    [House Hearing, 119 Congress]
    [From the U.S. Government Publishing Office]

                     FROM PRINCIPLES TO POLICY: ENABLING 21ST
                   CENTURY AI INNOVATION IN FINANCIAL SERVICES

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

                                    HEARING

                                   before the

                        COMMITTEE ON FINANCIAL SERVICES

                         U.S. HOUSE OF REPRESENTATIVES

                        ONE HUNDRED NINETEENTH CONGRESS

                                 FIRST SESSION
                                   __________

                               DECEMBER 10, 2025
                                   __________

                               Serial No. 119-48

           Printed for the use of the Committee on Financial Services

                    [GRAPHIC NOT AVAILABLE IN TIFF FORMAT]

                                www.govinfo.gov

                                   ______

                     U.S. GOVERNMENT PUBLISHING OFFICE

    63-576 PDF                WASHINGTON : 2026

                     HOUSE COMMITTEE ON FINANCIAL SERVICES

                        FRENCH HILL, Arkansas, Chairman

    BILL HUIZENGA, Michigan, Vice        MAXINE WATERS, California, Ranking
        Chairman                             Member
    FRANK D. LUCAS, Oklahoma             SYLVIA R. GARCIA, Texas, Vice
    PETE SESSIONS, Texas                     Ranking Member
    ANN WAGNER, Missouri                 NYDIA M. VELAZQUEZ, New York
    ANDY BARR, Kentucky                  BRAD SHERMAN, California
    ROGER WILLIAMS, Texas                GREGORY W. MEEKS, New York
    TOM EMMER, Minnesota                 DAVID SCOTT, Georgia
    BARRY LOUDERMILK, Georgia            STEPHEN F. LYNCH, Massachusetts
    WARREN DAVIDSON, Ohio                AL GREEN, Texas
    JOHN W. ROSE, Tennessee              EMANUEL CLEAVER, Missouri
    BRYAN STEIL, Wisconsin               JAMES A. HIMES, Connecticut
    WILLIAM R. TIMMONS, IV, South        BILL FOSTER, Illinois
        Carolina                         JOYCE BEATTY, Ohio
    MARLIN STUTZMAN, Indiana             JUAN VARGAS, California
    RALPH NORMAN, South Carolina         JOSH GOTTHEIMER, New Jersey
    DANIEL MEUSER, Pennsylvania          VICENTE GONZALEZ, Texas
    YOUNG KIM, California                SEAN CASTEN, Illinois
    BYRON DONALDS, Florida               AYANNA PRESSLEY, Massachusetts
    ANDREW R. GARBARINO, New York        RASHIDA TLAIB, Michigan
    SCOTT FITZGERALD, Wisconsin          RITCHIE TORRES, New York
    MIKE FLOOD, Nebraska                 NIKEMA WILLIAMS, Georgia
    MICHAEL LAWLER, New York             BRITTANY PETTERSEN, Colorado
    MONICA DE LA CRUZ, Texas             CLEO FIELDS, Louisiana
    ANDREW OGLES, Tennessee              JANELLE BYNUM, Oregon
    ZACHARY NUNN, Iowa                   SAM LICCARDO, California
    LISA McCLAIN, Michigan
    MARIA SALAZAR, Florida
    TROY DOWNING, Montana
    MIKE HARIDOPOLOS, Florida
    TIM MOORE, North Carolina

                          Ben Johnson, Staff Director

                             C  O  N  T  E  N  T  S

                                  ----------

                          Wednesday, December 10, 2025

                               OPENING STATEMENTS

                                                                       Page
    Hon. French Hill, Chairman of the Committee on Financial
      Services, a U.S. Representative from Arkansas..................     1
    Hon. Maxine Waters, Ranking Member of the Committee on Financial
      Services, a U.S. Representative from California................     2

                                   STATEMENTS

    Hon. Bryan Steil, Chairman of the Subcommittee on Digital Assets,
      Financial Technology and Inclusion, a U.S. Representative from
      Wisconsin......................................................     3
    Hon. Stephen Lynch, Ranking Member of the Subcommittee on Digital
      Assets, Financial Technology and Inclusion, a U.S.
      Representative from Massachusetts..............................     4

                                   WITNESSES

    Ms. Jeanette Manfra, Vice President and Global Head of Risk &
      Compliance, Google Cloud.......................................     5
        Prepared Statement...........................................     7
    Mr. Tal Cohen, President, Nasdaq.................................    14
        Prepared Statement...........................................    16
    Mr. Nicholas Stevens, Vice President of Product, Artificial
      Intelligence, Zillow...........................................    23
        Prepared Statement...........................................    25
    Ms. Wendi Whitmore, Chief Security Intelligence Officer, Palo
      Alto Networks..................................................    31
        Prepared Statement...........................................    33
    Mr. Joshua Branch, Big Tech Accountability Advocate, Public
      Citizen........................................................    41
        Prepared Statement...........................................    43

                                    APPENDIX

                       MATERIALS SUBMITTED FOR THE RECORD

    Stephen F. Lynch:
        FinTech Regulatory Sandbox Guidelines FinTech Regulatory
          Sandbox Guidelines.........................................   126
    Hon. Gregory W. Meeks:
        U.S. Halted Plans to Sanction Chinese Spy Agency to Maintain
          Trade Truce, FT Says.......................................   146
    Hon. Maxine Waters:
        Coalition Letter Opposing H.R. 4801..........................   148
        Consumer Reports Opposing H.R. 4801..........................   151

                     RESPONSES TO QUESTIONS FOR THE RECORD

    Written responses to questions for the record from Representative
      Barry Loudermilk
        Ms. Jeanette Manfra..........................................   155

                                  LEGISLATION

    H.R. 4801, the Unleashing AI Innovation in Financial Services Act   157
    H.R. 2152, the Artificial Intelligence Practices, Logistics,
      Actions, and Necessities (PLAN) Act............................   178
    H.R. 1734, the Preventing Deep Fake Scams Act....................   183
    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..   188
    H.R. ------, the Artificial Intelligence Innovation Sandbox
      Information Generation and Harmonization of Testing Act (AI
      INSIGHT) Act...................................................   192
    H.R. ------, the Fostering the Use of Technology to Uphold
      Regulatory Effectiveness in Supervision (FUTURES) Act..........   198

                    FROM PRINCIPLES TO POLICY: ENABLING 21ST
                   CENTURY AI INNOVATION IN FINANCIAL SERVICES

                                  ----------

                          Wednesday, December 10, 2025

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

        The committee met, pursuant to notice, at 10:12 a.m., in
    room 2128, Rayburn House Office Building, Hon. J. French Hill
    [chairman of the committee] presiding.
        Present: Representatives Hill, Lucas, Sessions, Huizenga,
    Wagner, Barr, Williams of Texas, Loudermilk, Davidson, Rose,
    Steil, Timmons, Stutzman, Meuser, Kim, Garbarino, Fitzgerald,
    Flood, De La Cruz, Ogles, Nunn, McClain, Salazar, Downing,
    Haridopolos, Moore, Waters, Sherman, Meeks, Scott, Lynch,
    Green, Cleaver, Himes, Foster, Beatty, Vargas, Gottheimer,
    Gonzalez, Casten, Pressley, Tlaib, Torres, Garcia, Williams of
    Georgia, Pettersen, Fields, Bynum, and Liccardo.
        Chairman Hill. The Committee on Financial Services will
    come to order. Without objection, the chair is authorized to
    declare a recess at any time.
        Today's hearing is entitled From Principles to Policy:
    Enabling 21st Century AI Innovation in Financial Services.
        Without objection, all members will have 5 legislative days
    within which to submit extraneous materials to the chair for
    inclusion in the record.
        I now recognize myself for 4 minutes for an opening
    statement.

