that technology, once they enroll and they use the
    platform, it combines anti-spoofing technology and compares it
    against 25 to 30 different factors, such as the phone number
    the member is calling from. In addition to that, the type of
    transaction that is being requested is risk rated, and varying
    levels of additional authentication are offered depending on
    the transaction risk.
        Mr. Luetkemeyer. What are the risks that you face with
    generative AI that we are not thinking about?
        Ms. Osborne. That is a very good question. I would say
    generative AI is constantly changing. So for our institution,
    for Great Lakes Credit Union, the challenge we have and the
    risks we have is, how do we continue to ensure that we are
    meeting the best of both worlds, providing the service to our
    members, while also ensuring the appropriate controls and
    guardrails are in place. So it is truly in partnership with our
    third parties that we work through that, constant discussions
    with those third parties and check-ins help to ensure and
    mitigate that risk as much as possible.
        Mr. Luetkemeyer. You know, I sit on the China Select
    Committee as well. I had a briefing this morning, and AI is
    used to surveil the Chinese people. They spend $300 billion a
    year to surveil, detain, and build detention camps on their
    people, and AI is a big part of that. So now in my seat here in
    the Financial Services Committee, we look at this from the lens
    of, on my gosh, what can the Chinese do to us here using AI,
    which you guys have all talked about this morning all the
    benefits of it. There are some risks to it as well and some
    real concerns, and those are the ones that I think that we as a
    committee have to think in terms of and figure out how we can
    protect the industries that we oversee, the individuals, the
    people of this country. So I am very concerned.
        I think we had a situation last spring with the run on
    Silicon Valley and those other banks where within 10 hours, $42
    billion went out the door. I can see a scenario where the
    Chinese are looking at artificial intelligence in many
    different ways. You figure out which one you want to take. They
    could have an artificial intelligence-generated commercial with
    Chairman Powell up there or with Bloomberg or with Dave Ramsey
    or somebody who is a recognized financial expert saying, uh-oh,
    we have 100 banks today that are in trouble, and all of a
    sudden everybody runs out, and we have FedNow, which is, as you
    know, almost instantaneous. Instead of taking 10 hours to do
    $42 billion, they are doing in 10 minutes with FedNow, a year
    or so from now, perhaps.
        So it really strikes me as being a wonderful tool. It also
    has some really big challenges, and I am very concerned about
    it. Do you think I have a right to be concerned? Mr. Zecca, you
    deal with the Nasdaq folks. I mean, this has to be concerning
    to them to have short selling going on with this sort of
    situation.
        Mr. Zecca. Well, thank you for the question. I think it is
    very important. We obviously had situations before where people
    have tried to put out fake information, but there is the risk
    that deepfakes have a level of legitimacy or apparent
    legitimacy that others do not. So we monitor social media
    trending to try to see situations that we may have missed where
    something came up.
        Mr. Luetkemeyer. So it begs the question then, what do we
    as legislators need to do to come up with regulations or
    provide oversight in a way that monitors this or controls this
    yet does not stifle the markets? We have to be thinking about
    this, and I am sure you guys have already thought about it as
    well. Mr. Zecca? I will have to ask the question later. I am
    out of time, but thank you, Mr. Chairman.
        Chairman McHenry. The gentleman from Illinois, Mr. Foster,
    is recognized for 5 minutes.
        Mr. Foster. Thank you, Mr. Chairman and to our witnesses.
        Mr. Reynolds, on page 5 of your testimony, you identified
    one area where you believe that congressional action is
    critical, which is updating customer identification programs
    and development of a digital identity system. Can you say a
    little bit more about that?
        Mr. Reynolds. Yes, thank you, Congressman. This is an area
    that I think for the last couple of years we have noticed
    growing weakness, I think, in the CIP and the CDD system. I
    think it started with Web3, where you have users who are able
    to exist on the blockchain have anonymous or pseudo-anonymous
    identities and are able to divert away from the legitimate
    financial system and really avoid some of the controls that we
    have. One of the things that I think is critical for us is, as
    these controls start to weaken as deepfakes become more
    prevalent as the ability to fake identities become more
    prevalent, we need to think is there a different way to think
    about identity.
        Identity, essentially, is in our system a proxy for trust,
    how much do I trust you, and I think based on how much I trust
    you is, do I know who you are. So I think if we start instead
    from the basis of trust and think how can I instead understand
    how much do I trust you without necessarily regard to identity
    purely, that will be the basis for a new digital identity
    system that will help solidify our existing CIP and CDD----
        Mr. Foster. Now, in other countries, including, I guess,
    the entirety of the EU, they are going to an approach where you
    basically have the national government present something that
    is very similar to the digital driver's licenses, a Real-ID-
    compliant digital driver's license, where if you want to open a
    bank account, you say, okay, get out your phone, do your
    biometric login, show your face shape, and take advantage of
    the trusted electronics in modern cellphones to present a
    federally issued certificate that pretty much says here is my
    proof that I am a single legally traceable person. This will
    allow a single step know your customer (KYC) in 30 seconds,
    which will really remove one of the big barriers to entry for
    like fintechs setting up their KYC operations or small banks.
    It is a real burden on them that could be avoided if you do
    what actually a lot of the States in the United States are
    doing, is just provide digital driver's licenses as a means of
    proving you are who you say you are in an online environment.
    Is that part of the solution we should be looking at for
    dealing with deepfakes and other----
        Mr. Reynolds. Absolutely. I think that concept of the
    digital ID will make sort of banking portability a reality, and
    I think it will allow consumers to move very freely throughout
    the financial system. As you note, it will significantly reduce
    costs, but significantly increase trust.
        Mr. Foster. Yes. Ms. Osborne, did you have any comments on
    it?
        Ms. Osborne. Yes. So at GLCU, we do not currently use or
    accept digital ID. However, it is an area that we are actively
    researching and hope to in the future pursue. Our biggest
    challenge at this point is really consumer adoption. You do not
    see the use of it and nor are there many providers that we
    could leverage to integrate into our platforms for this. They
    exist, but they are still new and so we are doing our due
    diligence and thoroughly vetting partners and solutions out
    there to ensure we make the right decision for our members when
    that time comes.
        Mr. Foster. Yes. I think many people are also worried about
    sort of splintering a set of commercial identity ecosystems so
    that, you know, maybe log in with Google or log in with
    Facebook becomes a de facto standard and that makes a lot of
    Americans uncomfortable, that if there has to be a standard way
    to authenticate you are who you say you are, then that maybe is
    an essential government role to provide a way to assert your
    Real ID, which is something you are going to need to get on an
    airplane soon enough. If you can present that in a digital
    online format, you know, essentially, the heart of the mobile
    ID that was developed by NIST.
        Let us see. This also relates, I think, to privacy in ways
    that I think people underappreciate. Mr. Karunamurthy, you have
    to deal with the various privacy bills that are moving through,
    and it is a real burden if all of a sudden a customer has the
    right to retract the data that your model has been trained on
    and if, you know, spent millions of dollars. How do you
    anticipate having to deal with that?
        Mr. Karunamurthy. Thank you, Congressman. We absolutely at
    Scale believe privacy is one of the important considerations to
    make when considering the data used to train AI models and the
    data that is used to observe and improve these models in the
    future. While we do not train our own models, and so that
    specific case is not something that we have to particularly
    consider, we do take into account privacy when we are
    considering different perspectives we can gain on AI models.
    There are certain situations where you can use proxies for
    actual customer data without using customer data itself and
    still gain a lot of insight into how an AI system will perform.
    Increasingly, we are using those methods to try to get good
    observability into how these systems operate without needing
    oversight of individual customer data.
        Mr. Foster. Thank you, and I yield back.
        Chairman McHenry. We will now recognize the gentleman from
    Michigan, Mr. Huizenga, for 5 minutes.
        Mr. Huizenga. Thank you, Mr. Chairman. Given the rapid
    advancements in artificial intelligence and potential on
    various sectors, what specific measures should Congress
    consider to ensure that AI technologies are developed and
    deployed ethically with adequate protections for privacy,
    security, and fairness? This was what ChatGTP came up with as a
    question when it was loaded in write a question for a
    congressional hearing on AI. So that was my team's idea to
    check in with the experts apparently, but just to kind of make
    the point that this, well, I will let others decide whether
    that is a more intelligent question than has been asked in this
    room previously by actual humans, but it is certainly the
    reality. This goes back to the chairman's point right at his
    opening, which is, this is the reality of our lives today.
        So I am going to see if we can get back to that, but I have
    a couple of other quick things I need to do, and, Ms. Osborne,
    I am going to start with you very quickly. Credit unions are
    seeking to adopt more effective fraud management and the use of
    AI through that. The impracticality of manually monitoring
    transaction patterns is certainly something that we have heard
    about for a number of years but what are the barriers to
    incorporating AI-powered fraud analytics that you are
    experiencing?
        Ms. Osborne. Thank you for your question, Congressman
    Huizenga. So fraud is one of the three areas that we are
    currently looking to expand AI use in at GLCU, as I included in
    my written statement. We do not currently use AI for fraud.
    However, there are great benefits to incorporating that
    technology to improve accuracy. Really, the goal here is to not
    just detect, but to prevent those type of fraudulent
    occurrences from happening.
        Mr. Huizenga. Yes. Are there some barriers, though, that
    are keeping you from implementing that?
        Ms. Osborne. Yes. It is really about finding the right
    partner. At our asset size, a 1.6-billion-dollar institution,
    we are very dependent on third-parties. We go through a
    rigorous process to ensure we find the right solution,
    integration with that is also key. There are quite a few
    players out there and it is constantly emerging and changing.
        Mr. Huizenga. Okay. All right. Thank you. Mr. Zecca, in
    your testimony, you noted an estimated $3.1 trillion in illicit
    funds flowing through global financial system in 2023. Last
    year alone, losses from fraud totaled $484-plus billion dollars
    globally. How has Nasdaq used machine learning to address
    illicit activity and then specifically criminals are also using
    AI to develop new and more sophisticated financial crime
    strategies, what are you doing to out-innovate those folks?
        Mr. Zecca. Well, we have a whole team that tries to
    deconstruct and analyze the information we get from our bank.
    Under the Patriot Act, there is an opportunity for U.S. banks
    to share information. So through a trusted third-party like us.
    We are able to see the connections across all the
    organizations. It helps us build more valuable patterns that we
    can then implement.
        Mr. Huizenga. That has been somewhat controversial, that
    third-party, in allowing insight. I know that has been an
    ongoing conversation. Sticking with you, sir, I want to discuss
    the AI Act. According to a recent article in Financial Times,
    European regulators admit the costs and compliance of the EU's
    new AI regulation could hit six figures for a company as small
    as 50 employees. Essentially, in my mind, being a small
    business owner myself, this amounts to a tax on small
    businesses, startups, entrepreneurs. It is another barrier that
    is being put in the way, and we are not talking about massive
    companies with massive compliance offices. In your view, what
    lessons can the U.S. learn from the EU's approach to date? Now
    I will let you answer.
        Mr. Zecca. Well, yes, we are still digesting it in fairness
    because it just came out, but I think that the two areas that I
    would focus on, one is one act that tries to describe
    everything that is going on is a very complicated thing to do,
    and it does stick things entirely.
        Mr. Huizenga. Because, in fact, I think you had written it
    down. I think you wanted to avoid specific regulation, that was
    just going to be a follow up on that, but you also said you
    wanted consistent and harmonized regulation. I am curious how
    that fits into flexibility because I agree. I mean, I think
    Europe sort of locked itself in a box here in a way that does
    not seem manageable.
        Mr. Zecca. Right. It is true, and so I think I am talking
    about the world we would like to see, not perhaps the world we
    live in. I think organizationally, having a general system of
    principles-based regulation, we are concerned about a central
    regulator, in part, because they are not going to have the
    expertise about the underlying subject matter even if they have
    the AI expertise and see application that matters. So that is
    not the direction that Europe is going. They do have a central
    AI office. So I think that is another lesson we can learn here
    and then there is a concern about State regulation just going
    in different directions and so it will be very hard for
    companies to comply with different AI regulation.
        Mr. Huizenga. I appreciate that. I yield back.
        Chairman McHenry. I will now recognize the gentlewoman from
    Ohio, Mrs. Beatty, for 5 minutes.
        Mrs. Beatty. Thank you, Mr. Chairman and Ranking Member,
    and thank you to all the witnesses being here today, and let me
    give a special, Mr. Chairman, if I may, shout out to someone I
    call a friend, Lisa Rice. More importantly, a few years ago,
    she received one of the highest awards that we gave for fair
    housing, so it is good to see you again today.
        I will start with you, Mr. Linda and Ms. Rice. I would like
    to kind of follow up on some of the questions we have heard,
    and especially from Ranking Member Waters. What legislative
    solution should we be pursuing to ensure responsible use of AI?
    Mr. Linda?
        Mr. Linda. Thank you for the question, Congresswoman. We
    are really spending a lot of time thinking about how our
    existing regulation, such as Fair Housing Act, apply to
    everything we do at Zillow, including the use of AI. We see AI
    as the same or a solution that is similar to how we would
    instruct and train great real estate agents. We believe in
    proportional risk-based and flexible regulation and to the
    extent that there are new gaps, we welcome the chance to be
    part of dialog like this, one where private and public sectors
    comes together, and really putting the customer at the center
    of it because, ultimately, we want to put the customer first,
    find ways to help them get home and find a home in this really
    challenging market. That is the approach that we would like to
    put forward.
        Mrs. Beatty. Ms. Rice, would you like to add anything?
        Ms. Rice. Congresswoman Beatty, thank you for the question
    and for your support for fair housing over the years. So I do
    agree with some of the other speakers on today's panel that we
    should take a principles-based approach to legislation, but we
    also need to incorporate an outcomes-based approach. One of my
    members in Los Angeles just lost a case, in fact. They
    conducted systemic testing of an apartment complex that was
    using dynamic pricing systems. Every time they send a tester of
    color to the apartment complex, the apartment rental rate was
    higher for the same unit, every time they sent a white tester,
    the apartment rental rate, again, for the same unit was lower,
    and this was over and over and over again. When they brought
    the lawsuit, the landlord said, look, it is not our fault. We
    were using a dynamic pricing model. That model set the rental
    rate, and we are not liable because it is a third party that
    did it.