        OPENING STATEMENT OF HON. FRENCH HILL, CHAIRMAN OF THE
      COMMITTEE ON FINANCIAL SERVICES, A U.S. REPRESENTATIVE FROM
                                ARKANSAS

        Advancements in artificial intelligence are not just on the
    horizon; they are here, and they are transforming our economy.
    Last Congress, Congressman Bill Foster and I served on Speaker
    Johnson and Minority Leader Jeffries' bipartisan congressional
    AI task force, and Congressman Steve Lynch and I led this
    committee's AI working group. In both forums, we examined how
    financial services firms and regulators are approaching
    artificial intelligence, analyzing its benefits and risks in a
    highly regulated environment.
        Just this past fall, the Digital Assets, Financial
    Technology, and Artificial Intelligence Subcommittee, led by
    Chairman Bryan Steil, held a hearing where members evaluated
    how financial regulators and firms are using AI.
        Today's hearing builds on our earlier work in last Congress
    and this Congress and will help us further examine AI use cases
    in the financial and housing industries. We will also consider
    how agencies are providing clear regulatory environment and
    assess where existing laws may fall short or require
    modernization and explore ways to foster innovation.
        For decades, the financial services industry has pioneered
    the real-world application of AI and continues to be a standout
    leader. AI has shown its transformative potential to reshape
    how financial institutions operate from enhancing analysis, to
    managing risk, mitigating fraud, and, importantly, enhancing
    customer service.
        However, as with any innovation, risks give rise to new
    challenges. Yet human progress is ever changing, never static,
    and AI represents the latest leg in this journey. To move
    forward, we must embrace and adapt for innovation.
        This was the approach taken in the 1990s by former
    Congressman and former Securities and Exchange Commission (SEC)
    Chairman, Chris Cox, when Congress confronted the rapid
    commercialization of the internet. Rather than allowing fear to
    stall advancement, lawmakers adopted existing laws to fit the
    new technological era.
        Congress established clear guidelines and principles early
    on, setting the stage for the rapid growth of the internet,
    enabling countless innovations and entire industries, and
    putting the United States at the forefront of that innovation
    and the internet's expansion.
        Just as Congress navigated the uncertainties of the
    internet in the mid-1990s, we must use AI as an opportunity,
    not a threat, applying lessons of that era in order to confront
    today's technological landscape.
        Identifying gaps and obstacles in our regulatory frameworks
    will help Congress create an AI landscape where innovation can
    flourish without unnecessary barriers while ensuring robust
    consumer protections and risk-based technological--technology-
    neutral regulations.
        The committee is steadfast in its commitment to empowering
    U.S. firms to leverage AI's potential and drive the U.S. global
    leadership and adoption and innovation.
        I thank all of our witnesses for joining us today and
    providing your valuable insights and perspectives, and I yield
    back.
        It is now my pleasure to recognize the ranking member of
    the committee, Ms. Waters, for 4 minutes for an opening
    statement.

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

        Ms. Waters. Thank you very much, Mr. Chairman.
        AI is already embedding in the lives of millions of
    Americans, and we have a duty to ensure that it benefits
    society, not harms it. Unfortunately, Republicans have been
    complicit as Donald Trump bypasses Congress, directs Federal
    agencies to deploy biased AI with no safeguards, and now
    issuing an executive order that seeks to undermine all State
    oversight.
        Republicans in Congress fail to acknowledge that despite
    all the benefits that AI may bring, it also poses challenges
    that demand our full attention. We are already seeing AI
    systems that hurt children, spread hate and discrimination, and
    amplify systemic risk in the financial system. As unemployment
    reaches new heights, reports continue to show AI replacing
    workers across industries. That is why the Biden Administration
    put in critical protections to ensure AI systems are
    transparent, explainable, and nondiscriminatory.
        These are just basic safeguards to keep families and
    children safe. In some of the worst cases, chat boxes have
    encouraged self-harm, like suicide, carrying out sexually
    explicit conversations, and even provided dangerous
    misinformation and disinformation to minors but no one should
    be surprised that the same President who calls affordability a
    hoax is ignoring these very real concerns with AI. Since--twice
    this year, the White House and congressional Republicans have
    tried and failed to impose a moratorium on State AI laws, and
    now, despite those failures, Trump announced he would sign an
    executive order that would block States from adopting their own
    AI safeguards by threatening legal challenges and withholding
    Federal funding.
        For this hearing, Republicans posed a bill--posted a bill
    that would give AI and its Big Tech creators a pass when they
    violate consumer, housing, banking, or securities laws.
    Democrats are not going to sit by and watch Trump and
    Republicans allow Big Tech to forcibly experiment on our
    communities. Any legislative efforts by Congress must ensure
    that consumers and investors understand the AI they are
    interacting with in the financial services space, while giving
    companies meaningful standards they can innovate under.
        A partisan approach is not necessary if Republicans fight
    for their constituencies. Last Congress, I and then-Chair
    McHenry launched the first ever bipartisan AI task force. We
    held joint sessions, conducted oversight, and introduced bills
    to confront AI bias, deep fake scams, and discrimination,
    proving that innovation and oversight can go hand in hand.
    Democrats are leading these efforts because we believe in
    responsible innovation.
        The stakes are too high, the risks are too great, and the
    consequences are too real to hand this technology over to Big
    Tech billionaires with no guardrails, no accountability.
        Thank you, and I yield back the balance of my time.
        Chairman Hill. The gentlewoman yields back.
        I now recognize the chair of the Subcommittee on Digital
    Assets, Financial Technology, and Artificial Intelligence, Mr.
    Steil of Wisconsin, for 1 minute for an opening statement.

     STATMENT OF HON. BRYAN STEIL, CHAIRMAN OF THE SUBCOMMITTEE ON
      DIGITAL ASSETS, FINANCIAL TECHNOLOGY AND INCLUSION, A U.S.
                     REPRESENTATIVE FROM WISCONSIN

        Mr. Steil. Thank you very much, Mr. Chairman.
        The U.S. must win the global AI race and the regulations
    that we may make here are incredibly consequential. We cannot
    get it wrong; some have. For example, the EU AI Act is a broad,
    horizontal approach that stifles innovation in Europe. In the
    U.S., I believe we should take a targeted, activity-specific
    approach to AI. As we examine use cases and those who develop
    and deploy AI today, we should examine if our rules need
    adjustments and tweaks to get this right. These use cases will
    only continue to evolve as generative and agentic AI
    technologies evolve. We must make sure we unlock innovation
    while protecting Americans. As the United States strives to win
    the AI global race, maintaining a ``try first'' approach will
    help preserve American dominance and agility. Let us win this
    race. I yield back.
        Chairman Hill. The gentleman yields back.
        I recognize the ranking member of the Subcommittee on
    Digital Assets, Financial Technology, and AI, Mr. Lynch, for 1
    minute for an opening statement.

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

        Mr. Lynch. Good morning. Thank you, Mr. Chairman and
    Ranking Member Waters, for holding this hearing to continue
    examining AI innovation and regulation in the financial
    services sector. I also want to thank our panel of witnesses
    for helping the committee with its work.
        Last Congress, I was proud to co-lead our bipartisan AI
    working group with my friend, Chairman Hill, to examine the
    benefits and risks of deploying AI technologies in banking,
    housing, credit lending, and other areas. These findings should
    continue to guide us as we pursue effective and responsible AI
    regulation. However, as ranking member of the Subcommittee on
    Digital Assets, Financial Technology, and Artificial
    Intelligence in this Congress, I am concerned that some of the
    regulations--regulatory proposals before this committee not
    only fail to include adequate guardrails but they also invite
    the financial services industry in adopting AI to choose which
    consumer protection and investor protection and safety and
    soundness regulations that they would like to avoid, which is
    reckless and dangerous.
        In closing, I look forward to working with my colleagues to
    develop bipartisan AI legislation that promotes innovation
    while ensuring robust consumer and financial protection. I
    yield back the balance of my time.
        Chairman Hill. The gentleman yields back.
        Today we welcome the testimony of Jeanette Manfra, vice
    president, global head of risk and compliance at Google Cloud;
    Tal Cohen, president of Nasdaq; Nicholas Stevens, the vice
    president of product and senior developer of AI and engineering
    at Zillow; Wendi Whitmore, chief security intelligence officer
    at Palo Alto Networks; and Joshua Branch, Big Tech
    accountability advocate at Public Citizen.
        We thank each of you for taking time to be with us. Each of
    you will be recognized for 5 minutes to give an oral
    presentation of your testimony. Without objection, your written
    statements will be made part of our record.
        Chairman Hill. Mrs. Manfra, we will start with you. You are
    recognized for 5 minutes for your oral remarks.

    STATEMENT OF JEANETTE MANFRA, VICE PRESIDENT AND GLOBAL HEAD OF
                    RISK & COMPLIANCE, GOOGLE CLOUD

        Ms. Manfra. Chairman Hill, Ranking Member Waters, and
    distinguished members of the committee, thank you for the
    opportunity to appear before you today. My name is Jeanette
    Manfra, and I am the vice president of risk and compliance at
    Google Cloud. We appreciate the House Committee on Financial
    Services holding this important hearing, and we look forward to
    sharing Google's perspective on the opportunity that artificial
    intelligence provides to America's financial sector.
        Google believes that the introduction of AI in the
    financial services sector promises to usher in a transformative
    era for quality, accessibility, efficiency, and compliance in
    financial markets and services. AI offers many benefits,
    including the potential to enhance individual productivity,
    strengthen security operations, and drive database
    decisionmaking and operational efficiencies. These improvements
    will benefit institutions of all sizes, including small-and
    medium-sized financial entities.
        At the same time, we also recognize that AI introduces
    risks that must be managed and mitigated. We believe that
    existing risk management frameworks and established governance
    practices can be applied to manage risks in the AI context.
        For more than a decade, Google has used advancements in AI
    to further protect people from online scams, where malicious
    actors deceive users to gain access to money, personal
    information, or both. We offer a wide variety of protections
    powered by AI, including using Google Cloud's anti-money
    laundering AI service for financial sector customer risk
    scoring, utilizing AI-powered scam detection systems in search,
    an Android ecosystem that automatically identifies and blocks
    phishing messages and scam calls, and our efforts to ensure the
    integrity of Google ads on our platforms.
        Additionally, Google further disincentivizes this malicious
    behavior by proactively filing litigation to dismantle massive
    fraud operations. In a recent example, in November 2025, we
    announced our filing to dismantle Lighthouse, a massive
    phishing-as-a-service operation. Bad actors built Lighthouse as
    a phishing-as-a-service kit to generate and deploy massive
    short message service (SMS) phishing attacks. These attacks
    exploit established brands, like E-ZPass, to steal people's
    financial information. Our legal action is designed to
    dismantle the core infrastructure of this operation.
        We recognize that Google must work with industry
    participants, regulatory bodies, and technology providers to
    counter the critical threats posed by scams and frauds, and we
    are leading cross-industry efforts in combating fraud and scams
    in response.
        We have introduced the Agent Payments Protocol, or AP2, an
    open-source protocol collaboratively developed with over 60
    partners, including financial and technology companies, like
    American Express, Mastercard, and PayPal. AP2 aims to
    standardize and secure transactions made by AI agents on behalf
    of users, addressing key challenges such as authorization,
    authenticity, and accountability. This is an important step,
    and I look forward to discussing this with the committee.
        We at Google have also introduced the Secure AI Framework,
    or SAIF, a conceptual framework for secure AI systems,
    including those in the financial sector, and we have recently
    published an extension of the SAIF Risk Map to address the core
    operational components of agentic systems and their related
    risks and controls. In addition, we partnered with Amazon,
    Microsoft, IBM, Nvidia, and many others to co-find the
    Coalition for Secure AI, which is an open-source initiative to
    help all developers and deployers of AI create and maintain
    secure by designing AI systems and help advance the SAIF
    framework.
        We recognize that we alone cannot solve these challenges.
    We have recently announced our endorsement of key bipartisan
    bills in Congress, crucial bills that we believe will help
    bring a decisive end to the financial harm and damage wrought
    by foreign cyber criminals. As the world focuses on the
    potential of AI, and governments and industry work on a
    regulatory approach to ensure AI is safe and secure, we believe
    that AI represents an inflection point for digital security.
    Regulators should support the development of global standards
    and their use across the financial services and regulatory
    landscape. In addition, regulators should foster industry
    collaboration and training based on such standards.
        In closing, thank you for convening this really important
    hearing. We look forward to continuing to further raise
    awareness about cybersecurity, threats, and defenses for the
    financial sector and beyond.

        [The prepared statement of Ms. Manfra follows:]

    [GRAPHIC(S) NOT AVAILABLE IN TIFF FORMAT]

        Chairman Hill. Thank you very much.
        Mr. Cohen, you are now recognized for 5 minutes for your
    testimony.

               STATEMENT OF TAL COHEN, PRESIDENT, NASDAQ

        Mr. Cohen. Thank you.
        Chairman Hill, Ranking Member Waters, and members of the
    committee, thank you for the opportunity to testify on the
    impact of artificial intelligence on our financial system and
    the role of responsible innovation. My name is Tal Cohen, and I
    serve as president of Nasdaq. At Nasdaq, we aspire to be the
    trusted fabric of the global financial markets, connecting
    entrepreneurs and investors, fueling economic growth, fighting
    financial crime, and supporting millions of everyday savers and
    retirees. As a global technology provider and an operator of
    regulated markets, we have a unique perspective that shapes how
    we employ AI.
        Our approach centers on three core principles: enhancing
    liquidity, ensuring transparency, and protecting integrity. AI
    is already making a measurable difference across all three of
    these within our solutions and I am going to start with
    financial crime, where the human cost is greatest.
        In 2023, an estimated $3.1 trillion in illicit funds flowed
    through the global financial system, and scam and bank frauds
    cost roughly 485 billion in losses worldwide. Behind these
    numbers are family losing savings, communities harmed by drug
    trafficking, human trafficking, and terrorism financing.
        Nasdaq's cloud native AI enabled anti-financial crime
    platform, Verafin, is used by thousands of financial
    institutions to detect and prevent these crimes. Verafin
    analyzes vast data sets across its network to identify
    suspicious activity in ways no single institution could do
    otherwise.
        This work went a step further with the launch of our
    agentic AI workforce. Our digital sanctions analyst has reduced
    the alert review workload for sanction screening by more than
    80 percent, freeing compliance professionals to focus on
    complex, judgment-heavy issues while AI handles routine tasks.
        AI also strengthens market integrity. Our surveillance
    platform uses machine learning to help exchanges and regulators
    detect insider trading across billions of daily transactions,
    rooting out market abuse that erodes investor confidence.
        These achievements are only possible if you have the right
    governance and the right oversight in place. From the outset,
    Nasdaq established an enterprise-wide AI governance program
    aligned with the National Institute of Standards and Technology
    (NIST) AI risk management framework, overseen by an executive
    steering committee that includes our CEO. We also implemented a
    cross-functional governance committee led by legal, risk,
    regulatory, and technology teams.
        Additionally, we adopted responsible AI principles covering
    transparency, fairness, privacy, reliability, and
    accountability, and we applied it across all products and
    internal operations.
        And that brings me to the AI policy consideration before
    this committee. The U.S. has historic opportunity to lead in
    AI--if we strike the right balance. From our perspective, the
    following principles are critical.
        First, leverage existing regulatory frameworks. The
    financial sector is already heavily supervised. Many AI-related
    risks can be addressed with existing rules and mandates.
        Second, focus on use cases and outcomes. Regulation should
    reflect risk profiles. An AI tool detecting drug trafficking or
    protecting seniors from fraud should be subject to higher
    standards than when that same tool is used to approve apartment
    rentals. They carry different risk profiles and require
    different regulatory treatment.
        Third, Keep frameworks flexible and innovation friendly.
    Overly prescriptive rules tend to age poorly and risk pushing
    innovation overseas. We support sandboxes, pilots, and AI
    centers of excellence where regulators and industry can learn
    and work together.
        Fourth, strive for harmonization. A patchwork of State-
    level AI laws creates uncertainty, raises costs, and can limit
    access to these tools. Federal coordination is the best way to
    protect investors while preserving U.S. competitiveness.
        Applying these principles will ensure that AI is working to
    make our financial systems more fair, more efficient, and more
    resilient and that is the future we are working toward at
    Nasdaq and why we are grateful for the committee's leadership
    on these issues.
        In closing, we support the administration's America's AI
    Action Plan and bipartisan effort in Congress to address harms
    such as nonconsensual deep fakes and synthetic media while
    preserving space for innovation. Thank you again for the
    opportunity to testify today, and I look forward to your
    questions.

        [The prepared statement of Mr. Cohen follows:]

    [GRAPHIC(S) NOT AVAILABLE IN TIFF FORMAT]

        Chairman Hill. Thank you, sir.
        Mr. Stevens, you are recognized for 5 minutes for your oral
    presentation.