        If we make sure that we have comprehensive legislation that
    is both principles based but also outcomes based, we can close
    the loophole on those kinds of discriminatory practices we are
    seeing in the marketplace.
        Mrs. Beatty. Let me go to another part since we are talking
    about housing. Let me stick with you. What is the role of AI in
    the underwriting process for mortgages?
        Ms. Rice. We are seeing, particularly amongst our larger
    financial institution partners as well as fintech partners,
    that AI is playing a critically important role. So,
    Congresswoman Beatty, I will lift up a positive. One of the
    positive uses that we are seeing with AI is the ability to use
    rental housing payment data and cashflow data in the
    underwriting process and the utilization of those two
    nontraditional characteristics is actually expanding access to
    credit in a more responsible way. We know that credit scores,
    loan-to-values (LTVs), debt-to-incomes (DTIs), highly
    correlated to race, and they also result in lesser or lower
    access to responsible credit.
        Mrs. Beatty. You mentioned race, so let me, while the clock
    the ticking, in the latest funding bill draft, House
    Republicans have eliminated funding for fair housing
    enforcement and blocked HUD's ability to enforce affirmatively,
    furthering fair housing provision of the Fair Housing Act. Can
    you discuss the importance of Federal fair housing enforcement
    to combat automated discrimination in housing lending through
    online housing platforms with AI technology? I have about 30
    seconds.
        Ms. Rice. It is extremely important, and the thing that I
    will say is that many of our business partners have recognized
    that if we eliminate discrimination, we will actually increase
    profits, will increase productivity. Citigroup released a
    report that showed if we eliminate racial inequality, we would
    increase the U.S. Gross Domestic Product (GDP) by $5 trillion
    over a 5-year period.
        Mrs. Beatty. You are getting some nods, so.
        Ms. Rice. Right. That is one of the reasons why the
    Business Roundtable in its platform supported affirmatively
    furthering fair housing and disparate impact so that those
    businesses can responsibly increase their footprint and profit.
        Mrs. Beatty. Thank you. My time is up. Thank you very much
    for that.
        Chairman McHenry. I will now recognize the gentleman from
    Minnesota, the majority whip, Mr. Emmer, for 5 minutes.
        Mr. Emmer. Thank you, Mr. Chairman. Thank you for holding
    this important hearing today, and like the others, I want to
    thank all the witnesses for your testimony.
        Mr. Karunamurthy, if I said it right, and hopefully I did,
    you have had a long and very impressive career in machine
    learning and artificial intelligence. Just to get us started, I
    have a quick ``yes'' or ``no'' question. Have you contemplated
    any potential intersections between AI and digital assets?
        Mr. Karunamurthy. Yes.
        Mr. Emmer. All right. I figured that was the answer. The
    next is between AI and digital assets, it seems necessary and
    inevitable to meet. In fact, there could realistically be a
    future symbiotic relationship between the two. In many ways, AI
    has opened their eyes to the reality of digital inauthenticity
    and crypto ensures digital authenticity. In other words, the
    immutable nature of digital assets transactions offers digital
    assets and blockchain, it offers them up as at least one tool
    for authenticating and if not potentially being the key tool,
    which is becoming increasingly unclear with the rise of AI. Of
    course it goes without saying that AI offers significant
    promises and benefits to our economy. AI, along with digital
    assets, are leading us toward a powerful global digital
    economy. We just need to ensure this technology is crafted by
    Americans and with American values.
        Scale AI is at the forefront of AI development, given AI's
    need for reliable data. When I think of what makes data
    reliable, I am not only concentrated on the data inputs
    themselves, but also the processes by which data is gathered
    and verified. Last month, there was an article in Cointelegraph
    that discussed the critical role of blockchain technology, a
    critical role that technology can play to ensure that data used
    in AI models is authentic, immutable, traceable, and
    transparent. This would therefore give certainty. Sir, as you
    consider how data labeling will evolve over the medium and long
    term, do you think blockchain technology can be used as a tool
    to ensure data authenticity in the future? If so, how?
        Mr. Karunamurthy. Thank you, Congressman, and I appreciate
    the opportunity to talk about the important role AI plays for
    U.S. economy and American competitiveness. We consider this the
    most transformative technology we faced in the last 20 years,
    and so it is important to get ahead of these opportunities and
    also highlight safety risks. The provenance of the data that we
    use to train these models and that these models leverage in
    order to come up with decisions is increasingly important,
    paramount for us to observe, monitor, and to find authoritative
    sources of information. So whether we are talking about the
    blockchain or digital identity solutions, those all have an
    important role to play in ensuring that data is accurate and
    up-to-date.
        We also have developed a variety of techniques that
    understand how these AI models leverage that data. One
    important one that we have let in our written testimony was red
    teaming to understand that those models can be tricked or
    fooled in certain ways to believe that someone is who they are
    not or to reveal private information and so we are developing a
    lot of those techniques that will come at the intersection of
    blockchain and other solutions and helping establish the
    providence of the data use of the models and how that drives
    decisionmaking.
        Mr. Emmer. Thank you. I believe the convergence of
    blockchain and AI cannot only improve the trustworthiness of
    data, but the decentralization of artificial intelligence data
    can mitigate single point of failure security issues and
    additionally as AI systems communicate with each other and need
    to transact with each other to obtain information, digital
    assets and AI can have that symbiotic relationship. Sir, what
    do you envision is the primary way that digital asset
    technology can engage and interact with artificial intelligence
    systems in the future?
        Mr. Karunamurthy. Thank you. An important way is to make
    open and transparent what information is being used by these AI
    models so that as a public we can evaluate where these models
    are giving safe and responsible answers or where these models
    might have been misled. Having an open and transparent system
    to understand where data is coming from or where data has
    evolved over time can be incrementally important to the U.S.
    economy and it is important for the evaluation of these
    systems.
        Mr. Emmer. Well, thank you. Finally, Mr. Karunamurthy--
    sorry, I was going to blow it at some point--to develop and
    lead in the next iteration of the internet powered by AI and
    digital assets, we need this technology to be designed and
    built by Americans and with American values. How can government
    support entrepreneurs and businesses like yours in the global
    talent world to not only attract, but to retain high skilled
    tech talent right here in the United States?
        Mr. Karunamurthy. Yes. We are absolutely in a global talent
    world, and we have seen increasing evidence that countries like
    China have really powerful AI talents, and they are putting in
    decisions of authority where they can train future AI models.
    We believe here in the U.S., it is important to highlight the
    role that developing talent and upskilling workers plays in
    developing the future of this technology, which goes beyond
    just researchers. It also means American workers needs to
    understand how AI can be used to assist them in their job
    functions and understand where these models are capable or
    where they might not be capable today. So we are spending a lot
    of effort in helping upskill and retrain workers in these
    technologies.
        Mr. Emmer. Thank you, and thank you, Mr. Chair.
        Chairman McHenry. The gentleman from Illinois, Mr. Casten,
    is recognized for 5 minutes.
        Mr. Casten. Thank you, Mr. Chairman, and thank you, also,
    Mr. Chairman, for putting together the AI Working Group. I
    think we have had really a very productive set of hearings.
    Thank you all because I know a lot of you have come out many
    times for this.
        So in a lot of our Working Group hearings, I have raised
    this concern about disparate impact and the idea that you can
    have discriminatory outcomes independent of discriminatory
    intent, and there is a long history of companies being held
    accountable one way or the other for that. I have this growing
    concern that you see a lot of people saying, well, the AI did
    it, I did not do it, I did not program it to do this. Before
    digging into the questions, I am not a lawyer, and, Ms. Rice,
    you are way smarter than this, and I thought you had in your
    written testimony, like, an interesting block on this. I wonder
    if you would just share with us briefly the legal distinction
    between disparate treatment, disparate impact, the relative
    enforceability, and some of the things you have shared
    previously about what to think about before I get to the
    questions I want to go through.
        Ms. Rice. Congressman, thank you. Sure. I would be happy to
    do that. So an example of disparate treatment is where
    technology firms treat people differently based on their
    protected class characteristics. A case example of that is in
    our lawsuit against Facebook in which Facebook, it designed a
    model so that if you are advertising for an employment, a
    credit, or a housing opportunity, you could turn off African-
    American audiences. You could turn off Asian-American
    audiences. You could turn off Hispanic audiences. You could
    never turn off white audiences. So that is an example of
    differing treatment. You are treating people differently based
    on their protected class status.
        An example of disparate impact is where you have a rule or
    a system that is not race based, but it is generating
    discriminatory outcomes. An example of that is our lawsuit
    against Prudential in which we challenged the use of their
    insurance scoring system. If you looked at the underwriting
    algorithm, there were no variables that were akin to things
    like race or gender, but it was generating discriminatory
    outcomes. In that case, Prudential had to prove that it had a
    business necessity for using the insurance scoring model.
        Mr. Casten. I am sorry because I am just sensitive of the
    time, but is it safe to say that it is a higher legal bar to
    get enforcement done to disparate outcomes than disparate
    treatment?
        Ms. Rice. Yes. Under disparate impact, yes.
        Mr. Casten. Yes, lacking the smoking gun. Okay. So for
    those of you who are building and running AI algorithms, do any
    of you currently have in that code blocks that test for
    disparate treatment or for disparate impact? Just show of
    hands. Do any of you include those blocks of code? Mr. Zecca,
    you do include those blocks of code? Okay.
        Mr. Zecca. We do. We are not in banking, but, yes, for our
    order types to ensure that all customers are treated equally.
        Mr. Casten. Okay. Ms. Osborne, you did not raise your hand.
    Is there any particular reason?
        Ms. Osborne. Yes. So at our sites, we do not develop in-
    house AI.
        Mr. Casten. Okay. So it is not a relevant question for you.
    Fair enough. Okay. So I am glad that you do, and do you test
    for disparate treatment or disparate outcome, right? Like in
    the example, like all of you who raised your hand, given that
    it is a higher legal bar, do you also test for it that you are
    getting different outcomes?
        [Nonverbal response.]
        Mr. Casten. Yes, across the bar. Okay. Part of the reason I
    ask all of that is because that is a legal bar, right? But
    there is this thing, Project 2025, that my colleagues across
    the aisle and the Republican candidate for President are making
    a big push on, when one of their provisions on page 538 is to
    remove disparate impact as a valid theory of discrimination for
    race and other claims. By the way, Justice Thomas, Justice
    Alito have indicated in some of their dissenting opinions that
    they also do not think those are valid claims.
        If those laws were to be taken away, can you commit that
    you would continue in your algorithms to test for disparate
    impact, or is this only something that we are doing because it
    has currently got some legal protection against it? Would all
    of you maintain those models even if the government were to say
    discrimination is now fine because thanks to Project 2025, we
    are all good with discriminating?
        [A chorus of ayes.]
        Mr. Casten. We have yeses? We are going to hold you to
    that. Thank you. I yield back.
        Chairman McHenry. Now we will go to the gentleman from
    Pennsylvania, Mr. Meuser, for 5 minutes.
        Mr. Meuser. Thank you, Mr. Chairman. Thank you all very
    much for being here. It is interesting, fascinating, good to
    hear. So the EU's AI Act, which the Financial Times reported as
    rushed and vague, serves as a clear example where regulations
    are very often the problem. They specifically excluded
    technologies like generative AI, as we have been discussing,
    focuses instead on existing tools, highlighting the limitations
    of broad regulatory approaches. Considering AI's impact, we
    need to recognize the disproportionate burden that regulation
    places on small businesses. Small businesses typically bear
    regulatory costs more intensely as evidenced by many things
    that this administration put forth, so just the Consumer
    Financial Protection Bureau's (CFPB's) 1071 rule, FinCEN's
    beneficial ownership requirements, and Basel III Endgame.
        Mr. Zecca, I would like to just start with you. The EU's AI
    Act is currently being implemented. How would you grade the
    European approach so far?
        Mr. Zecca. Well, I think the core question is how do you
    prepare for the future, and the AI Act is sort of a point in
    time. My main worry is as the technology develops, if you look
    back over 2 years, it has already moved from where they started
    because the technology has moved. How do you prepare for the
    future? I also worry about the context of a single AI regulator
    that is responsible for the enforcement because I do believe
    that, like, for example, we operate markets in Europe. There
    are prudential regulators for the markets. What happens if they
    disagree with the AI office? So I think there are a number of
    areas there that are unclear on how enforcement is going to
    work and how technology is going to innovate.
        Mr. Meuser. How would the EU's AI Act affect Nasdaq?
        Mr. Zecca. For our operations in Europe, it will have an
    impact. It will have an impact on a lot of U.S. companies that
    operate in Europe.
        Mr. Meuser. Okay. Do you believe these standards should be
    calibrated for the particular type of use case, or should it
    focus on regulating the technology broadly?
        Mr. Zecca. Well, thanks for that question. I think that is
    the critical idea. I think it is risky to regulate the
    technology because the same technology can be used for
    different use cases. You should regulate the risks and
    opportunities of the use case. For example, anti-financial
    crime (AFC), financial crime, one of the biggest things we want
    to ensure is that the financial sector can use AI to fight
    financial crime even if there is not pure explainability
    because we are trying to keep up with criminals.
        Mr. Meuser. All right. Thank you. Ms. Osborne, your credit
    union is similar in assets to many community banks but larger
    than many of mine. Can you describe how AI has helped you scale
    your business and better serve your customers?
        Ms. Osborne. Absolutely. Thank you for your question, Mr.
    Congressman. At our asset size, $1.6 billion, currently in
    terms of AI, we are using it primarily for customer service by
    introducing our virtual assistant, Olive. We have been able to
    streamline and really focus on the right level of support for
    our members for those that require a human to interact with
    them. We provide that ability. I see that as a great case study
    across all financial institutions any time we can automate. We
    can improve the outcome of decisioning, and any type of data
    input today that is very manual, that is a great benefit,
    especially for a not-for-profit organization like Great Lakes
    Credit Union.
        Mr. Meuser. Is it expensive to take it on board?
        Ms. Osborne. It was not, no. It was in line with what my
    expectations are for a technology of that size.
        Mr. Meuser. Are your competitors engaged in equipping
    themselves with AI as well?
        Ms. Osborne. They are starting to. So our current provider,
    Interface AI, they serve both banks and credit unions, and they
    have currently over a 100 clients. There are several providers
    like them, so you are starting to see that adoption ramp up.