       STATEMENT OF NICHOLAS STEVENS, VICE PRESIDENT OF PRODUCT,
                    ARTIFICIAL INTELLIGENCE, ZILLOW

        Mr. Stevens. Chairman Hill, Ranking Member Waters, and
    members of the committee, thank you for the opportunity to
    testify today. My name is Nicholas Stevens, and I serve as vice
    president of product, artificial intelligence of Zillow. Our
    mission is to help people navigate one of the most meaningful
    and complex decisions of their lives: finding a home.
        After 9 years at Zillow, improving the housing journey, I
    recently had the chance to live it again. My family just moved.
    At the end of the process, my 5-and 7-year-olds could not
    sleep. They were up at 3 a.m. and when my wife and I sat them
    down to ask what on earth was troubling them, they had two very
    nervous questions. A: ``Are we bringing our cat, Lily, to the
    new home?'' B: ``Are we also bringing the TV?'' I am happy to
    report that both have made it safe to our new home but
    everything in between--the search, the paperwork, the
    financing, the closing--was a reminder of how emotional and
    confusing this process can be. That is the friction we are
    trying to address with responsible AI.
        Zillow has been applying AI to housing for nearly two
    decades, starting with the Zestimate, one of the first large-
    scale consumer uses of machine learning in real estate. It is
    not an appraisal or used for credit decisions. Rather, it
    empowers buyers and sellers with a ballpark estimate of home
    value. The Zestimate was so popular, it crashed our site
    because consumers were asking for information that has
    historically been available only to a select few. Today, 250
    million unique users come to Zillow every month to dream, to
    rent, to tour, finance, buy, and sell their homes and with that
    scale comes real responsibility. Our products cannot just be
    clever. They have to be reliable, fair, and trust-affirming.
        Since Zillow last appeared before this committee, Next
    Generation AI has moved from pilot to production. AI in real
    estate is not theoretical. It powers tools people are using
    today, and I will highlight a few examples.
        We have built an AI to be a true assistant to real estate
    agents nationwide. It helps prioritize an agent's leads. It
    summarizes notes. It drafts emails and one of the agents'
    favorite features is that it suggests key topics to touch on in
    their clients' catch-up call.
        Technology also allowed our company to go fully remote, and
    we now have employees in all 50 States.
        Our latest computer vision experiences allow you to not
    only generate a home's floor plan so potential buyers can
    explore the inside in detail; we also let them fly around and
    get a sense of the exterior and yard.
        The through line is that safeguards are built in, not
    bolted on. Internally, Zillow employees complete fair housing
    and privacy training, and anyone working with AI receives
    additional responsible AI training. Before we launch an AI
    feature, cross-functional teams conduct a model risk review
    focused on fairness, bias, privacy, and explainability. After
    launch, we perform periodic audits and continuous monitoring so
    we can catch issues that only appear at scale.
        Externally, this is even true with public partnerships. Now
    consumers can talk to Zillow directly inside ChatGPT's new in
    app experience, asking, for example, ``Zillow, show me 2-
    bedroom homes that are wheelchair accessible,'' and in return,
    they get listings, photos, and maps from Zillow in a
    conversational flow.
        We built this with a fair housing first design. Our Fair
    Housing Classifier helps detect and prevent potential digital
    steering. We also believe responsible innovation means raising
    the floor for everyone. Zillow open-sourced that same Fair
    Housing Classifier under a permissive license, and it is now in
    use by real estate platforms and researchers working to combat
    housing discrimination. That is a concrete example of
    innovation and compliance rowing in the same direction.
        All of this argues for a right-sized national framework for
    AI in financial services, including housing. A coherent
    national baseline for fairness, transparency, privacy, and
    accountability would give consumers consistent safeguards and
    give companies the confidence to integrate strong protections
    from day 1. The alternative is a patchwork where the same
    consumer gets different quality experiences across State lines
    for the same transaction.
        Even the latest AI technology would not have prompted me to
    explain to my kids that, yes, both our cat and the TV would be
    coming to our next home, but it can help tackle the structural
    challenges we all worry about: affordability, supply, and
    outdated processes.
        As you consider a national framework for AI and
    modernization of our housing finance system, we stand ready to
    share data, technical expertise, and lessons from building
    these systems at scale. We appreciate the committee's
    leadership on this issue, and I look forward to answering your
    questions.

        [The prepared statement of Mr. Stevens follows:]

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        Chairman Hill. Thank you very much.
        Ms. Whitmore, you are now recognized for 5 minutes for your
    oral testimony.

       STATEMENT OF WENDI WHITMORE, CHIEF SECURITY INTELLIGENCE
                      OFFICER, PALO ALTO NETWORKS

        Ms. Whitmore. Good morning.
        Chairman Hill, Ranking Member Waters, and distinguished
    members of the committee, thank you for the opportunity to
    testify. My name is Wendi Whitmore, and I am the chief security
    intelligence officer at Palo Alto Networks. We are an American
    cybersecurity company protecting more than 75,000 organizations
    in over 150 countries, including 97 of the Fortune 100, eight
    of the 10 largest banks, the U.S. Federal Government, and
    critical infrastructure operators.
        The promise of AI for financial services is undeniable and
    realizing it requires the sector to simultaneously embrace AI
    for cybersecurity, and cybersecurity for AI. There is both
    urgency and opportunity for financial institutions to lead. The
    threat landscape is evolving as advanced AI and quantum
    computing reshape both innovation and risk.
        Attacks are faster, more automated, and harder to detect.
    Time from compromise to data exfiltration is now 100 times
    faster than 4 years ago, and attackers are increasingly
    exfiltrating data within 1 hour.
        Generative and agentic AI now super-charge every phase of
    the kill chain, enabling deep fake-driven fraud, know your
    customer evasion via face swapping, and tailored spear
    phishing, imitating trusted financial institutions. Our own
    research shows that agentic AI can compress a multi-day
    ransomware operation into only 25 minutes, from reconnaissance
    to compromise to data theft.
        This reality underscores why the financial sector must do
    two things: one, adopt AI-driven security operations that
    operate at machine speed; and two, harden their AI ecosystems
    with a secure-by-design approach.
        Today's security operation centers, known as SOCs, are
    drowning in fragmented data, and alerts from an average of 83
    security solutions, forcing skilled analysts into inefficient,
    manual triage. Consequently, critical alerts are buried, with
    75 percent of breaches having actionable logging that was never
    reviewed, leaving vulnerabilities exposed and degrading our
    core ability to rapidly detect and respond to threats.
        AI-driven SOCs flip this paradigm, acting as a force
    multiplier for cyber professionals that substantially reduces
    detection and response times. The results of deploying AI in
    our own SOC are transformative. We ingest roughly 90 billion
    events every day. By leveraging AI, we reduce those to only
    26,000 alerts, ultimately resulting in one single incident
    requiring manual investigation.
        In financial institutions deploying AI-driven SOCs, we see
    four times more security data ingested into a consolidated
    platform, thousands of models providing real-time prevention
    and detection, automation cutting analysts' workload by three-
    quarters, and the mean time to respond falling by up to 90
    percent. In one global financial markets utility, their mean
    time to respond fell from 24 hours to 14 minutes. Another large
    bank saved more than 1,000 analyst hours annually while
    improving threat hunting and reducing attrition.
        AI adoption is integral to America's innovation leadership,
    which is why Palo Alto Networks was proud to support America's
    AI Action Plan, which recognize that the benefits of AI will
    stall if we do not secure AI itself.
        Attacks against AI systems have fundamentally evolved
    beyond traditional cybersecurity considerations. They target
    how systems learn and how they reason. The answer to accelerate
    AI innovation is to embed security throughout the AI life cycle
    so that we can adopt AI tools confidently.
        Secure AI by design provides the blueprint to integrate
    security from development through deployment and use, ensuring
    visibility, control, and protection at enterprise scale.
    Practically, that means discovering and governing external AI
    tools, including Shadow AI, securing AI infrastructure and
    data, monitoring and controlling AI agents, and safely building
    and deploying AI applications.
        Building an AI-ready ecosystem is a team effort. As a
    Financial Services Industry (FSI) sector advisor and a
    participant in the Cyber Risk Institute innovator program, Palo
    Alto Networks helps align best practices with leading standards
    and reduce compliance friction for financial institutions. If
    we secure the future now through AI-driven defense and secure
    AI by design, we can unleash the full potential of AI
    innovation while protecting consumers, markets, and national
    security.
        Thank you for the opportunity to testify. I look forward to
    your questions.

        [The prepared statement of Wendi Whitmore follows:]

    [GRAPHIC(S) NOT AVAILABLE IN TIFF FORMAT]

        Chairman Hill. Thank you very much.
        Mr. Branch, you are now recognized for 5 minutes for an
    oral presentation of your testimony.