        Mr. Meuser. All right. When I was in business, particularly
    in the early 2000s, we had a saying that if you do not use IT
    as a weapon, somebody is going to use it against you, so I
    would imagine your outlook on AI is pretty much the same. So if
    it were regulated in line with the EU Act, for instance, where
    cost prohibitions came from the regulations that were
    excessive, how would that affect your ability to serve your
    customers in your day-to-day operation?
        Ms. Osborne. Yes. So today's regulations are very
    technology agnostic, so that is benefit for us. If additional
    regulations were proposed, that would increase the cost for our
    institution to serve our members. Those costs would have to
    then be passed down, and that would be a burden to our
    consumers.
        Mr. Meuser. Okay. Running out of time, but I would love to
    also hear from perhaps all of you on some of the downside that
    you think about. We are talking about the positive aspects.
    What are some of the downside? Somehow we can get that
    afterwards.
        Mr. Meuser. I yield back, Mr. Chairman.
        Chairman McHenry. The gentlewoman from Massachusetts, Ms.
    Pressley, is recognized for 5 minutes.
        Ms. Pressley. Thank you, Chair McHenry, and to all our
    witnesses for joining us today.
        As we examine the role of artificial intelligence in
    financial services and housing, we must be honest about the
    truth. The algorithms that value our homes, filter our job
    applications, and approve or deny our loans exist in a larger
    context of biases that have plagued our communities for
    centuries. This is evident in the home appraisal industry where
    systemic racism robs families of color of generational wealth
    and economic opportunity simply because of the color of our
    skin. The data is clear and the data is damning. Homes in Black
    neighborhoods are consistently undervalued by at least 21
    percent compared to similar homes in predominantly white
    neighborhoods. This translates to more than $162 billion in
    loss equity for Black homeowners, opportunities denied, dreams
    deferred, and the perpetuation of the vast racial wealth gap
    that exist in our country. Now, some may argue that AI and
    automated valuation models offer an objective solution to bias
    and discrimination, but I caution against blind faith in these
    systems. These systems are only as biased as the data used to
    train them and the human beings who design them.
        Mr. Karunamurthy, is that right? I have been practicing all
    day. Names matter and I want to get it right, okay? Is that
    right?
        [Nonverbal response.]
        Ms. Pressley. Okay. You are the chief technology officer at
    one of the most financially valuable AI startups in the
    country. Are you taking a race-conscious approach to
    eliminating bias while developing and deploying AI technology?
        Mr. Karunamurthy. Thank you, Congresswoman Pressley. We do
    believe these are really important issues to consider in the
    future of this technology. So from the forefront or from the
    beginning of our approach to evaluating AI models, we have
    considered what different sort of perspectives are important to
    ensure that we are not just putting blind faith in this
    technology, that we understand how decisions are made by AI,
    what sources of data they are using, and that data avoids bias
    or the course of action.
        An example of that would be ensuring that when these models
    use geographic data, they are not biased by prior sources of
    data and maybe information on regions or districts that may be
    out of date or that may not give the full picture of what may
    be present in a given area. Then, we try to ensure that these
    models explain their decisionmaking to human operators so that
    there can be human oversight of how these models came to the
    decision. Thank you.
        Ms. Pressley. Thank you. Dr. Linda, Zillow has been a major
    player in the home appraisal industry by making home valuation
    data readily accessible on the internet. How is Zillow ensuring
    that its AI algorithm does not perpetuate financial services
    industry's long legacy of redlining and housing discrimination?
        Mr. Linda. Thank you for the question, Congresswoman. It is
    absolutely a problem as you pointed out, and we work tirelessly
    to measure, understand the bias. As an example, our Zestimate,
    our main tool that we developed, started a company to empower
    our customer with information so that they can often challenge
    some of the bias that you pointed out. Every change to the
    algorithm is not only evaluated on does it improve the
    accuracy, but also does it improve fairness, especially with
    respect to the dominant race of the demographic of the people
    living in the neighborhood. We did make some changes. As you
    noted, the neighborhood is an important aspect. We make sure
    our algorithm does not look just at the small neighborhood, but
    also look at a much bigger area, which allows us to be much
    more comprehensive and resulted in more objective, accurate,
    and fair assessment of a value.
        Ms. Pressley. Thank you. Ms. Rice, you heard these two
    responses. Do you agree that technology companies like Scale AI
    and Zillow are doing everything they can to ensure AI tools and
    housing appraisals do not become vehicles for discrimination?
    Yes or no.
        Ms. Rice. Yes, regarding Zillow. I do not know for Scale.
        Ms. Pressley. What steps should these companies be taking
    additionally in your opinion?
        Ms. Rice. I think Zillow has a very great model, and we
    partner with Zillow a lot on responsible AI and fairness
    principles. So testing the underlying data to make sure that it
    is representative, make sure that it is accurate and make sure
    that it is not missing critical information, testing the
    underlying data used to train the models to make sure that
    there are not proxies that are highly associated with race. So
    that is a pre-processing phase. In processing, making sure that
    you are testing those systems for their outcomes and you
    understand what the outcomes are, and if you are seeing
    discriminatory outcomes, recalibrating.
        Ms. Pressley. I have to reclaim my time. Thank you so much.
    It is clear that we need more transparency, accountability, and
    equity for both human and AI-driven home appraisals. We need
    robust, independent bias testing of the AI systems, diverse
    representation in the appraisal industry, and a commitment to
    advancing racial equity from our regulators and industry
    stakeholders if we already use AI responsibly and safely in our
    communities. I still believe in the promise of homeownership as
    a path to financial security and generational wealth.
        Chairman McHenry. The gentleman from Texas, Mr. Williams of
    Texas, is now recognized for 5 minutes.
        Mr. Williams of Texas. Thank you, Mr. Chairman, and thank
    you all of you for being here, and I am a great supporter of
    innovation. I am a car dealer in Texas, and artificial
    intelligence has been at the forefront of innovation. Across
    all industries, AI has assisted in making business operations
    easier and less time intensive, including in the financial
    services industry, and AI provides increased workstream
    efficiency and improved client engagement. It is important for
    us to understand this evolution so that we may continue to make
    the practice of running a business more efficient and foster
    greater competition within industries. Ms. Osborne, could you
    elaborate on how artificial intelligence helps you and your
    credit union make lending decisions?
        Ms. Osborne. Thank you for your question, Congressman
    Williams. At GLCU, we do not currently use AI for lending.
    However, it is an area we are actively pursuing. I think it is
    important to note that 2 weeks ago, Karen Harbin, CEO for
    Commonwealth Credit Union, testified and spoke of her use of
    Zest AI and how that has helped her credit union be innovative
    as well as expand lending to areas, demographics, disadvantaged
    individuals that in the past, because she is lending outside of
    the credit score, she was unable to do so beforehand.
        Mr. Williams of Texas. Okay. Thank you. Mr. Linda, you
    mentioned in your testimony that Zillow has been using
    artificial intelligence in some capacity since the company was
    founded, and so you are no stranger to the expansion and ever-
    changing landscape of artificial intelligence. Some view
    artificial intelligence as dangerous and confusing, but you and
    your company have used it to turn something also confusing,
    like finding a house and obtaining a mortgage into a more
    simplified consumer-friendly process. Mr. Linda, how has Zillow
    used artificial intelligence to make the often opaque and
    stressful process of finding a home easier for consumers?
        Mr. Linda. Thank you for the question, Congressman. As you
    pointed out, from the beginning of the company, we started by
    putting customer and transparency at the forefront of what we
    do and using AI to do that. Empowering customer information,
    how much a particular home might be worth was the start, and
    then we continued by using AI to help you find all the homes on
    the market. It is not easy to understand all the inventory out
    there, and AI, again, is a great tool to do it. During the
    pandemic, it became extremely useful to have an AI tool that
    allows you to virtually and remotely tour a home. These are
    just some of the examples where AI makes the process more
    transparent, accessible, and we are looking forward to how we
    can further use the technology to further empower our customers
    to get them home.
        Mr. Williams of Texas. Okay. Thank you. One of the biggest
    challenges in running a successful small business is the number
    of roles owners have to play, from CEO to CFOs to CMO to
    managing inventory and fulfilling or delivering orders. By
    leveraging AI tools, small businesses can access the same
    technology that big business use to their advantage, making
    them more efficient and giving them the ability to make more
    informed financial decisions. Operating a small business is
    becoming increasingly harder due to challenges like high
    inflation, supply chain disruptions, and worker shortages.
        Artificial intelligence tools give small business the
    possibility to run smoothly and be able to tackle other issues
    that they face. All of this shows why small businesses should
    not be overlooked in discussions surrounding AI because they
    understand the competitive benefits and risk perspective, so,
    and I will say I am chairman of the Small Business Committee,
    so this means a lot to us. Mr. Reynolds, as Congress looks to
    potentially regulate emerging technologies like AI, how should
    we try to ensure that small businesses can access and use AI
    applications that allow them to compete with a larger market
    participant?
        Mr. Reynolds. Thank you, Congressman. I think critical to
    that is that the regulation has to be designed to allow rapid
    innovation. I think, obviously, to the extent that we can
    innovate and lower the cost of the technology that allows
    different market participants to be able to embrace the
    technology and use it. If you look, for instance, at FIS, we
    have some very large clients. We have some very small clients
    and it is critical to us that we provide AI services and allow
    access to AI, both for large institutions, but also for small
    institutions because that really does level the playing field.
        Mr. Williams of Texas. I have a limited amount of time. I
    am just going to ask you another short question, Mr. Reynolds.
    Can you expand on the use of artificial intelligence by
    financial institutions to assist in risk management and
    preventing unlawful activity?
        Mr. Reynolds. Yes, sir. We think, actually, that artificial
    intelligence, in many ways, is going to be critical to the
    evolution of combating financing of terrorism, combating money
    laundering, combating fraud. One of the most significant
    problems in that is the false positive rate. We all have seen
    that for years. It runs in the high 90's percent, sometimes
    even more than that. We really think that the use of generative
    AI, especially in fraud, is going to dramatically lower the
    false positive rate, which will, again, allow more transactions
    throughout the economy, but allow better transactions. We have
    seen some very positive results in early testing on fraud
    systems where we have dramatically reduced false positives and
    increased true transactions.
        Mr. Williams of Texas. Okay. I yield my time back. Thank
    you very much.
        Chairman McHenry. The gentleman from Texas, Mr. Green, is
    recognized for 5 minutes.
        Mr. Green. Thank you, Mr. Chairman. I would like to
    associate myself with the remarks, to a great extent, of the
    chairman and also the ranking member. The ranking member has
    touched on some issues that are very important to me, and I
    would like to look a little bit further into these issues.
        Ms. Rice, you indicate that artificial intelligence, and I
    am reading from your message that you have codified, artificial
    intelligence is the new civil rights/human rights frontier.
    That is a strong statement, ``new civil rights/human rights
    frontier.'' You go on to explain about data going in and how
    the data, over the years, has been a part of corruption, and
    when you put something in that is discriminatory, then you will
    probably get information that is going to reflect what you put
    in. I will be quite honest. It saddens me to know that we have
    suffered all of these years of actual discrimination--slavery,
    convict leasing, invidious discrimination in the form of
    segregation--and now artificial intelligence may not only
    perpetuate, but do it in a more horrific way. Could you please
    elaborate more on why you would conclude that this is the civil
    rights/human rights movement of our time, perhaps, you say the
    next frontier?
        Ms. Rice. Certainly. Much like the industrial revolution,
    the industrial frontier changed the landscape of our Nation, of
    our economy, of our neighborhoods, and societies. AI will be
    doing the same thing, we believe. It is going to drive
    innovation. It is going to be the main driver of every single
    market in our Nation. From that perspective, we believe that AI
    and technology are the new civil and human rights frontier.
        You are absolutely right, bad data in means bad data out,
    which is why at the National Fair Housing Alliance, we have
    been innovating on new systems and constructs that compel
    legacy systems and new systems that are being developed to be
    fairer. Just a few months ago, we released a groundbreaking
    report on a new strategy that we have developed with one of our
    partners, FairPlay AI. That strategy utilizes distribution
    matching. It is a way of sort of globally optimizing for
    systems for both profit and fairness to make sure that they are
    not generating biased outcomes.
        Mr. Green. Mr. Linda, you seem to have a similar position.
    Reading from your report, you indicate that we place a great
    emphasis on transparency, meaning your organization, and
    fairness in the housing industry, which has a history of
    discrimination and bias against people of color and other
    historically marginalized groups. You go into the LLM, which is
    the large language model, and you give some explanations, but
    your views are very similar. Would you kindly explain to us why
    you have concluded that this is something that can be of harm
    to people of color and other marginalized groups?
        Mr. Linda. Thank you for the really important question. I
    think any kind of progress is to start from a point of
    realizing what we are starting with, and any data we use when
    building AI system will have imprinted past history of
    segregation, and we have to be accountable to it. We have to
    understand it. To your point, AI can pose a lot of risk, but AI
    can also shed light on some of these things. We recently shared
    some of our research where we used AI to understand at a
    national scale what are the common phrases that are being used
    by listing agents when they put listings online, and we saw
    stark differences. Just from starting from a point of
    understanding, that helps us to put that early on in our
    development cycle and build better products and better AI model
    because we are aware of this.
        Second, we want to make sure that auditing models for
    fairness is a part of our everyday work. That is what we do at
    Zillow, and that is what we try and do, share it with the
    community and publish our methodologies. It is a vital part of
    developing responsible AI system for the benefit of our
    customers.
        Mr. Green. Thank you. My time is up. Thank you, Mr.
    Chairman. I trust we will use it for good. Thank you.
        Chairman McHenry. The gentleman from Tennessee, Mr. Rose,
    is recognized for 5 minutes.
        Mr. Rose. Thank you, Chairman McHenry, and I also thank
    Ranking Member Waters for holding this hearing, and thank you
    to our witnesses for being here today and sharing your time
    with us and your expertise.
        I am proud to say that in my home State of Tennessee, there
    are already financial institutions that are leveraging
    artificial intelligence to bring great benefits. One such
    institution is Oak Ridge National Laboratory Federal Credit
    Union, or ORNL--I will try to say it--based out of Oak Ridge,
    Tennessee. Thanks to artificial intelligence models, ORNL will
    be able to serve more customers and provide customers with a
    better experience overall. I am proud to say that Tennessee is
    home to financial institutions such as ORNL FCU and hope that
    as we discuss and debate artificial intelligence regulation, we
    do not hamper innovation.
        Ms. Osborne, one area that interests me regarding the
    utilization of artificial intelligence is expanding access to
    financial services to those who live in rural communities
    versus high population areas. How does leveraging AI help to
    expand access to rural communities?
        Ms. Osborne. Thank you for your question, Congressman Rose,
    and also for your story. That is a great story to hear. At
    Great Lakes Credit Union, we have a similar story. We have
    introduced AI and seen great results from that. In terms of
    expanding access to more rural areas and just broadly overall,
    I can speak from Great Lakes Credit Union's perspective. By use
    of Olive, our virtual AI agent, we are able to serve members
    where we are able to provide support to them 24/7. So
    regardless of where they live, what time zone, what their needs
    are, they are able to have a conversation with Olive and get
    the support they need. That also plays, in fact, with other
    areas of AI that we are exploring that would allow us to lend
    to more members, members beyond the credit score that we were
    not able to in the past that maybe are newer or in
    disadvantaged communities.
        Mr. Rose. Thank you. I appreciate that insight. There have
    been some concerns that consumers are limited in recourse
    against unfavorable decisions made by artificial intelligence.
    However, the Fair Credit Reporting Act and the Equal Credit
    Opportunity Act regulate adverse action when a creditor acts
    against a consumer based on their credit report. Mr. Reynolds,
    is it not true that adverse action regulation applies equally
    to human-made decisions and system's rules-based decisions or
    AI decisions?
        Mr. Reynolds. Yes, it does.
        Mr. Rose. Thank you. I think that is pretty plain and
    simple. Ms. Osborne, as a former bank director, I know
    firsthand how burdensome and difficult it can be to comply Bank
    Secrecy Act information and share it with the appropriate
    agency. How can artificial intelligence be leveraged to help
    satisfy regulatory requirements?
        Ms. Osborne. Yes. Thank you for that question. Yes. So
    Suspicious Activity Report (SAR) is a perfect example. Any
    other type of reporting, if we have the ability to leverage
    technology like AI to automate, to make it more accurate and
    timely, there is tremendous benefit. That would reduce resource
    times, would improve our response times to meet regulatory
    requirements as well, and because it is more accurate, less
    review is needed. Then we can utilize resources in better ways,
    such as improving or providing additional support for our
    members.
        Mr. Rose. I agree with that. Mr. Reynolds, as financial
    institutions increasingly leverage artificial intelligence to
    better identify suspicious activity reports, will it not be
    imperative that they receive strong feedback from Financial
    Crimes Enforcement, or FinCEN, regarding the suspicious
    activity reports submitted so that their models are
    appropriately calibrated?
        Mr. Reynolds. Yes, I absolutely agree with that and I think
    that this is something which AI and generative AI, in
    particular, really should enable the agency to much more
    quickly and much more thoroughly provide feedback real time, to
    your point, to make sure that models continue to learn the
    right lessons.
        Mr. Rose. I could not agree more. Frankly, I believe that
    the Bank Secrecy Act needs to be reformed in a wholesale
    fashion but until then, FinCEN simply must provide productive
    feedback to financial institutions so that they can reduce the
    regulatory cost, the cost of regulation through tools such as
    artificial intelligence. I can say firsthand as a bank
    director, I saw this repeatedly. I suspect everybody up here
    who has had to deal with this directly, the lack of effective
    feedback from FinCEN is a major problem for institutions in
    knowing what to report. Thank you, Mr. Chairman. I yield back.
        Mr. Steil [presiding]. Mr. Horsford is recognized for 5
    minutes.
        Mr. Horsford. I want to thank the chairman and the ranking
    member for holding this very important hearing, and to thank
    you to our witnesses for appearing to discuss the ongoing
    adoption of artificial intelligence in our financial sector.
        AI has the capacity to change the way we work, the way we
    interact as consumers, and the very way that we live. As we
    examine the rapid proliferation of new AI development, we owe
    it to the constituents that we serve to remain forward thinking
    on these trends, which will undoubtedly have a significant
    impact on their livelihoods. We have an ethical, moral, and
    societal responsibility as leaders to establish safeguards to
    ensure that the advancement of AI protects all of our
    communities, including communities of color. Everyone deserves
    equal opportunity to benefit from the transformational change
    of what I believe will 1 day be a powerful public good.
        The constituents I represent in Nevada and across the
    country as chairman of the Congressional Black Caucus are
    counting on us to be forward thinking and to establish a
    responsible AI policy framework today so that everyone can
    enjoy an equitable deployment in the future. Recognizing the
    challenges ahead, the Congressional Black Caucus (CBC) launched
    the AI policy series to bring together leaders from government,
    industry, academia, and civil society to provide an overview of
    best practices to help shape these policies.
        Under the thoughtful leadership of vice chair,
    Congresswoman Yvette Clarke, Congresswoman Barbara Lee, and
    Ranking Member Cleaver, we have explored the importance of
    design, the implications of job equity, and the potential
    impacts on the racial wealth gap. I want to thank the ranking
    member, Congresswoman Maxine Waters, for her leadership in
    bringing us to Silicon Valley to meet with industry leaders
    specifically. Similarly to the work done by this committee,
    these briefings have been focused on equipping Members of
    Congress with the expertise that is necessary to apply existing
    laws to the utilization of AI and the understanding to identify
    regulatory gaps that need to be fixed before they lead to
    consumer harm. However, our work distinguishes itself by
    centering marginalized voices and examining the dangerous
    implications of bias that exist within AI. We must ensure that
    not only do our communities have the opportunity to participate
    fully in the transition to this technology, but to also develop
    the protections and the guardrails that can prevent the worst
    impacts of inherent bias in a proactive way. Both in lending
    decisions and automation of certain careers, unchecked reliance
    on AI can certainly lead to algorithmic redlining.
        I wanted to ask Mr. Karunamurthy, how do you believe AI
    developers can proactively address the social and political
    values, including bias around race, that are appearing in
    generative AI today?
        Mr. Karunamurthy. Thank you, Congressman Horsford. These
    are really important issues for developers to consider, but
    also folks adopting AI technology in industry and in financial
    services and housing companies.
        Two key parts of this process that I want to highlight. The
    first is considering the data that goes into training these
    models and that these models leverage to make decisions.
    Ensuring that data is authoritative and up-to-date is
    incredibly important, also observing that you have robust and
    diverse perspectives in that data can be very important to
    ensure these AI models give objective and fair answers to given
    questions. The second is the importance of testing these models
    and these AI systems as they are built, observing situations
    where they may give a biased answer, and understanding how the
    decisionmaking process of these AI systems led to bias. This is
    an important first step in mitigating potential harms of these
    systems and it is also important for human oversight of these
    systems going forward.
        Mr. Horsford. Thank you. These are the conversations that
    must be had if we are going to prevent discrimination within
    our financial system from becoming baked in the very start as
    we continue to train new models. It is so much more that can be
    accomplished with these technologies if we are able to harness
    their capabilities to create, not hinder, economic advancement.
    Again, I think there is a lot more that we need to explore
    around the Fair Housing Act and the protections that are
    afforded and the dangerous positions that are proposed by
    Trump's Project 2025 that impact this proposal under that
    agenda. So I look forward to having further discussion with
    those of you on the panel. I yield back.
        Mr. Steil. The gentleman yields back. I now recognize
    myself for 5 minutes.
        I will start with you, if I can, Mr. Ondrej. Where are we?
    I am looking down here. Sorry about that. Thank you for being
    here.
        One of the challenges we face, in particular, is home
    prices. The median mortgage has doubled in the past 3-and-a-
    half years from $1,200 to $2,400 a year. Huge challenges with
    where we have been placed from the Biden Administration as it
    relates to inflation. That will ultimately, God willing, come
    down as we get reckless spending under control. Other areas I
    think we have room for improvement is on the transaction costs
    on buying and selling homes. How has Zillow utilized the AI
    application to reduce the cost and complexity associated with
    selling a home?
        Mr. Linda. Thank you so much for the question. We
    definitely recognize that affordability and the constrained
    supply is a big challenge for our customer and makes the
    already daunting process even more complicated. Where we see
    that we, and especially AI, can play a big role is in improving
    the ability of our customers to understand all the supply on
    the market.
        Imagine a customer who searches with a specific zip code,
    and they might only be shown that specific result. We use AI to
    augment their search results to make sure they are aware of the
    other options they have and perhaps lead them toward a home
    they might otherwise not consider. Similarly, as already was
    mentioned, the cost associated with the process. The more we
    can automate mundane, repetitive tasks for the lending
    officers, for real estate agents, the more efficient we can
    make them and potentially some of these costs can be
    transferred to the customer.
        Mr. Steil. That is ultimately the goal, Mr. Linda. In
    particular, as we look at this, what are the concerns you have
    that an impediment might be placed from a regulatory
    standpoint, maybe with good intentions, but that would
    ultimately have a negative impact on the end user and the
    consumer.
        Mr. Linda. Yes. We truly believe that the existing
    regulations, such as the Fair Housing Act, should be applied
    equally to the business processes, the real estate
    professional, as well as to AI. So what we are looking for is
    exploring how that could be applied, and, of course, we are
    supportive on exploring additional risk. We fully support
    proportional risk-based and flexible regulation that further
    enables a company like us and everybody else to innovate for
    the customer without stifling innovation.
        Mr. Steil. Thank you very much. Let me jump over to you,
    Mr. John Zecca, if I can. Technology, specifically AI, can
    really drive down costs in particular and be a big benefit for
    markets. Can you briefly touch on how Nasdaq's identifying
    applications of AI that can improve the way the exchange works?
        Mr. Zecca. Sure. So I put them in two buckets. One is the
    trading activity itself, and we recently had approved the first
    AI-powered order type, and that was a process with the SEC to
    get them comfortable with the changes. It gets a better fill
    rate. I think you will start to see more order types that take
    market conditions into account, so that is one bucket. The
    second is in surveillance. We have an obligation to monitor our
    markets. We have huge surveillance systems that take, ingest
    billions of transactions a day, and so AI will help us get
    those cases together and our investigators get a better case.
        Mr. Steil. As that is coming online, what has your
    interaction been like with regulators?
        Mr. Zecca. The SEC has been very pragmatic and very
    interested. I find often when technology is being adopted, not
    only by us, but by the government, that there is just a
    different knowledge level. I think AI, cloud before it, cyber,
    of course, are areas where the government is very focused as
    well. So that has helped the discussion because they do come
    with a growing knowledge base, and they have been very
    constructive in their questions.
        Mr. Steil. So then let us dive in. You are talking about
    the use cases for AI. In your testimony, you mentioned $3
    trillion illicit funds flowed through the financial system last
    year, we are all working to reduce that. AI could be a
    significant tool. One example that is attracting a lot of
    attention is synthetic identity fraud where criminals are using
    AI to present themselves as if they were legitimate customers.
    How is Nasdaq working to stay one step ahead of the bad actors?
        Mr. Zacca. Yes. We are working with our customers. So we
    are not a bank ourselves, but on the AFC side, we are working
    with community banks, large banks. So those organizations in
    the check fraud area, which is a huge and has been a growing
    problem, in the money laundering area. They are coming with
    problems that they feel need to be addressed. So we are using
    those as the case studies as we try to develop our technology.
        Mr. Steil. Are these criminals utilizing AI to try to
    defraud or avoid Bank Secrecy Act (BSA)/AML filters?
        Mr. Zecca. I think there is some of that. I mean, they are
    using technology to try to, in the check fraud space, have more
    realistic-looking checks that they are stealing in an AML to
    move their money more transparently. If I could make one point,
    I do think it is important that regulators recognize that there
    are different use cases and the regulation that should apply to
    them should be different. So if we are fighting financial
    crime, we probably should have a different standard than using
    the same technology in another space, and so I think that is
    something that regulators need to consider.
        Mr. Steil. Thank you very much. Thank you all for being
    here.
        I will now recognize Ms. Garcia for 5 minutes for the
    purpose of asking questions.
        Ms. Garcia. Thank you, Mr. Chairman. It is great to see
    some familiar faces that we have visited either before here or
    in the task force. So I first want to thank Ranking Member
    Waters for her leadership on this very important issue in
    addition, of course, both to the chair and the ranking member
    for creation of the Working Group. It has been a pleasure to
    work with my colleagues as we explored AI and how to ensure the
    United States proceeds in a safe, competitive, fair, and
    efficient manner as more and more people learn to work and live
    with AI.
        We all know how expensive buying models can be. For
    example, our local institutions cannot afford to invest and
    create their own AI models. Ms. Osborne, as of right now, are
    smaller institutions like your credit union able to compete
    with larger entities?
        Ms. Osborne. Thank you for your question, Congresswoman
    Garcia. So as a $1.6-billion institution, if we compare that in
    size to all banks and all credit unions, we are considered
    smaller.
        Ms. Garcia. Smaller. So are you able to compete?
        Ms. Osborne. We are able to compete, and that is due to
    third-party relationships that we have available to us. We are
    too small to develop AI in-house.
        Ms. Garcia. Okay. So what can we do to better support our
    community-based organizations as AI becomes more and more of a
    commonplace?
        Ms. Osborne. Thank you for that question. I would say our
    existing regulation is technology agnostic, so that right there
    is a great starting point. We have the guardrails in place to
    enable financial institutions of all sizes to proceed with
    entering into relationships like our current relationship with
    interface.ai. One area that would be helpful is if our
    regulators provided a statement or clarity in terms of where AI
    fits in that regulation. My assumption, and in partnership with
    vice chairman of the NCUA, I joined an AI panel with him last
    March, and we had great conversations about the needed clarity
    in regulation in which AI is considered technology. We consider
    AI technology, thus regulation exists for that.
        Ms. Garcia. What about any security concerns taking place
    when your financial institutions or other small entities
    explore the adoption of third-party service?
        Ms. Osborne. So we have a very thorough third-party
    management policy and program due to regulation. That applies
    across the board, no matter which partner you are looking at,
    so all technology partners, including AI. So one factor that we
    spend quite a bit of time looking into is the security of that
    particular partner. We look at any type of data breaches they
    may have had, what their reputation in the market is. We review
    their financials. We spend a great deal of time vetting that
    partner before we make a decision. That long, lengthy process
    is important to ensure we provide safety and security to our
    members. It also is why sometimes it takes a bit longer to make
    a decision on a partner, but it is the right reason to do so.