     STATEMENT OF JOSHUA BRANCH, BIG TECH ACCOUNTABILITY ADVOCATE,
                             PUBLIC CITIZEN

        Mr. Branch. Thank you, Chairman Hill, Ranking Member
    Waters, and members of the committee. My name is J.B. Branch. I
    am an artificial intelligence policy expert at Public Citizen,
    a nonprofit with more than 1 million members and supporters
    across the country.
        Each day, we fight for everyday Americans by defending
    democracy, resisting corruption, and challenging corporate
    greed. I am here today to talk about the difference between
    responsible AI innovation, and the current reckless push by Big
    Tech to undermine State laws, weaken consumer protection, and
    place all of us at the mercy of a handful of AI billionaires.
        For decades, the default posture toward regulating new
    technology has been deference to industry. Congress deferred to
    the expertise of social media companies. We now live with the
    consequences, including rampant misinformation and harms for
    children. We cannot make the same mistake with AI.
        The scale, speed, and autonomy of AI creates risks far
    beyond previous technology. The good news is that most AI
    regulations are rooted in common sense. Algorithms should not
    discriminate. Companies should be held accountable for harm.
    Nonconsensual deep fake pornography is a devastating crime.
    These are American values and Americans agree. A Gallup poll
    shows 97 percent of Americans agree that AI should be subject
    to regulation--97 percent. States across the country have
    responded with bipartisan, commonsense safeguards that reflect
    this democratic will in action.
        Yet, instead of respecting that will, we have seen
    relentless attempts to override it. Over summer, some in
    Congress attempted to strip all State AI laws. Even worse, they
    tried to slip the proposals into the must-pass National Defense
    Authorization Act. Big Tech tried to use our national security
    as leverage to avoid accountability.
        What was once called a moratorium is now being repackaged
    as a sandbox. These are all the same: deregulation schemes
    designed to invalidate all existing safeguards and the
    hypocrisy is striking. Big Tech is attacking many of the State-
    based AI laws they helped to write. They praised those laws
    locally while lobbying to destroy them nationally.
        I am equally alarmed by President Trump referring to basic
    protections as dangerous ideology. Several of the
    administration's policy priorities target civil rights
    principles in AI but fairness in AI simply means preventing
    discrimination, sexism, and antisemitism.
        While the Trump Administration bashes equality as a
    dangerous ideology, it has simultaneously entered a Federal
    contract with Elon Musk's Grok, an AI system that Office of
    Science and Technology Policy (OSTP) Director Kratsios
    testified, violated the administration's own principles. Grok's
    behavior is well documented: racist slurs, sexism, and even
    referring to itself as Mecca Hitler. Is this the type of AI we
    want deployed throughout the U.S. Federal Government?
        I also want to speak about the impact AI will have on
    workers and rural communities like the one I was raised in
    central Pennsylvania. These communities know what broken
    promises feel like. We remember when steel mills closed and new
    jobs did not come. Now, Big Tech companies are telling us that
    data centers and AI will be salvation, and we have heard this
    before. So let me be clear: Data centers are at the bottom of
    the tech stack. They receive major tax breaks while local
    communities foot the bill and most employ fewer people than a
    single Wal-Mart. They do not replace the jobs that communities
    have lost. This is another extraction model.
        I want to offer simple guiding principles. Responsible
    innovation requires enforceable accountability. Public Citizen
    respectfully submits that the better path forward is not a
    mystery. Reject blanket preemption and deregulatory sandboxes,
    allowing States to respond to evolving harm. Require
    transparency. Companies must be able to explain how their AI
    systems work and how decisions are made. When AI systems cause
    harm, companies must be held accountable. Invest in regulatory
    enforcement. Laws mean nothing if they cannot be enforced.
    Congress must ensure regulators have the staff, the authority,
    and the resources to do their job.
        We all share the same goal: a strong middle class and
    leadership in responsible AI. Leadership means protecting
    workers, children, and democracy at the same time we foster
    innovation.
        This is a defining moment. It is a test of who governs
    America, the people or the most powerful corporations on earth.
    Public Citizen urges Congress to stand up to Big Tech, reject
    backdoor deregulation, and deliver real, enforceable
    guardrails. Thank you, and I look forward to answering your
    questions.

        [The prepared statement of Mr. Branch follows:]

    [GRAPHIC(S) NOT AVAILABLE IN TIFF FORMAT]