        Ms. Garcia. Well, I was encouraged about your comments with
    regard to addressing some language access issues. So is there
    not enough language access, AI third-party providers out there
    already that you all can easily adapt?
        Ms. Osborne. So I can speak to our current relationship
    with interface.ai. That solution provides the ability to speak
    80 different languages to our members. We are just starting,
    just scratching the surface by offering Spanish very soon.
    After that, we plan to introduce Polish. AI is constantly
    evolving and moving, and so I see the adoption of language
    somewhat newer and be made available to us. So I expect that
    will continue to grow over time.
        Ms. Garcia. Since your English version is Olive, will the
    Spanish version be Popeye?
        Ms. Osborne. It will be Olivia.
        Ms. Garcia. Olivia? Okay. Well, we will take Olivia. Okay.
    Well, thank you so much. Mr. Karunamurthy, in your opinion,
    could AI impact these jobs or similar jobs to those in my area,
    which are like longshoremen, construction workers, and pipeline
    workers? So many workers are just scared of losing their jobs.
    How can we reassure them?
        Mr. Karunamurthy. Thank you, Congresswoman Garcia. This is
    a really important factor for us to consider as this technology
    gets adopted across the U.S. economy. American workers are some
    of the most capable, intelligent workers on the planet, and
    they are immensely capable of leveraging this technology.
    However, we have to make a broad adoption in upskilling workers
    and helping them understand where AI can be trusted and where
    AI needs additional oversight in order to be used successfully.
    We consider that the important work that we do here at scale,
    and hopefully, we can be more competitive as an economy as we
    leverage this successfully.
        Ms. Garcia. How can we reassure them not to be scared?
        Mr. Karunamurthy. I think it starts by helping workers
    understand where we are with this technology, what the
    limitations of it are, and where the real opportunities are so
    that folks feel that this is something that assists human
    workers rather than something that is just going to replace
    jobs.
        Ms. Garcia. All right. Thank you. I yield back.
        Mr. Fitzgerald [presiding]. The gentlelady yields back. The
    gentleman from Kentucky, Mr. Barr, is recognized for 5 minutes.
        Mr. Barr. Thank you, Mr. Chairman, and I want to first say
    how much I appreciate the good work of the Working Group on AI
    in this Congress, and it is important work because AI and
    related technological progress in financial products hold a lot
    of promise for innovation. A responsible approach to
    developments in financial technology is to strike the right
    balance between innovation and regulation to ensure that AI
    generates net benefits and enhances the well-being of
    Americans. We must recognize that government regulators already
    have regulations, rules, guidance, and laws in place that put
    restrictions on uses of technologies by financial institutions.
        Congress and any future administration must be exact and
    definitive in identifying AI use cases for which those tools
    are thought to be somehow insufficient. Congress should not
    defer solely to often opaque regulatory agencies to effectively
    legislate through regulation on AI and related technological
    innovations. We should not rely on simple fears and speculation
    on potential adversities of innovation. As well, I believe that
    as regulatory agencies themselves adopt AI or utilize their own
    regulatory algorithmic black boxes, they must adhere to the
    same principles and regulatory treatment that they require of
    the private sector.
        Mr. Zecca, let me ask you a question first. Do you agree
    that Federal regulators of financial institutions, including
    the Federal Reserve System (Fed), FDIC, and OCC, already have
    regulations, rules, guidance, and laws that place restrictions
    on use of technologies, including AI, generative AI, and the
    like, by financial institutions and third-party vendors with
    whom they contract?
        Mr. Zecca. Well, thank you for the question. Yes, it is one
    of the most heavily regulated industries in the world.
        Mr. Barr. One of the pieces of feedback that I am getting,
    especially from smaller community banks and community financial
    institutions, is that when they rely on a third-party vendor
    that provides AI services, they are concerned that regulators
    would hold them accountable for the artificial intelligence and
    the algorithms developed by these third-party vendors. Should
    the law provide a safe harbor for especially small community
    financial institutions and allocate the liability properly?
        Mr. Zecca. Well, I think it is an important question. I
    would look at it from the perspective, I think any organization
    is going to want to know what is going on, so there is going to
    be a natural interest for the organization to understand what
    is happening. At what point does liability come in? I think
    that is a point for discussion. We are a vendor to a lot of
    community banks, and we work collaboratively with them to
    ensure that we are meeting their needs and that they understand
    the processes that we are using.
        Mr. Barr. Well, let us direct that question to Ms. Osborne.
    What is your view on the liability that could attach to a
    credit union, for example, when using a third-party vendor?
        Ms. Osborne. Yes. Thank you for your question, Congressman
    Barr. From my perspective, an AI third-party provider is a
    technology provider, and so the same rules apply for all. I do
    not see a benefit of assigning liability for a specific AI
    where if we think of the flip side, the arguments that we have
    been making is that AI is a technology provider, so we should,
    with technology agnostic regulation, treat them equally. From
    our perspective, AI providers, we do treat more as a critical
    vendor, so they are vetted more thoroughly. So if that vetting
    and review is done properly, we should have awareness of
    technology providers that may not be meeting the needs of the
    industry.
        Mr. Barr. Final question for any of our witnesses. Can
    anyone comment on the activities at the CFPB? These days, the
    Bureau has shown itself to be more inclined to move the market
    by intimidation and supposition rather than by evidence and
    administrative procedure. Industry players responsible for
    developing the innovative AI solutions that can create
    beneficial change for consumers need an informed regulator that
    establishes and maintains reliable, consistent rules of the
    road. Are you all observing overreach by the Bureau and fear of
    speculative regulatory actions that would stifle innovation?
    Anyone worried about CFPB stifling innovation in artificial
    intelligence?
        [No response.]
        Mr. Barr. No, maybe? Okay. Crickets. Okay. Well, the reason
    why I asked that question--my time has expired--we have heard
    from some fintechs that this is actually going to hamper bank
    fintech partnerships. With that, I yield back.
        Mr. Fitzgerald. The gentleman yields back. We now recognize
    the gentleman from North Carolina, Mr. Nickel, for 5 minutes.
        Mr. Nickel. Thank you so much. Thanks to our witnesses for
    being here with us today. I am very glad we are holding today's
    hearing on artificial intelligence, or AI. It is important that
    Congress, and particularly the Financial Services Committee,
    does not fall behind as new technology advances. Also, thanks
    to Chair McHenry and Ranking Member Waters for their work on
    this in a bipartisan way, and of course, thanks to our current
    chair, Fitzgerald, for traveling around the world on a
    bipartisan Congressional Delegation (CODEL) where we made AI a
    significant part of the work of our group as we traveled the
    world.
        First question, Mr. Zecca, to you. The financial services
    industry seems to be ahead of many other sectors when it comes
    to AI. Not only have market participants been using it for many
    years, but we already have a strong legal framework to protect
    consumers, prevent discrimination, and more. We need to
    determine what tools we already have to address AI, assess
    regulatory gaps, and continue to encourage innovation here in
    the U.S. Do you agree with that, and what tools does the
    Federal Government already have to address AI?
        Mr. Zecca. I do agree with the premise. I think that the
    Federal Government, I will talk about the agency that I work
    with most, which is the SEC. Not only are they in the process
    of adopting AI for their own operations, but they also have a
    great familiarity with the markets, how technology changes have
    come, and they have looked at changes in the past, so deepfakes
    or other things that might impact market stability.
        Over the years, the SEC has come out with circuit breakers,
    limit up/limit down bans, which stopped trading around stocks.
    So there are a lot of changes that have been made over the
    years that would apply to AI just like any other issue. So I
    think there is a lot of work that has already been done.
        Mr. Nickel. Can you talk a little bit about regulatory
    gaps? What do you see there and what should Congress be doing?
        Mr. Zecca. Well, I do think that it is always important
    with new technology to see where the gaps are. I think the
    technology is still evolving in many areas. So where a gap may
    develop may not exist yet, so I think in areas we want to look
    at market stability, use of deep fakes, certainty about
    individual identities are all things that I think are
    important. Over time, gaps will develop, and I think that is
    the beauty of the regulatory process. Aside from legislation,
    there is the opportunity for specialty agencies that are
    experts in the area to take action.
        Mr. Nickel. Thank you so much. Ms. Rice, next question to
    you. In their latest draft funding bills, House Republicans
    have cut funding for fair housing enforcement and blocked HUD's
    ability to enforce the affirmatively furthering fair housing
    provision of the Fair Housing Act. Can you discuss the
    importance of Federal fair housing enforcement to combat
    automated discrimination in housing and lending through online
    housing platforms and AI technologies?
        Ms. Rice. Congressman Nickel, thank you for the question.
    Yes, what we know from decades of research is that when we
    remove discriminatory barriers from the marketplace, we
    actually increase profitability and productivity. That enables
    companies to expand their footprints, right, and have a broader
    market reach. So that is one of the reasons why some of the
    largest lending institutions in the United States of America
    joined with us in asking the previous administration to not
    place undue restrictions on the disparate impact rule that
    would disable entities from being able to appropriately use
    disparate impact to analyze their systems for fairness. It is
    also one of the reasons why the Business Roundtable, of course,
    which is comprised of the Nation's largest corporations in
    America, adopted a platform calling for the adoption of a
    strong, affirmatively furthering fair housing rule. When we
    level the playing field for everyone, we increase productivity
    and opportunity.
        Mr. Nickel. Thank you so much. Mr. Reynolds, to you. AI-
    generated audio and visual content, also known as deepfakes,
    has armed criminals with a new tool, contributing to a
    significant increase in the frequency and sophistication of
    attacks against or through the financial services sector. In
    addition to attacking financial institutions, deepfakes have
    the potential to create widespread panic and confusion,
    especially when combined with social media, leading to consumer
    fraud, scams, and significant risk to the U.S. financial
    stability. How is your organization and industry responding to
    these emerging threats, especially combating malicious AI-
    generated audio and visual content?
        Mr. Reynolds. Yes. Thank you, Congressman, for the
    question.
        Mr. Nickel. I realize now we have 5 seconds.
        Mr. Reynolds. Very briefly. We are looking at two things.
    One is reinforcing existing controls that help stop that;
    second, using generative AI to actually combat generative AI.
        Mr. Nickel. Thanks. I yield back.
        Mr. Fitzgerald. The gentleman yields back. We now go to the
    gentleman from Georgia, Mr. Loudermilk, for 5 minutes.
        Mr. Loudermilk. Well, thank you, Mr. Chairman. Thank you
    all for being on this panel. It is a very important subject,
    something I have been very interested in since I have been in
    Congress. I have spent 30 years in the IT industry, and I think
    back from when I first got involved, how quickly technology has
    grown since then over the previous 30 years. In fact, if you go
    back and you look at how the internet has grown, it was the
    internet that drove most of the technology advances. If you
    look at these devices here, they have grown so much over the
    past few years, it is interesting to understand that the least
    used part of this is the telephone, right, because that was the
    most heavily regulated. The reason we saw such advancement in
    information technology is because of our entire structure, our
    economy here, technology has been the least regulated. It has
    been free from a lot of excessive government regulation.
        Now, a couple of Congresses ago, I was ranking member of
    the first Artificial Intelligence Task Force, and what we were
    talking about then, as far as AI, is different than what we are
    talking about today with generative AI. I am thinking 3 to 5
    years from now, we are going to be having an entirely different
    conversation about AI. I agree with some of the testimony is,
    if we can stop government from stamping out innovation and
    working to allow, with constraints, with guardrails, to protect
    individuals from AI. I understand a lot of people are scared by
    what they do not understand, but we can see a lot of
    advancements that are good for the industry there.
        With that, Mr. Reynolds, in previous hearings about AI and
    financial services, I had not had much of an opportunity to
    discuss the technology's application in payments and financial
    messaging. We talked about fraud detection and prevention, but
    I would like to talk about how AI is used to streamline and
    improve payments messaging. As you know, by early 2025, Fedwire
    will have completed a transition into the ISO 20022 financial
    messaging standard. The Society for Worldwide Interbank
    Financial Telecommunication (SWIFT) network will have fully
    phased out the SWIFT MT format, and transition to the SWIFT MX
    format, which is also aligned with that same standard, by
    November of next year. The ISO 20022 provides for much more
    information-rich messaging than the older messaging standard.
    Combined with artificial intelligence, this standard could
    vastly improve efficiency in payments, messaging, and create
    positive feedback loop and improving messaging algorithms.
        All of that leads up to this question: what benefits could
    a more information-rich financial messaging standard have in
    terms of integration with artificial intelligence?
        Mr. Reynolds. Thank you for the question, Congressman. I
    think, as you noted, as we move to a more information-rich
    architecture, it certainly allows for greater speed of
    payments. It allows for greater accuracy of payments. What it
    also allows is, on the regulatory side, it allows us to better
    understand how particular companies, how particular individuals
    are using payments, which should also increase our ability to
    deter and detect fraud. It should increase our ability, for
    instance, to protect vulnerable populations. We look
    consistently at, for instance, elderly account holders and how
    can generative AI systems perhaps look at behavior of their
    payments, notice anomalies, and potentially intervene to
    protect more vulnerable account holders.
        So I think when you see those more information-rich
    environments, there is certainly, I think, economic benefit for
    the system, but I think there is also a benefit to the
    consumers as well.
        Mr. Loudermilk. All right. Thank you. Follow up question on
    that. For the banks and businesses that FIS works with, what
    benefits could the new ISO 20022 standard combined with AI
    algorithms have in terms of payment messaging, if any?
        Mr. Reynolds. It will certainly make them more secure. I
    think, to start with that, safety and security is always one of
    the primary concerns that we have at FIS, and to ensure that
    for our clients. I think, additionally, again, as I noted
    before, we work with very large institutions. We work with very
    small institutions and everything in between. I think that
    moving to this standard and moving to that technology platform
    will allow us to really deploy technology that all firms of
    varying sizes can use, which levels the playing field.
        Mr. Loudermilk. Mr. Zecca, regarding trading, how is this
    messaging standard apply in securities, and is Nasdaq using any
    type of machine learning to process or analyze trading and
    settlement messages?