        Chairman Hill. I thank our panel very much.
        I now recognize our members for questioning, and I
    recognize myself for 5 minutes.
        The Trump Administration's AI Action Plan prioritized the
    acceleration of AI adoption in government and outlined several
    policy actions that agencies can take to further U.S.
    government's use of AI to, as it says in his plan, ``deliver
    the highly responsive government American people expect and
    deserve.'' I am pleased to see this mentality at the highest
    levels of government.
        Ms. Manfra, how does the AI Action Plan align with industry
    best practices and the recent advancements in AI, and what can
    the government learn from market participants to close the AI
    adoption gap, and promote responsible use by our government
    agencies? Emphasis on ``responsible.''
        Ms. Manfra. Thank you for the question, sir.
        The administration's approach to ensuring that AI and other
    technologies are used to better deliver services to the
    American people is very welcome and combined with that, the
    approach to ensuring that responsible use with safeguards, and
    the experience that agencies are having in implementing their
    own AI governance has also been welcome. So being thoughtful
    about how these AI tools are being used in different risk
    scenarios is something that we see every day with our customers
    and partners in the government and we welcome that.
        We also appreciate the approach of experimentation, and
    being able to create spaces where an agency or an organization
    can explore a use case for AI and work out responsibly what
    those issues might be before----
        Chairman Hill. I think that is a--I think that is an
    important point because over the years, we have had bipartisan
    support for regulatory sandboxes for a variety of things, block
    chain and other aspects of it. That is something that
    technology providers and banks have tried to work out. It is
    important in financial technology (fintech) partnerships. It is
    not deregulatory per se if one has a sandbox, and in an offline
    capacity tries to perfect the use, in this case, of AI and then
    the responsible compliance procedures for it. Is that not a
    fair way to describe it?
        Ms. Manfra. Yes, sir.
        Chairman Hill. You do not consider that, per se,
    deregulation, do you?
        Ms. Manfra. I do not, sir. I think it allows organizations
    to have a safe space where they can experiment and work out any
    potential issues before they would then----
        Chairman Hill. Yes----
        Ms. Manfra [continuing]. employ it.
        Chairman Hill. I kind of share that--I share that view as a
    general statement, subject to the details, obviously.
        While machine learning is not new, Generative AI and
    agentic AI have recently been paradigm shifts for this
    technology, particularly in the private sector. Such tools may
    hold enormous potential for our financial institutions and our
    financial regulators to change how they work.
        Mr. Cohen, let me turn to you. At Nasdaq, you moved trading
    markets to the cloud. You were one of the first exchanges to
    release an AI-powered order type. Can you describe the
    efficiency gains that you achieved through this transition and
    what lessons you think that taught Nasdaq as you look at other
    AI adaptations?
        Mr. Cohen. Thank you for the question.
        In terms of the order type you are referring to, it was--it
    is called Dynamic Midpoint Extended Life Order (M-ELO). It is
    the first SEC AI-enabled order type, and the SEC had to get
    comfortable with exactly how we designed it, the explainability
    of it, how we are testing it, and then how we are deploying it
    into the market.
        We designed it for large institutions--the Fidelity's, the
    Wellingtons of the world--so that they can execute inner
    markets with confidence and get the execution quality they need
    as they are serving, we tell investors, everyday investors sit
    behind the money that they put to work every day at our
    markets.
        So we wanted to democratize good execution quality, and we
    did it through this order type, through, initially, a static
    delay mechanism that allowed them to void fast money in the
    market, which meant that at the end of the day, they could look
    at their executions and the price movement after they executed
    and see that there was quality in those executions.
        What we did with AI is we incorporated 140 data points
    every 30 seconds to determine what that delay should be to
    optimize the execution quality that those large institutions
    get.
        Chairman Hill. Appreciate that. My time is short, so Ms.
    Whitmore, if I could ask you to respond in writing--of course,
    good people and bad people can use AI, so countering threats, I
    think, is very important, and I think cyber risks are a big
    challenge for the private sector and the government sector. Can
    you follow up in writing with me and talk about how we should
    counter those cyber risks to counter bad actors in this space
    using AI? Thank you.
        Ms. Whitmore. Yes, sir.
        Chairman Hill. Thank you very much. If you do that in
    writing, please.
        I recognize the gentleman from Georgia, the distinguished
    Mr. Scott, for 5 minutes.
        Mr. Scott. Thank you very much, Chairman.
        You know, concerning this AI business, I am very fearful
    that we are rapidly becoming servants of the machine that we
    created to serve us. I want that thought to register with you
    and that is why I am working on a bill to expand wage insurance
    protections, guaranteeing temporary wage replacement for
    workers who are forced into lower paying jobs by AI. This is
    dramatically important.
        Mr. Branch, let me ask you. You said something very
    important in your statement. You said in your statement, and I
    quote, Workforce policy built on aspiration and retraining
    slogans is not a real A-1 workforce policy.
        So let me ask you a couple of questions. Have you talked
    with our labor unions about this? What are they saying, if you
    have or have not?
        Mr. Branch. Thank you for the question, Representative.
        Yes, we have spoken with union representatives, and they
    are concerned about the potential for replacement and lost
    jobs. The problem with some of these reskilling programs is
    that oftentimes the jobs are not available in the communities
    where the jobs are lost. So you lose a job in rural America,
    but the job is actually based in Silicon Valley that you are
    being retrained for. A lot of times, the reskilling jobs do not
    even come at the rate of the jobs that end up being lost. So
    you end up having a variety of folks who lose jobs but then
    cannot be employed afterwards.
        Mr. Scott. Spell out for us, how can unregulated A-1 jobs
    lead to widespread job displacement?
        Mr. Branch. Well, thank you again. Currently, a variety of
    tech companies are really moving forward with trying to replace
    a variety of their employers--employees. We are seeing this in
    the tech force right now where you have whole slews of coders
    who are losing their jobs. So that is why it is important for
    Congress to address this issue head-on to work with unions to
    ensure that when these jobs are lost, that there are actually
    going to be jobs available for these folks.
        Mr. Scott. That is why I keep saying we are rapidly
    becoming servants of this machine we created to serve us and I
    hope, Chairman Hill, that this very timely hearing will wake
    our Nation up to realize. I am working on a bill, as I
    mentioned, to expand wage insurance protection, guaranteeing
    that our workers are taken care of here.
        Let me ask you one more question, Mr. Branch. The
    unemployment rate for recent college graduates has risen
    recently. Why is it important to modernize workforce
    protections to reflect today's AI driven economy?
        Mr. Branch. Thank you again for the question----
        Mr. Scott. Because the AI is driving it, man.
        Mr. Branch. Thank you again for the question.
        Again, to sort of boost efficiencies that some of these
    companies are talking about, they are really targeting lower-
    level employees and lower-level jobs and so you are seeing
    waves of college graduates who do not have those lower-level
    jobs available for them. They are oftentimes requiring
    additional job experience that they might not have because they
    have not had the opportunity because efficiencies have made
    those jobs essentially redundant.
        So it is important to ensure that there are opportunities
    for these young kids to get into because if you are removing
    those jobs, you are essentially removing the ladders to success
    that the middle class was built on.
        Mr. Scott. In my last 7 seconds we got to guarantee that
    our workforce is protected with this advance of AI. Mr.
    Chairman, thank you.
        Chairman Hill. I thank the gentleman from Georgia.
        I recognize the vice chairman of our full committee, the
    gentleman from Michigan, Mr. Huizenga. You are recognized for 5
    minutes.
        Mr. Huizenga. Thank you, Mr. Chairman.
        It is interesting, the conversation that we are having
    here. Some seem to be suggesting we should not innovate because
    of potential job loss. I cannot imagine where America would be
    today if that was our guiding principle. We also seem to be
    having some folks who lump in AI generally into one big
    category. It seems to me an AI system dispensing ``life
    advice'' about whether I should take this job or, more
    tragically, and is a huge problem, where we have seen systems
    tell kids to go do things to themselves that they should not--
    it should not even come across their screen but that is very
    different than an application to seek facts. What was the gross
    domestic product (GDP) of the United States in 1904 is a fact
    and because of AI and because of some of these systems, we are
    able to--we are able to sort through those things but both of
    those are very different than an application to introduce
    efficiency into a simple process, or even a complex process. We
    have got two great examples here with Nasdaq and Zillow, and
    others.
        I am going to start there. Someone suggested that there are
    not the same protections against discrimination with the
    application of AI to various transactions. Mr. Cohen and Mr.
    Stevens, I would like you both to quickly address this. What
    have you done to ensure that does not happen in your respective
    industries? Housing and investing are two of those areas that
    we talk about often here in the committee. Is there any
    evidence that what your efforts have done is not working, and
    is not mitigating that concern, and have you had to make any
    adjustments? Mr. Cohen.
        Mr. Cohen. Thank you for the question. So we do not serve
    retail directly. We serve institutions. We serve financial
    markets----
        Mr. Huizenga. Sure.
        Mr. Cohen. The way that we think about it and the way that
    we employ it into our solutions, whether it is our anti-
    financial crime solution, our surveillance solution, or
    regulatory solutions, it goes through the same PBLC process
    that we have for anything that we would deploy. What is really
    important about that is the rigorous testing that we do, the
    understanding of explainability and transparency--can we
    reproduce it and do we understand what the models are
    providing? There is always a human in loop. Really important
    you keep that human in the loop.
        Mr. Huizenga. Obviously, you are not quite as forward
    facing as a real estate transaction?
        Mr. Stevens. Yes. Similar answer. I will talk about fair
    housing and then back to the first part of your question, how
    important advice from a human really is in housing. For fair
    housing, we built the classifiers----
        Mr. Huizenga. By the way, as a former realtor, I fully
    subscribe to that.
        Mr. Stevens. Yes. Our research shows--it might be
    counterintuitive for a leader of AI to say it, but our research
    shows that humans making a buy, sell, rent decision want to sit
    across a kitchen table and get real human advice from someone
    who lives in their market. So, our AI is built to really target
    the processes that are keeping real estate agents, like your
    former self, away from those important conversations. Then
    along the way, we built classifiers like the Fair Housing
    Classifier, AI should be held to the same standards as any
    human. We want to make sure the facts that are presented to
    people as they prepare are meeting those same fair housing
    standards.
        Mr. Huizenga. Real quickly, in my last minute and a half
    here. Mr. Cohen, how is Nasdaq leveraging artificial
    intelligence to create markets that are not only more
    efficient, but more transparent and fair? You touched on that a
    little bit. Do you care to elaborate or--or--how you have to
    deal with that--through that?
        Mr. Cohen. A really great example of that I did not talk
    about in my opening remarks--we have a market intelligence desk
    where our listing clients, our corporate clients will contact
    us and ask us about sentiment, will ask us about, what is the
    intelligence and what are you seeing in the markets? What we
    are doing is using AI to essentially group different parts of
    data or different pieces of data, which is what we see in
    social media, what we see in the news, and what we see in the
    markets, and the confluence and the aggregation of that allows
    us to use AI, develop patterns, develop insights and
    intelligence. We can provide business leaders so they can make
    real-time decisions. Now, again, there is a human in the loop.
        Mr. Huizenga. Yes.
        Mr. Cohen [continuing]. We ensure that, to the extent that
    there is news out there that is not real or fake, we are taking
    that out before we provide----
        Mr. Huizenga. So you are stitching that altogether.
        Ms. Whitmore, I am sorry. You seem to be--you are going to
    be answering a lot of our questions in writing, but I want to
    touch on the anti-money laundering (AML), Know Your Customer
    (KYC), and how AI has the potential to enhance compliance
    efforts. So 10 seconds.
        Ms. Whitmore. Okay. We will follow up in writing on that.
    Thank you.
        Mr. Huizenga. With that, I get to yield back with 2
    seconds. So sorry about that.
        Chairman Hill. I thank the vice chairman.
        I call on the gentleman from Massachusetts, Mr. Lynch, who
    is the ranking member of our Digital Assets, Fintech, and AI
    subcommittee.
        Mr. Lynch. Thank you, my friend.
        I have got a couple of things here. So we are talking about
    a quasi-sandbox process here to try to test some of these AI
    applications and financial services. As a member of this
    committee in the past, we have done oversight on the whole
    sandbox process. One of our more instructive experiences was in
    Singapore where we went in there and they had a financial
    services tech--a fintech sandbox where they invited companies
    to come in and participate.
        The difference here is that when they did that in
    Singapore, they had their regulations in place already and here
    we are--we are doing it backward. We are inviting--we are
    inviting financial services companies as they deploy AI to
    choose which--which consumer investor protections, which
    soundness--safety and soundness regulations that they might
    want to avoid.
        Mr. Branch, is that a proper way to introduce a new
    technology? Where you are asking the private sector to identify
    regulations that are in place to protect consumers, investors,
    depositors--because this applies to banks as well--and you are
    asking them what would you like to do to avoid a compliance
    with some of the current existing financial services
    regulations?
        Mr. Branch. Thank you for the question, Representative.
        No, I do not think that is a responsible way of moving
    about this. The sandboxes that are being proposed often are
    completely deregulatory. That is entirely different from the
    Singapore model, which was actually hailed in this committee
    just a few months ago. They had regulators in place. It was
    time limited. Consumers were warned if they are a part of that
    sandbox. None of that is present here.
        Mr. Lynch. Right. So the idea of the sandboxes, you have a
    contained area. Matter of fact, Mr. Chairman, I would like to
    ask unanimous consent to introduce the Fintech Regulatory
    Sandbox Guidelines, dated November 2016. This was in advance of
    the Singapore sandbox.
        Chairman Hill. Without objection.

        [Information referred to can be found in the appendix on
    page 126.]