        Mr. Zecca. So we are. The surveillance piece is where we
    are the most active with machine learning and moving into
    generative AI, so trying to assess all of the data in the
    market and determine patterns of bad behavior, insider trading,
    and things like that. So over time, I can see we did have the
    first order type recently approved by the SEC that has AI in
    it, but I can see other use cases for it down the road.
        Mr. Loudermilk. All right. Thank you. I yield back.
        Mr. Fitzgerald. The gentleman yields back. We now recognize
    the gentlewoman from Michigan, Ms. Tlaib.
        Ms. Tlaib. Thank you, Mr. Chair. I know you have heard from
    a lot of my colleagues. One of the areas, though, I think is
    really critical to talk about is, while AI may offer a number
    of it, it can also promote what we call market concentration or
    manipulation and deepen existing power imbalances that we
    experience in the 12th Congressional District in Michigan, and
    also lead to discriminatory outcomes.
        AI, as you all know, played a large role in boosting
    investor ownership in single-family rentals, for example, has
    been documented. These institutional investors are often highly
    concentrated in specific markets, purchasing more than half of
    single-family rentals in Atlanta recently, and controlling 75
    percent of rental homes, I believe, right outside of Nashville,
    Tennessee and because such investors use LLCs and special-
    purpose vehicles, it is often impossible to know true market
    share that they enjoy. We have also seen how RealPage is being
    used by many landlords to set rental prices now, which is so
    incredibly scary. For example, it has been reported that 90
    percent of rental units in large buildings in Metro DC, right
    here in the area, are priced using that software.
        President Rice, what challenges does AI pose in terms of
    facilitating market concentration or manipulation? Can you give
    us a sense what legislative gaps exist and what we could be
    working on, such as, I have heard folks talk about a National
    Rental Housing Registry to get a clearer picture. I do not know
    really what the answer is and would love to hear from you.
        Ms. Rice. Thank you, Congresswoman Tlaib. Yes, you are
    absolutely right. So AI is powered by Big Data, and so your
    ability to effectively build AI models and use them is going to
    be associated with your ability to sort of harness and amass
    large amounts of data. You have to be able to get the data, you
    have to be able to control it and use it, and we know that
    larger institutions have a much better ability to acquire the
    huge data sets that are necessary for building AI systems.
        As you noted, we also know that large institutional
    investors have been able to use AI to strategically purchase
    single-family homes and take them off the market. We believe
    that there has to be both a principles-based and an outcomes-
    based system of legislation so that we can accurately govern AI
    systems.
        Ms. Tlaib. Just to kind of lead into the next kind of
    issue, what my team and I have noticed that AI has regularly
    tipped the scales toward landlords, in favor of landlords
    versus tenants. I know a host of technologies have been
    deployed by specific landlords to screen tenants, to collect
    payments, to evict families, and more. These tools raise a
    significant concern of the agro, whatever, the bias--I can
    never say that word--digital surveillance, and data collection,
    and security. Data collection should scare all of us. Today,
    tenants might actually have to grapple with racially biased
    facial recognition technology, which I have been pushing my
    colleagues to abandon in our country, or be forced to use apps
    to pay rent or communicate with their landlord.
        President Rice, can you touch base on some of the issues
    being raised on what we call now, I think in the nonprofit or
    folks or people that work in housing, the landlord tech
    movement?
        Ms. Rice. Certainly. One of the primary concerns related to
    proptech is the inability of consumers to get accurate
    information about the decisions that those technologies are
    making. So for example, if you are denied because of a tenant
    screening selection system, you do not currently have the right
    to know why that system denied you or even how it denied you or
    perhaps what data was used to deny you. If a dynamic pricing
    system is setting a rental price at a certain level, you do not
    currently have the right as a consumer to know, to see the
    transparency about how those rental prices were set.
        Ms. Tlaib. I ran out of time.
        Mr. Fitzgerald. The gentlewoman's time has expired. The
    gentlewoman from Missouri, Congresswoman Wagner.
        Mrs. Wagner. I thank you, Mr. Chairman. Mr. Zecca, I
    appreciated Nasdaq's participation in the AI Working Group
    roundtable earlier this year, and as a global technology
    provider and operator of exchanges, could you please describe
    the ways in which Nasdaq utilizes AI in its products and across
    markets? Briefly.
        Mr. Zecca. Sure. Well, I think you put it into the three
    categories. One is potential order types, in other words,
    adding liquidity to the market. The second is using both data
    and analytics to allow corporates and investors to make better
    decisions, so giving them higher quality data collated in
    different ways. Finally, it is ensuring that the markets are
    well surveilled. We also have solutions that work on compliance
    reporting and regulatory reporting, and then perhaps the most
    exciting is always we fight financial crime. We help eliminate
    money laundering and other financial crimes.
        Mrs. Wagner. Yes, that is key. What guardrails, sir, are in
    place to ensure there is proper security, oversight, and
    governance in AI?
        Mr. Zecca. Yes. So the corporate governance piece is
    important at each company. What we tried to do was start with
    the governance process. So we basically blocked off all access
    and we made a single path for approval, and that created a
    single inventory of all the models. So we know that all of the
    models are going through the same rigor. We assign a data
    scientist to each project and then the cross-functional group
    that approves all models includes lawyers, risk managers, and
    they look with our ethics policies how we are implementing and
    where the human interaction is and ensuring stability and all
    the other accountability and all of the factors that you need.
        Mrs. Wagner. I care deeply about retail and what I will
    call mainstream, everyday investors saving for their retirement
    and for the future. How does AI benefit the millions of retail
    investors participating in our capital markets, and could you
    please provide a few use cases?
        Mr. Zecca. Sure. I do think there is a huge opportunity
    here. Again, it is in its early days, but what we see are the
    main opportunities with that data and the analytics that we
    talked about. You can create products, unique products that
    allow mainstream investors to diversify their portfolios at a
    reasonable cost, types of tools and access to various
    investment vehicles they have never had before. You can also
    see a world where they can get financial advice provided
    through AI with human intervention but in a way that they
    probably could not have afforded if they had to get individual
    advisors. I think those are two great examples.
        Mrs. Wagner. Yes. I serve and I am proud to serve as the
    chairman of the Capital Markets Subcommittee, and as you
    highlighted in your written testimony, participants in our
    capital markets have been using AI for decades. How has Nasdaq
    approached this technology over the years and how has it
    changed with the, I would say, the development of GenAI?
        Mr. Zecca. Well, Congresswoman, I think you hit on the
    important point. A lot of the guardrails that we developed in
    the past, I think, apply with AI. So the markets over the years
    have built up a lot of contingencies for aberrant trading
    activity, for fast movements in the market. There are circuit
    breakers and other things. We also monitor social media to try
    to discover attempts to manipulate stocks. So all of those help
    us now, but I would argue we have the potential to enhance our
    monitoring with the AI as well, so AI will help us detect
    fraud.
        Mrs. Wagner. Would you describe how AI applicants are
    enabling a fairer and more efficient financial system?
        Mr. Zecca. I do think, as we said, the access is one. I
    also think that the financial crime is another one. There is a
    huge amount of financial crime that is an impact, it is a tax,
    but it also supports a lot of the crimes that we are most
    concerned about, human trafficking and terrorism. If AI can
    allow investigators to zero in much more quickly on potential
    crime, I think society benefits and that is a huge equalizer.
        Mrs. Wagner. Mr. Zecca, last year the SEC approved Nasdaq's
    filing to introduce the first exchange AI-powered order type.
    What is this new order type and which market participants
    benefit from this and what kind of engagement have you had with
    the SEC?
        Mr. Zecca. Sure. Well, the order type, basically, it is a
    midpoint order type, and so it is people who are not as focused
    on speed. They just want clarity of execution, so it kind of
    freezes the market for them. Normally that was a static period,
    and what we have done with AI is take market conditions into
    account, so it is a variable time horizon and that has
    increased and improved the fill rates. So that was something we
    had to explain to the SEC when they came with very intelligent
    questions.
        Mrs. Wagner. Thank you. My time has expired. I yield back.
        Mr. Fitzgerald. The gentlewoman yields back. We now
    recognize the gentleman from Ohio, Mr. Davidson, for 5 minutes.
        Mr. Davidson. I thank the chairman. It kind of out of order
    how I add my questions, but to stay on the capital markets
    theme there, Mr. Zecca. Nasdaq has a unique perspective on the
    market, obviously straddling some of the biggest tech companies
    in the world. One of the challenges that we see with the
    standard trading desk might have an analyst, kind of a math
    geek, and a programmer and they work together in tandem. You
    could see how AI could do a lot of the work of analytics, could
    do a lot of the work of the code, and might even be able to
    accelerate or lower the bar for the skill needed in the math,
    so it could change that function. Then you have core things
    that seem to be a challenge in the market right now. I mean, in
    theory, put call parity is a reality, but we all know there are
    shorts that are oversold at times.
        How do you see AI bringing reconciliation to markets like
    that, problems that are not getting solved today. You can see
    how it takes out labor and it is something that really works
    pretty efficiently already. How are we going to use AI to solve
    problems that are not getting adequately solved yet today?
        Mr. Zecca. Well, I think the first thing to remember is
    that the industry is heavily regulated. There are a lot of
    organizations, including the SEC, Financial Industry Regulatory
    Authority (FINRA), and us, watching the market. To the extent
    our tools are enhanced by AI, our capacity to catch activity
    that actually does violate the law increases, so I think that
    is a net positive.
        Mr. Davidson. Okay. Well, I hope that put call parity will
    be a reality using blockchain or AI or something, aside from
    just regulators that seem to miss a lot, but, hey, sports have
    refs that miss calls every now and then, too. When we review
    them, maybe we can make them right somehow. We will see how
    that goes as we continue to talk about regulating our capital
    markets.
        Dr. Linda, real estate is front and center. As people are
    dealing with inflation and the cost of living today, the cost
    of homeownership or even residence for people that are renters
    is one of the biggest challenges for households. If you look at
    Zillow, you guys have really created a hobby for lots of
    people. In some free time, they will go out and check. If I am
    traveling, I will say, oh, what is the market price for a house
    here versus where I live or whatever. So you collect a massive
    amount of data, but on that consumer end we have talked a lot.
    Several other people have asked questions on that.
        I am curious if you have any insight into the rental market
    because the Justice Department (DOJ) just did an investigation
    into a software company in Atlanta that was analyzing rents.
    They were basically helping the biggest private equity groups
    and hedge funds and real estate investment trusts (REITs) buy
    the software and rig the rental market to inflate prices and,
    frankly, raise the barrier. What is out there to offer hope for
    the consumer that we could find a way to lower the cost of home
    ownership?
        Mr. Linda. Thank you for the question, Congressman. I think
    you are getting to the key of how Zillow is trying to benefit
    the customer using AI, and that is with transparency. Similar
    to how we have Zestimate, our key model that puts a price on
    every roof in a wholesale market, we also do that in the rental
    market. On every home, customers can see what that rent might
    be, and that empowers them with information that they can use
    potentially to have a more informed discussion with that
    landlord and challenge them or ask them, why is the price you
    are asking me that much higher? That is another application
    where AI is used for the benefit of a customer by providing
    more transparency.
        Mr. Davidson. Yes, and this particular software company
    would target the biggest, wealthiest funds and create a barrier
    to entry so that they had a market there, and as we have all
    experienced, somehow, Zillow makes money. It is all free out
    there. Maybe we will save that for another scenario, but the
    tool is available for consumers and it does give people some
    availability.
        Finally, Ms. Osborne, if you look at credit unions, Ohio is
    certainly home to many of them and it works for our
    communities. You look at the costs, you are generally looking
    at third parties that you have to partner with in small
    business. As a small business owner myself, we could not build
    everything we wanted. You had to find trusted vendors. What is
    the regulatory burden like for you guys? On the other hand, you
    have regulators that are pushing you guys to collect massive
    amounts of data and engage in operation Choke Point 2.0, 3.0,
    whatever version of regulatory things there. What promise does
    this offer for you to run a more efficient operation and serve
    your community better?
        Ms. Osborne. Thank you for your question, Congressman
    Davidson. So from a financial institution perspective and as a
    smaller credit union, there are significant benefits of using
    AI. I found pricing from third parties to be comparable and as
    expected for the services they offer, weighing that against
    regulation, so our existing regulation is technology----
        Mr. Davidson. Thank you. I am sorry, I did not leave you
    with much time with the question, and please feel free to reach
    out to my office.
        Mr. Davidson. My time has expired, and I yield back.
        Mr. Fitzgerald. The gentleman yields back. The gentleman
    from Iowa, Mr. Nunn, is now recognized for 5 minutes.
        Mr. Nunn. Well, thank you very much, Chairman Fitzgerald,
    and thank you for the panel being here today. Together, we have
    spent more than six sessions going over an engagement on the
    future of artificial intelligence, its impact here, and the
    technology that comes with it. Nowhere is this more clear than
    in the financial sector and its opportunity to revolutionize
    where we are going. We recognize already today the human to AI
    ratio output is roughly one-to-one, but I think we all concur,
    in less than 5 years, that output is going to look more like a
    ratio of 1 to 1,000. It is a bold new space we are in, and with
    this comes both opportunity and clearly challenges as we
    discussed on this.
        In the course of certainly my time here in Congress, we are
    going to see AI advance to an over $4 trillion industry and has
    the opportunity to revolutionize economies around the globe. At
    the same point, it is enfranchising some of our greatest
    adversaries, both in the Nation, State, and at the hostile
    actor level. Generative AI, as we noted, is going to touch
    every aspect of this and it is going to unleash not only the
    economic productivity, but the empower communities like mine
    back in Iowa. So I want to start here with a conversation on
    how to implement this.
        We have a number of regulatory challenges. We have
    innovation being sparked, and as we learned from you, is that
    there are a lack of lanes in the road in where operators are
    going to be. In short, I believe we need a plan, which is why
    working with this team, we have introduced H.R. 7881, the AI
    Plan Act. 7781 discusses the opportunity for private sector to
    help work with key leads in the government sector--Treasury,
    Commerce, Homeland Security--to both help inform this, but
    shape something that is meaningful.
        So I want to get started with you, Mr. Reynolds. Do you
    believe artificial intelligence requires an entirely new
    legislative system and a regulatory regime that comes with
    that, or can we start to apply some of these existing laws and
    get us on the same sheet of music or on the same level of code,
    if you will?