        Mr. Lynch. So a few of the things that they point out here
    that are essential to--essential to a successful sandbox, and
    that is to manage risks, to contain the possibility that
    individuals might be injured, or financially damages. The
    consequences of failure for that technology need to be
    contained and people need to have advanced knowledge of the
    risks that they are embracing by participating in adopting that
    technology. It cannot--it says here it cannot be used as a
    means to circumvent legal and regulatory requirements, which is
    exactly what this bill is suggesting, that--that in adopting
    AI, they are allowing, or encouraging, ways of circumventing
    existing regulation in what are supposed to be a technology-
    neutral approach.
        There are also cautions here where applicants have not
    demonstrated that they have done due diligence, including
    testing the proposed financial services technologies in a
    laboratory environment beforehand and knowing the legal and
    regulatory requirements for deploying the proposed financial
    service technology.
        So what--so, Mr. Branch, what is wrong with that whole
    approach in terms of protecting consumers, investors,
    depositors, and others who rely on the benefits of this
    financial services industry?
        Mr. Branch. Well, I think the main thing that is wrong with
    that approach is that there is everything to gain for large
    corporations, and the harm is just going to be entirely brought
    on by the American consumer. If anything, if things go wrong,
    the government is going to be expected to either bail out or
    help out the corporations that have put the consumers in harm's
    way.
        Mr. Lynch. Thank you very much. Mr. Chairman, I believe my
    time has expired. I yield back.
        Chairman Hill. The gentleman yields back.
        I am pleased to recognize the gentleman from Oklahoma, Mr.
    Lucas, who chairs our task force on Monetary Policy and
    Treasury Market Structure.
        Mr. Lucas. Thank you, Mr. Chairman and thank you to our
    witnesses for being here today.
        Our whole economy benefits when United States companies
    lead the world in innovative financial services. I think we all
    agree on that, and our regulations should allow for that
    responsible growth and expansion when it comes to AI, too.
        Mr. Cohen, would you expand a little bit, what are the
    best-use cases, as you see them, for AI to be further deployed
    in capital markets?
        Mr. Cohen. Thank you for the question.
        We, at Nasdaq, deploy AI in two ways: in our products and
    then on the business and the way that we think about in the
    products. We are trying to generate greater productivity,
    better outcomes for our clients, and lead to better client
    experience with the types of solutions we provide and I
    mentioned this before. We see the possibility of improving
    how--how jurisdictions put out regulation, the complexity
    associated with regulations, and then what clients need to do
    to comply with those ever-changing regulations. AI can go from
    reg to code and really allow them to ensure with confidence
    they are complying with regulation. So that is a great use case
    for us.
        In fighting financial crime--I mentioned it earlier. We
    must use AI in the data that we have at our disposal to make
    sure that we are staying ahead of the bad guys. In terms of
    what I talked about with human trafficking, financing
    terrorism, we need to make sure that our solutions are
    protecting the reputation, the brand, and the consumer behind
    those institutions. So that is really, really important.
        Then in our markets, we are using AI--and this is our north
    star--to make sure that we are democratizing the types of--the
    types of capabilities that otherwise small broker dealers do
    not have the R&D and engineering talent to develop on their own
    and they would be left behind if not for Nasdaq and others
    developing it on their behalf.
        Mr. Lucas. On that same thought, throughout the committee's
    work on combating fraud and scams in financial services system,
    we have discussed the sophistication and technological assets
    that bad actors use against consumers. So giving you prime
    time, opportunity to answer, Ms. Whitmore, how is AI being used
    right now to detect and stop fraud and scams, and how will
    those services improve and expand in the future?
        Ms. Whitmore. Well, I am thankful for the question.
        So what we are seeing in terms of the threat landscape
    relative to AI--two areas.
        The first is attackers using AI to fuel traditional
    cybercrime. We are largely seeing that impacts the speed and
    the scale with which they operate.
        The second part that is not being talked about as much is
    how attackers are targeting AI, so creating the ability to make
    agents inside of our environments into rogue insiders that
    cannot be trusted.
        What we see with that is the need for security to be
    closely coupled with AI innovation, the need to make sure that
    we are protecting the build, the run, and the access--so,
    particular to the run time, making sure that agents do not have
    the capability to go rogue and that those protections are in
    place so that organizations can successfully innovate without
    that being hijacked by attackers.
        Mr. Lucas. Ms. Manfra, can you discuss how smaller
    financial institutions like community banks can utilize this
    technology? Why is it important for financial institutions of
    all sizes to have access to the latest technology, please?
        Ms. Manfra. Absolutely. Thank you for the question.
        One of the great benefits of what we are seeing in AI is
    the democratization of access to data that, historically, only
    large, well-funded institutions might have and the additional
    productivity gains that smaller institutions that are more
    resource-constrained now have access to larger data sets in
    more real-time and are able to put in place improved customer
    service experiences, improved productivity, are better able to
    detect fraud and reduce the amount of false positives so that
    their employees do not have to spend time chasing down
    potential dead ends.
        So we see a lot of opportunities in the small and medium-
    size financial institution space.
        Mr. Lucas. Mr. Cohen, I will ask you to respond in writing.
    Are existing regulations appropriate to encourage innovation
    while maintaining appropriate consumer and financial stability
    protections and what are the regulations/statutes, if any, that
    would use modernization? You can respond in writing.
        Mr. Lucas. I yield back, Mr. Chairman.
        Chairman Hill. I thank the gentleman.
        The chair recognizes the gentleman from California, Mr.
    Vargas, who is the ranking member on our Task Force on Monetary
    Policy.
        You are recognized for 5 minutes.
        Mr. Vargas. Well, thank you very much, Mr. Chairman. I
    appreciate it, you and the ranking member putting this
    together. I think it is a very important hearing.
        I do not believe that you can unring a bell or put the
    genie back in the bottle or do anything like that. So the
    reality is that science moves forward and our knowledge moves
    forward; you cannot reverse it. So I think AI is here. It is
    now, how do you manage it?
        A few years back, I had the opportunity to go with some of
    my colleagues here to the World Economic Forum, and somehow I
    got assigned to go to dinner with the young tech entrepreneurs
    to listen to Sam Altman. I thought I was not going to
    understand a thing because I am not young and I am not a
    techie. So I thought, this is going to be interesting, but I am
    not going to understand anything.
        It turned out I understood everything, because they really
    did not talk about technology; they really talked more about
    philosophy. It really was more the philosophy of the machine,
    you heard earlier, versus human being, the data that you put in
    and what comes out and whether that data is positive or
    negative.
        You could see some of the results--I have seen them in the
    following years--where, yes, AI, you will ask it a question and
    it will come back with what we would think is a pretty absurd
    answer or horrific answer for some young people, but you could
    see why the data that you put in, it would ultimately reach
    that conclusion. Because not all data is positive; you also
    have data that is in there that is negative.
        So I appreciate, then, also, we have heard today really the
    competition between the good and the bad in AI, because, I
    mean, the bad guys use AI also, for fraud and other things.
        With all that being said, Mr. Cohen, you have a very
    transparent company. In fact, when we talk to your staff, they
    get back to us right away. So I will ask you, who is winning
    here? I mean, are the bad guys winning in the attacks or not?
        Mr. Cohen. Yes and thank you for the question.
        What is interesting about this technology is, the rate of
    advancement of this technology is faster than the rate of
    adoption and you do not often see that with technology.
        The second thing is, we need to, as an organization, all of
    us, within our four walls, talk out loud about the negative
    scenarios that might occur with AI so that we prevent them. If
    we do not talk out loud and we do not have conversations
    internally about that, then we are missing an opportunity.
        To your more specific question, we are seeing in our
    markets bad guys, if you will, using AI to come up with more
    sophisticated market manipulation schemes. If we are not using
    the data and the technology to stay one step ahead, then we
    risk the integrity and the investor confidence around our
    markets.
        For banks, fraud is a massive, massive problem and fraud is
    becoming more sophisticated. It is more difficult to detect,
    and you cannot do it on your own; you need a public-private
    partnership, and you need a massive amount of data to really
    stay ahead of it.
        So, unless we arm our community to be able to do that, then
    we are going to be a step behind, banks are going to be subject
    to fraud, markets will be subject to market manipulation.
        Mr. Vargas. So I understand that part. You have to--they
    are going to use it, so you have to prepare. I mean, it is one
    of these things that has to happen.
        You also said something that is very interesting because it
    is also a philosophical point. And I do have a master's in
    philosophy. That is why I am so confused normally but it was
    more based on religion. I studied to be a priest for a long
    time. The notion of keeping the person in this process becomes
    harder and harder as it advances, because the decisions it
    makes--it takes all this data, and it uses it so quickly.
        How do you figure to keep the person, a human being, in
    this process--as you would call it, in the loop--when AI can
    make decisions so quickly?
        Mr. Cohen. It is an excellent question and the human in the
    loop is really, really important. Building those critical-
    thinking skills and those judgment skills are exactly what we
    are training our people to do.
        So, one, we are putting the tool in our hands. Two is we
    are providing them with training and education and three is we
    are putting good governance around them and providing them with
    policies that they can follow and understand. So the humans are
    armed with enough so they can make the right decisions at the
    right time and understand their roles and their roles are not
    decreasing or becoming marginalized. It is actually more
    important than ever to have that human in loop and provide that
    judgment, as you noted, because AI is designed to provide you
    with an answer. It is our responsibility to determine if that
    answer is correct and if that is the answer that we want to
    provide our investors and, if you will, institutions that we
    serve around the globe.
        Mr. Vargas. Well, I apologize, I asked you all the
    questions. I apologize to the others I was not able to, but I
    have 10 seconds left. With that, I will just thank all of you
    and I will thank the chair. Appreciate it very much.
        Thank you, Chair.
        Mr. Barr [presiding]. The gentleman yields back.
        The gentlewoman from Missouri, Mrs. Wagner, is now
    recognized for 5 minutes.
        Mrs. Wagner. I thank the chair.
        I say welcome to our witnesses.
        Mr. Cohen, in your testimony, you highlighted how Nasdaq
    has been at the forefront of technological innovation in our
    capital markets since the exchange was founded in 1971.
        Nasdaq was an important player in the creation of
    electronic trading. While rapid growth over the last few years
    in generative artificial intelligence, or AI, has brought this
    technology center-stage for the general public, Nasdaq has been
    quietly using AI for years and years to fight fraud and
    increase market efficiency, liquidity, and transparency.
        Mr. Cohen, can you describe some of the ways that Nasdaq
    currently uses AI and the extent to which generative AI either
    has or might have a role to play in our capital markets and how
    do you manage the risks associated with this technology, sir?
        Mr. Cohen. Thank you for the question.
        It is in our ethos, if you will, to adopt and integrate
    emerging and advanced technology early. We see it as a
    competitive advantage and one that we have embraced since our
    founding in 1971.
        With respect to the use of AI, what is important for this
    committee to understand is, you cannot harness the power of AI
    unless you make the foundational investments to do that. We
    have done that over the past decade, whether it is embracing
    the cloud, putting good governance in place, having a mature
    posture over information security, training and upskilling our
    employees, and making sure we have good governance; good
    governance is the lubricant for innovation. That is the way we
    see it.
        Then you have to be really clear to make sure that it is
    centralized so you do not have the AI sprawl happening within
    your organization and shadow AI, where you cannot control it,
    you do not know about it.
        So all of those things have been what we have been focused
    on over the past decade, which has now enabled us to put them
    in our products. As you mentioned, we use it for market abuse.
    We use it to fight crime. We use it to democratize access. We
    use it to provide more transparency, more information to end
    investors so they understand the risks that they are taking
    when they invest in our markets.
        We are always trying to stay one step ahead of the bad
    guys, one, and two, one step ahead of ensuring that our markets
    continue to be the best in the world. We have the pride in
    making sure that the U.S. markets are the most robust, most
    vibrant, if you will, highest moral ground in terms of the way
    that we operate and we protect the U.S. markets every day in
    the way that we use that----
        Mrs. Wagner. You are absolutely right; we have the most
    extraordinary markets in the entire world, and we are going to
    put a fine point on that this week with the Incentivizing New
    Ventures and Economic Strength Through Capital Formation
    (INVEST) Act that will be moving forward. So we are very
    excited about that.
        You are right; other companies and industries need to look
    at that AI sprawl, shadow AI, some of the things that--I do not
    know that they are entirely discerning, but those are key.
        Mr. Cohen, do existing technological-neutral regulations
    provide sufficient guardrails for the responsible use of AI in
    our capital markets, or are new AI-specific updates and
    clarifications required? If updates are in order, what should
    they look like, and how can they be implemented without
    stifling innovation?
        Mr. Cohen. That is a great question. It is one that we
    actually think about quite a bit, especially as the technology
    is evolving.
        What I would say is, we have a great foundation. We have
    securities laws. We have NIST that we follow. We have Reg
    Systems Compliance and Integrity (SCI) and we have worked hard
    as an industry to put more rules in place around technology. So
    the foundation is there.
        I think the key to that question is, because the technology
    is advancing at such a rapid pace, information-sharing or
    private-public partnership, where we can share information
    about the advancements of this technology to identify gaps,
    was--we do not know what those gaps might be today, because the
    advancement of that technology is such that it is not linear.
    It is exponential and we tend to plan very linearly as humans.
        So we are just going to have to make sure we stay in touch,
    we share information, we use the foundation that we have that I
    mentioned, with NIST and Reg SCI and the securities law, to
    ensure, when a gap comes up or when there is a risk that we
    have identified, we work together to address that quickly.
        Mrs. Wagner. Well, I may not--you may have to respond in
    writing here, but I am interested in knowing if there are any
    existing rules or ambiguities or proposals that are limiting AI
    innovation from reaching its full potential in U.S. capital
    markets--obviously, a focus of mine.
        So I will look for your answer in writing.
        Mrs. Wagner. I thank you for your testimony.
        I yield back to the chair.
        Mr. Barr. The gentlelady yields back.
        The gentleman from New York, Mr. Meeks, is now recognized.
        Mr. Meeks. Thank you, Mr. Chairman.
        Let me go directly to Ms. Whitmore.
        Ms. Whitmore, the nexus of AI and cybersecurity is
    particularly important and can be particularly concerning as
    well. Last year, the Salt Typhoon hack against the United
    States companies and high-profile leaders was called the
    largest cyber breach in United States history.
        Can you just give me a very brief summary of what happened?
        Ms. Whitmore. Yes. Thank you for your question.
        So you are referring to an attack by a nation-State actor,
    in this case China, that targeted our telecommunications and
    critical-infrastructure industries across the board, with the
    intent to steal data to then use it to meet political
    objectives at some point.
        Unique to critical infrastructure, the component there is
    the fact that the Chinese nation-State may be intentionally
    looking to embed themselves within critical infrastructure,
    with the intent to take some sort of action at a later date.
        Mr. Meeks. Thank you.
        Mr. Chairman, I want to, for the record, enter this article
    entitled ``U.S. Halted Plans to Sanction Chinese Spy Agency to
    Maintain Trade Truce.''
        Mr. Barr. Without objection.