        Mr. Reynolds. I think certainly in financial services, the
    existing regulatory regime is sufficient to deal with AI at
    this point. I think if we were looking at something more broad
    for AI, I think a principles-based sense that really is sort of
    technology agnostic. I think the challenge is with AI and
    generative AI, the different use cases will demand very
    different regulatory solutions, and so I think a one-size-fits-
    all will be very difficult for AI but specifically in financial
    services where I obviously specialize, I think the existing
    regulatory regime right now is sufficient.
        Mr. Nunn. I would agree with you. In fact, I think by the
    time we write laws, the level of AI that we have is probably
    going to advance beyond that. The principles-based seems like a
    much more prudent approach. I appreciate the innovation coming
    out of the private sector on this.
        Ms. Osborne, you shared with us, from your perspective,
    both at the credit union level, something that is hugely
    important for my main streets, my hometowns. The challenge here
    that we saw from the national security side, we heard from the
    CEO of Pindrop that there has been as much as a 450-percent
    increase in deepfake voice attacks using artificial
    intelligence. In fact, in as little as 2 seconds, a deepfake
    voice can be changed, fundamentally changing how you do
    consumer protection. Who do you see as your ally on the
    government level to be able to address when a small credit
    union like yours gets attacked? Who do you pick up the phone
    and call?
        Ms. Osborne. Thank you for your question, Congressman Nunn.
    So we are regulated by the National Credit Union Administration
    (NCUA), so in the event we had a security event or risk, we
    would reach out to both the Federal Bureau of Investigation
    (FBI) as well as the NCUA.
        Mr. Nunn. So not unlike our cyberattacks here, has that
    been flushed out because the challenge is when you get
    attacked, it is the same type of AI generative attack that can
    happen instantly to be able to hit a ton of credit unions,
    banks, lenders, all at the same time. Who in the Federal
    Government do you think is on point to deal with this?
        Ms. Osborne. I am not sure of the answer to that, so I
    would have to get back to you on it.
        Mr. Nunn. You are not alone.
        Ms. Osborne. Okay.
        Mr. Nunn. I am not sure, after our conversation, Congress
    is fully aware, and I would offer the Federal Government. All
    the more reason I think we need to have a collective effort
    when we are still at this one-to-one ratio versus when it is 1
    to 1,000.
        With my few seconds remaining here, Mr. Karunamurthy, you
    highlight here some of the future threats. What do you see that
    we should be dealing with today in Congress to help better the
    AI space, and I want to talk specifically about what threats
    should we be looking at now.
        Mr. Karunamurthy. Absolutely. These threats are real. We
    know that State actors like Iran and North Korea are using
    generative AI technologies to help spread misinformation, to
    probe our cybersecurity defenses. It is important to leverage
    techniques such as red teaming, which can help understand where
    AI systems are vulnerable and find ways to build more resilient
    systems. We have a great partnership with the Chief Digital and
    Artificial Intelligence Office (CDAO) within the Defense
    Department, and we hope to build more of those collaborations
    to spread more resilient systems in the future.
        Mr. Nunn. I could not agree with you more. I think there
    are good best practices out there. We need to incorporate more
    of them from recommendations on the private sector panels like
    this into what we are doing in the Federal Government. I
    appreciate you being here. I look forward to the future. Thank
    you, Mr. Chair. I yield back the remainder of my time.
        Mr. Fitzgerald. The gentleman yields back. The gentleman
    from South Carolina, Mr. Timmons, is recognized for 5 minutes.
        Mr. Timmons. Thank you, Mr. Chairman. I want to thank all
    the witnesses for being here today.
        Before the late 20th century, evaluating creditworthiness
    in the homebuying process was poorly standardized. Essentially,
    a homebuyer's ability to repay a line of credit was based on
    prior relationships to lenders and was highly subject to class,
    race, and gender biases. As a result, homebuying was largely
    unattainable to many Americans. However, in 1989, Fair Isaac
    Corporation (FICO) invented the first standardized consumer
    credit score. Upon its adoption, determining one's
    creditworthiness became easier for consumers and lenders to
    determine, and a standard was created that limited bias in the
    homebuying process. The results speak for themselves. As our
    technology has progressed, so have the factors that go into
    determining creditworthiness. This has led to the development
    of standardized credit scores, which provide an even more
    objective snapshot of an individual's risk profile. By
    innovating how we viewed creditworthiness, we allowed the
    American Dream to be accessed by a never-before-seen number of
    Americans. Despite the complex quantitative analysis being
    conducted throughout the lending process, the financial
    services industry continually has to fight back against claims
    of systemic racism in preferential lending.
        AI technology is no stranger to the financial services
    industry, as machine learning has been leveraged for a variety
    of use cases over the course of the last decade plus. The
    technology's continued development means it can potentially
    serve as a powerful tool to help financial institutions
    mitigate claims of discrimination. By implementing unbiased
    algorithms and rigorous oversight, AI can enhance objectiveness
    and transparency in the decisionmaking process. It has become
    clear that the technology's ability to process large tranches
    of data will make it yet another tool to determine risk and
    open up access to credit for many more Americans by removing
    human biases from lending decisions.
        As my colleagues across the aisle are seeking to stifle the
    technology's implementation amid new claims of bias and
    discrimination being contained within AI datasets, I believe
    that with appropriate oversight and intentional implementation,
    that is something that can be easily avoided. With
    transparency, we can all come to agree that is not a problem.
        Dr. Linda, would you agree that with proper data inputs, AI
    has the potential to expand access to lending opportunities and
    enable more Americans to access the credit they need to buy a
    home?
        Mr. Linda. Absolutely, I believe in that, and I can give
    you one example. At Zillow, if you are renting homes through
    the Zillow platform and the landlord works through Zillow for
    the payment, you can opt in to have your rental payments
    submitted to the credit reporting agencies, which actually we
    have seen, and as was pointed out before, can expand the access
    to credit. So we truly believe that AI has the potential as we
    look forward with the existing regulations.
        Mr. Timmons. Essentially using additional data inputs to
    make it more fair and more transparent?
        Mr. Linda. Yes.
        Mr. Timmons. Thank you. Ms. Osborne, what role does AI play
    in the lending process, and how has the industry begun to adopt
    AI technology, and has it expanded access to credit for more
    individuals?
        Ms. Osborne. Thank you for your question, Congressman
    Timmons. So currently at GLCU, we do not use AI for lending.
    However, it is an area we are exploring because of the benefits
    it introduces to be able to lend to individuals that we are
    unable to determine creditworthiness for today. There was a
    testimony 2 weeks ago by the CEO of Commonwealth Credit Union,
    Karen Harbin. She spoke of a relationship with Zest AI. Zest AI
    enables just that. So she is able to use that platform to lend
    to disadvantaged individuals by going beyond the credit score
    and using transaction data like frequent bill pays, you have
    paid your utility company 12 months in a row, things like that.
    That is a great benefit to AI that really broadens the
    capabilities of financial institutions to lend to those in
    need.
        Mr. Timmons. You would do that by accessing their checking
    account and seeing the monthly payments and that they are made
    on time? Is that how you do that?
        Ms. Osborne. Absolutely, yes. We have access to a wealth of
    data that we should be using for those type of decisions.
        Mr. Timmons. Thank you. Dr. Linda, you kind of touched on
    this, but how else has Zillow utilized AI to expand home
    ownership opportunities?
        Mr. Linda. We do that by informing the customers of the
    process. We are exploring how they can find what it takes to
    buy a home by empowering them with information of, for
    instance, what a home is worth, by making it easier through AI,
    through things like natural language search to find homes on
    the market and understand what home they might be purchasing
    and by empowering the real estate operators to spend more time
    with a customer, touring homes in person, walking them through
    the process, and really getting them to the home they
    eventually want to move into.
        Mr. Timmons. Thank you for that. AI is here to stay, and we
    need to use it to the best of our ability while mitigating any
    adverse impacts it can have. Appreciate you all being here, and
    with that, Mr. Chairman, I yield back. Thanks.
        Mr. Fitzgerald. The gentleman yields back. I now recognize
    the gentleman from Tennessee, Mr. Ogles, for 5 minutes.
        Mr. Ogles. Thank you, Mr. Chairman, and thank you to the
    witnesses for being here today.
        AI is certainly on everyone's mind these days. It seems
    like it comes up in almost every conversation that I have with
    constituents or stakeholders. Since my district includes part
    of Nashville Music City, I have learned a lot about the music
    industry's hopes for and concerns about the technology. On one
    hand, as you might suspect, they have concerns about how
    intellectual property will be protected as generative AI comes
    into wider use. However, many in the industry are also quick to
    point out that AI-based tools are incredibly useful in things
    like music production and strongly caution against taking
    reckless actions that could hinder the use or development.
        I bring up that example to highlight a principle that also
    applies in the financial services sector. While everyone,
    including companies in the industry, lawmakers, and
    policymakers, consumers alike need to be attentive to ensure
    that AI tools are used in a way that upholds the rights of all
    parties, protects rather than diminishes their security, and
    ensures fairness, we also have to ensure that we are not
    stifling beneficial innovation. Let us be honest. When it comes
    to AI, that is a little bit of a challenge, right? Where is the
    gives and takes there? There is a lot of innovation using AI,
    so we do not know all of the ways it could be useful or
    empowering, driving down costs, improving access to financial
    products, or empowering the creation or expansion of
    businesses, but with that, there is also some downsides that we
    may not quite realize yet.
        Ms. Osborne, in your written testimony, you described the
    NCUA's risk management rules as robust. You note that their
    previous requests for information have indicated that they
    understand the credit unions face considerable and wide-ranging
    competitive pressures where AI can help. In your experience, at
    least so far, is the NCUA's regulatory approach to credit union
    use of AI striking a good balance between ensuring compliance
    with the law and welcoming beneficial innovation where there is
    room for improvement, ma'am?
        Ms. Osborne. Thank you for your question, Congressman, and
    I do. So the regulations that exist today provide the
    appropriate guardrails to make decisions on use of AI. AI is a
    technology, and our regulation today, as it stands, is
    technology agnostic, so it currently applies. AI fits nicely
    into that. Where I do think there is an opportunity for further
    clarity is a statement or some type of comment from our
    regulator, the NCUA, that AI is a technology, and then,
    therefore, it would fall within our existing guardrails.
        Mr. Ogles. Well, you literally jumped to my next question.
    So you note that the adoption rate of AI programs by credit
    unions is still relatively low. Do you believe that it would be
    helpful to increase adoption if the government would give you
    some sort of statement or some clarity on the issue and how
    they can and cannot be utilized because, again, when you look
    at the regulatory State, especially in the financial services
    sector, there is always that hedge of getting too far out in
    front of the regulators because if you do, then they snap you
    back, and they penalize you in the process.
        Ms. Osborne. Yes, that is an excellent point, and it is a
    concern that has been shared with me from other credit unions
    as well. It creates hesitation to engage in a partnership with
    an AI solution where we all know the benefits of AI technology
    are so ginormous where we still cannot measure it. So by
    providing that clarity, it would help credit unions and other
    financial institutions to make better decisions on that.
        Mr. Ogles. This is somewhat subjective, of course, but when
    you look at AI, and if it can truly be implemented in such a
    way to, from your perspective, further vet someone who is
    coming in for credit because you are having lower risk or you
    have lowered your risk, that is where you have lower defaults,
    there is a savings there, there is a profit factor, but a
    savings there for the consumer, right? That also increases
    access at a time where you have greater inflation, where rents
    have increased, and, quite frankly, for a lot of individuals,
    the pursuit of the American Dream and that picket fence is
    slipping away. You now have the opportunity to get some of
    those people back into the equation.
        So I do think it is imperative, Mr. Chairman, that as we
    look at AI, as we look at, especially into the financial
    services sector, that we encourage, if not force, the CFPB, the
    other NCUAs, et cetera, to provide clarity so that our
    financial institutions can be bold leaders in this space, both
    from the perspective of protecting consumer data and the
    consumer themselves, but also creating better and greater
    opportunity for those that otherwise may not qualify.
        With that, I had other questions for most of you, and I
    apologize for not getting to them. I really appreciate all of
    you being here and sitting there and enlightening us on this
    issue. With that, Mr. Chairman, I yield back.
        Mr. Fitzgerald. The gentleman yields back. I am now going
    to recognize myself for 5 minutes.
        I just had a couple of questions. I am a member of
    Financial Services but also a member of Judiciary. I happen to
    be the only member on both committees right now, so I did have
    a couple of questions. I thought maybe we could just go right
    down the line. Mr. Karunamurthy, let me direct it to you first.
    What safeguards do we have in place to avoid copywritten
    material, and then the second part would be on anything you
    would want to hold a patent on. Do you have any comments on
    that? We can go right down the row and everybody can kind of
    give us a short answer on that.
        Mr. Karunamurthy. Thank you. It is important to understand
    the role that intellectual property (IP) rights and copyright
    plays in the development of this technology. We take this
    approach of testing and evaluating how models perform in really
    interesting scenarios to understand when the models might have
    been trained on material that might be problematic and
    situations where that model might help with that material. That
    is an important first step in understanding what the impact of
    violations of IP rights may play in development of this
    technology. We hope that will inform future discussions over
    how to understand the data that is used to train the models and
    what data can be used in the future.
        Mr. Linda. Thank you for the question. We certainly comply
    with all the existing regulation and our contractual obligation
    to our partners, who do publish data on a platform like Zillow.
    To your question about patenting, we lead with transparency. We
    are trying to share a lot of our methodology, including open
    sourcing, some of it. On occasions, we do have a regular
    process, and we explore what from our technology we want to
    patent. We use our legal patent expertise to guide us through
    the process.
        Ms. Osborne. At our current asset size, we do not develop
    in-house AI. We use third-party, so I cannot really comment.
        Mr. Fitzgerald. Thank you.
        Mr. Reynolds. Thank you, Congressman. As a technology
    company, obviously our product is intellectual property, so
    this is a particular concern for us with GenAI. One of the
    issues right now that exists in the industry where I think
    there could be clarity is the ability to either copyright or
    patent products that involve GenAI or have used GenAI in their
    creation. So that is obviously something we are very focused on
    and looking for increased clarity in the legal regime around
    that.
        Mr. Fitzgerald. Very good.
        Ms. Rice. I would agree with the comments of Mr. Reynolds.
    We find the process of trying to obtain a patent extremely
    difficult and very costly. We are a nonprofit organization, so
    our ability to hire very expensive attorneys is very limited.
    There are not a lot of attorneys that have expertise in this
    area and they charge very, very high rates, so anything that
    can help democratize that patent process would be beneficial to
    nonprofit groups.
        Mr. Zecca. Finally, I would just note we are spending a lot
    of time right now on data ownership and control. So we are
    rewriting a lot of contracts to ensure that we have mutually
    agreed ownership rights and understand what our rights going
    forward are, and just to think more broadly on intellectual
    property, including patent ownership as AI becomes more
    involved in creation of patents, you know, what are the patent
    rights I think will be important.
        Mr. Fitzgerald. Very good. I will yield, and we will now
    recognize the gentlewoman from California, Mrs. Kim, for 5
    minutes.
        Mrs. Kim. Thank you very much, and I would thank all the
    witnesses for joining us today. It really was an honor for me
    to personally serve on the Working Committee on AI, and through
    many discussions that we have had through the roundtable
    discussions, I learned a lot. I am glad that now we are having
    a full committee hearing at this setting to display the AI
    capabilities and the new opportunities that the technology may
    bring into the financial services sector.
        As you know, advancements in AI have created a lot of
    potential, but there are also a lot of fears about how AI will
    change how we interact with one another and conduct our work.
    History tells us that skepticism of new technologies is often
    unfounded and misplaced, as was the case with the railroad 200
    years ago and most recently personal computers. So, Mr.
    Reynolds, let me ask you a question first. One of the issues
    raised in our Working Group discussion was the issue of AI
    explainability. In your testimony, you highlighted how
    explainability is part of your core principles as you integrate
    AI and GenAI. So can you touch on how you are meeting those
    core principles on explainability and addressing the black box
    issue?
        Mr. Reynolds. Yes. Thank you, Congresswoman. I think,
    fundamentally, we start from the premise that I think you
    touched on, that for GenAI to be successful, it really has to
    be trusted. One of the ways that we think it is imperative for
    it to be trusted is explainability. If people see it as a black
    box, if they see it as something where they do not understand
    why the result is coming out of it, then they are never going
    to fully trust it. That is not to suggest GenAI will not make
    mistakes. Human beings do all the time. GenAI will also, and it
    will hopefully learn from those mistakes as you educate the
    models.
        Fundamentally, what we tried to do is we really start from
    the first principles of adhering to explainability as well as
    the other principle-based areas that we talked about. What we
    do is we actually have rigorous regulatory guidance within the
    company where both in terms of intake and then in ongoing
    supervision of the models, we look at them very carefully to
    ensure that they constantly adhere to those principles.
        Mrs. Kim. That is how you are enabling AI models to be
    audited, right?
        Mr. Reynolds. Yes, ma'am.
        Mrs. Kim. Yes. Okay. Thanks. How is AI being deployed in
    the payments industry to detect fraudulent payments or any
    other nefarious activities?
        Mr. Reynolds. I think if you look at AI more generally, it
    has been deployed across payments and in the industry for quite
    a long time. It originally started, I think, as a very basic AI
    that was more of a rules-based system and has grown over time
    to machine learning. I think what we are starting to see now,
    though--and we have started to prototype some of this and
    started to test it--we have seen very significant results of
    much lower false positives, but also, importantly for
    consumers, much more payments that are allowed because they are
    not flagged inappropriately as potentially fraudulent. So we
    think actually what we will see is consumers will have much
    less false positives, which makes the system safer and faster,
    but we will also see better payment flows.
        Mrs. Kim. Thank you. Let me ask a question to Mr. Zecca. I
    want to continue on that track on how AI can be leveraged to
    fight criminal activity. So can you talk about how hard it
    would be to identify millions of data points that identify
    criminal activity without the use of AI?
        Mr. Zecca. Well, thank you for the question. I think it is
    critical. We are on the frontier of a new opportunity to
    connect the dots. When you look at any financial crime, you are
    looking for a needle in a haystack. The people that we are up
    against are trying to obfuscate, they are trying to hide their
    activity, and sometimes it is a question of different bank
    accounts, different leads, different countries. AI can perform
    a lot of that work so that the investigators get a much more
    developed case, and they get fewer false positives, as Mr.
    Reynolds was saying. I think that is a potential game changer,
    particularly as crime gets more sophisticated.
        Mrs. Kim. Considering you are leveraging AI to improve the
    effectiveness of financial risk management programs, can you
    speak about the accuracy of AI in identifying illicit activity,
    and are there any real case studies you can share with the
    committee?
        Mr. Zecca. Well, let me come back with that because I think
    it is an interesting question on the case studies, and I do not
    have them handy.
        What we have tried to do, first of all, we do validate it
    through our own work and, of course, our customers because we
    are selling these to banks and institutions that are also doing
    their own validation. We are seeing about a 30 percent increase
    in efficiency, and that means a reduction of false positives.
    Now, that is not the same thing as saying everything is going
    to develop into a case, but we are definitely getting people on
    the road to getting to those cases faster.
        Mrs. Kim. Thank you very much. My time is up. Appreciate
    it.
        Mr. Lawler [presiding]. The gentlelady's time has expired.
    The gentleman from Nebraska, Mr. Flood, is now recognized for 5
    minutes.
        Mr. Flood. Thank you, Mr. Chairman. I had the great
    privilege of serving on the AI Working Group, which I really
    enjoyed. Before I begin my questions, I would like to quickly
    recall some of the main takeaways I had from those roundtables.
        I think it is worth starting with the simple explanation of
    terms. Artificial intelligence covers a lot of ground,
    including some technology that is not really all that new. I
    personally am a little less interested in the use of things
    like algorithms, which have been around for a long time, than
    the use of technology that has shocked the world since the
    debut of ChatGPT, for example. Well, we identified a number of
    interesting use cases with the financial service industry for
    artificial intelligence. We found less use cases for generative
    AI, specifically. A lot of the use cases for generative AI that
    we did hear about, for example, it is used in chatbots, do not
    necessarily raise the types of red flags that I worry about as
    much from a regulatory standpoint.
        I know that some of my colleagues are very concerned about
    the potential for things like AI discriminating against people
    based on race or ethnicity. That is a hugely important issue.
    There is no doubt that if a company discriminates against
    protected classes in a violation of law, they need to be held
    liable regardless of whether it is an algorithm or a human that
    did the discriminating.
        To the extent that there are gaps, I think it is
    appropriate to have a conversation about legislation to fill
    them. That being said, I did not necessarily see lots of
    evidence of that in our meetings, and when you think about it,
    that may be partially because companies have used AI in things
    like underwriting for many years at this point. While the term
    AI sounds new, some of what we are talking about today is not
    new or groundbreaking.
        That leads me to one of my main takeaways from these
    panels. I think that some of the greatest threats from AI in
    financial services are not necessarily going to come from how
    regulated entities use or misuse the technology, but how
    nefarious actors could use generative AI. On that note, I want
    to mention that I worked on a bill with my colleague,
    Representative Pettersen, that is noticed for this hearing. It
    is called the Stop Deep Fake Scams Act. It is a good bill and a
    bipartisan opportunity to highlight some of the problems that
    we are having in the face and age of generative AI.
        Mr. Linda, I want to briefly pivot to generative AI use in
    the housing market. You indicated in your testimony that Zillow
    is interested in using generative AI tools in real estate
    transactions through your platform. Can you expound on that?
    What specific ways would you use generative AI in real estate
    transactions?
        Mr. Linda. Thank you for the question, Congressman.
    Focusing on the customer, we do see generative AI being a great
    tool in terms of informing them of the process. We did ask many
    of our customers would you use generative AI and how would you
    feel about it, and they share with us that often, especially
    first-time buyers, feel that it is difficult to ask a landlord
    a question, you know, what does it take. They feel like they
    might be judged and that they cannot afford a home. They
    actually express that having something like a chatbot, having
    an experience that is always available, patient, can answer
    them with the factual information would be actually useful to
    get them the information they need.
        On the professional side, we see big use for generative AI
    to save loan officers and agents time from mundane repetitive
    tasks, things like AI-powered call summarization. If you get on
    the phone with a customer, you have your notes, so you know
    what the follow up steps are, you can move to the next
    customer, so you can spend more time in person with them. These
    are just some of the use cases we are thinking about that we
    feel generative AI can be very good at.
        Mr. Flood. Mr. Karunamurthy--now I practiced this for the
    better part of 5 minutes in my head and I just did a poor job
    there--Karunamurthy. In your testimony, you identified some of
    the work your firm does for financial services clients.
    Nominally you describe that is applying AI to ``harness the
    power of their proprietary data.'' More specifically, what kind
    of work are you referring to there? Is this an effort to
    improve these clients' customer chatbots or other applications
    of generative AI that we should note?
        Mr. Karunamurthy. Thank you, Congressman. A lot of the
    initial use cases we have seen excitement about are areas where
    you need to synthesize and understand the huge range of data
    and see how that data is evolving over time, for example, for
    reviewing legal or compliance documents and identifying redline
    changes or other changes and the impact those can have, and
    then helping human operators understand what is happening in
    that data.
        We also see a range of use cases where these ALMs can
    generate code and that code can go and discover new insights
    out of the data automatically, and so assisting data analysts
    in doing their job more efficiently. We have always seen that
    as an assistive technology that helps data analysts, wealth
    advisors, other job functions do their job better rather than
    replacing jobs at this point.
        Mr. Flood. Thank you, Mr. Karunamurthy. With that, I yield
    back.
        Mr. Lawler. The gentleman yields back. I now recognize
    myself for 5 minutes.
        Mr. Linda, purchasing a home is probably the most
    significant investment the vast majority of Americans will ever
    make. Realtors play a vital role overseeing and facilitating
    this process, especially for first-time homebuyers. Do you
    believe that humans will continue to retain an important role
    within the entire transaction, or do you see AI playing a
    larger role as we move forward?
        Mr. Linda. Thank you for the important question. I shared
    in my opening remarks that from our research, we surveyed
    buyers, and they shared with us that over half of them actually
    cried during the process. That is how emotional and distraught
    and difficult the process is, and that is why we see the human
    as the centerpiece of the whole entire transaction. We are
    actually trying to use AI to enable the humans to spend more
    time together, enable the agent to spend more time touring
    homes in-person with a customer as opposed to, again, doing
    mundane repetitive tasks. So we truly believe that the human
    aspect is a key to real estate transaction, and we see AI as an
    enabler of humans spending more time together as opposed to
    less or replacing anyone.
        Mr. Lawler. Now, we understand that AI can process vast
    amounts of data, including property records, sales history,
    economic indicators, and market trends within the real estate
    market. Using AI to process and analyze this information can
    help identify patterns and correlations. Do you believe that
    this means that AI will make buying a home a reality for more
    people within the market?
        Mr. Linda. Absolutely, by increasing transparency. As an
    example, the Zestimate empowers more people with information,
    by empowering people to find homes on the market more easily
    through things like natural language. These are some of the
    strategies that Zillow has been deploying to really enable more
    people to find a home, make more informed decision, and, as I
    mentioned already, spend more time with the agent in person so
    they can get the advantage in the market and actually end up
    with purchasing a home.
        Mr. Lawler. So to that end, how is Zillow ensuring that AI-
    powered tools are developed and deployed responsibly to make
    searching for renting, buying, or financing a home more
    equitable and transparent?
        Mr. Linda. Thank you for your question. We have several
    levels of things that we do. I will start by really putting
    fair housing and the existing regulation at the forefront of
    what we do, and kind of infusing it with what is needed to
    deploy AI responsibly. We started doing that by putting
    publicly our principles and then following them in what we do.
        We have a cross-functional working group at Zillow that
    proactively intakes algorithms early on as the ideas come up.
    We look at them from the lens of fair housing and responsible
    AI. We look for inputs that we should not be using. We think
    about the impact on the customer, define fairness metrics. We
    go through the audit. Every time a major new update to
    algorithm happens, we do it again because every time algorithm
    changes, algorithm that was fair yesterday might be not be fair
    tomorrow. So this is a repetitive process, and we also come to,
    you know, places like this to share our experience, and we
    continue to work as we go forward.
        Mr. Lawler. Mr. Zecca, according to your testimony, an
    estimated $3.1 trillion in illicit funds flowed through the
    global financial system in 2023, and last year alone, losses
    from fraud totaled $485.6 billion globally. How has Nasdaq used
    machine learning to address illicit activity?
        Mr. Zecca. So our Verafin product provides AFC surveillance
    software for more than 2,000 institutions, so all the way from
    credit unions to tier 1 banks. In that context, what they are
    trying to do is put together the various pieces to develop
    cases, and some of the data will be within their own systems.
    Some, frankly, will be on the internet when you are trying to
    figure out whether they have a connection with another
    individual.
        AI is a game changer in trying to connect those dots and
    having the compliance departments, who are stretched with a lot
    of different responsibilities, have them focus on the most
    likely cases and avoid the false positives. So we are seeing a
    significant increase in efficiency, and I think over time, that
    will even increase.
        Mr. Lawler. As is often the case when new technologies
    emerge, criminals are also using them----
        Mr. Zecca. True.
        Mr. Lawler [continuing]. to develop new and more
    sophisticated financial crime strategies. So from that end of
    it, what is Nasdaq doing to out innovate the bad actors, if you
    will?
        Mr. Zecca. Well, we are working with our clients on their
    use cases. I do think your question raises two important
    points. One is I think most institutions would say we would
    like a stronger feedback loop with the government. When we do
    our suspicious activity reports, which ones are the most
    useful? While there is feedback, I think that is an area where
    people would want more. The second is clarity on the
    expectations of the regulatory requirements. You know, if you
    are fighting crime, the level of explainability that you may be
    able to accomplish maybe that is less important than trying to
    stay up with the criminal. So I think that is another piece
    that regulators will need to weigh in on.
        Mr. Lawler. Thank you. I would like to thank our witnesses
    for their testimony today and for your patience this morning.
        Without objection, all members will have 5 legislative days
    within which to submit additional written questions for the
    witnesses to the chair. I will forward those questions to the
    witnesses for their response. I ask our witnesses to please
    respond as promptly as you can.

        [The information referred to can be found in the appendix.]

        Mr. Lawler. With that, this hearing is adjourned.

        [Whereupon, at 2:30 p.m., the committee was adjourned.]

                                A P P E N D I X

                                 July 23, 2024

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