        [The information referred to can be found in the appendix
    on page 146.]

        Mr. Meeks. Then, a few days after the report, more news
    came out of Department of Commerce, which I cannot believe that
    they intend to allow Nvidia to sell one of the most advanced AI
    chips to China that had been previously banned for export.
        In fact, the Justice Department was just about to go after
    someone who was trying to sneak them into China. Yet--this is
    unbelievable to me--the President started a trade war that we
    are losing, and he is now afraid to punish the People's
    Republic of China (PRC) and its entities that hacked our most
    innovative companies to access the private communications of
    the American people.
        Further, by allowing advanced Graphics Processing Units
    (GPUs) to go to the PRC, he is throwing away our biggest and
    most important advantage in the AI race with China.
        That is innovation? This is not innovation. This is not
    winning. This is, indeed, weakness. Unfortunately, it is going
    to cost our country.
        Let me go to another matter. In America today, rising
    housing prices and stagnant wages have put home ownership out
    of reach for millions of Americans. Median home prices remain
    near historic highs, and too many working families are nowhere
    near achieving their American Dream of owning a home.
        A major part of the problem is that we simply are not
    building enough homes. Whether it is because of zoning
    barriers, permitting delays, outdated systems, and rising
    construction costs because of--you got it--Trump's tariffs on
    materials like lumber and others, you have what is becoming a
    perfect storm.
        So, Mr. Stevens, in your testimony, you mentioned that AI
    can help address some of these challenges. You said it could
    help with clearing permitting and zoning backlogs, reduce loan
    origination and compliance costs, and speed up the development
    process. That would be good news.
        My question to you, sir, is: Aside from boosting
    efficiency, how can AI be leveraged to enhance housing
    affordability and increase the supply of homes? The President
    says this is a hoax, affordability, but let us go. How can that
    happen?
        Mr. Stevens. Yes. Thank you, Congressman, for the question.
        I very much agree, we are facing a housing affordability
    crisis, and there is no silver bullet. I think you mentioned
    one of the biggest drivers is that we are 4 million homes short
    in terms of supply.
        We are very excited about the efficiency gains that you
    mentioned--zoning laws, really understanding how we can make
    more efficient the mortgage and real estate processes.
        I would say, beyond that, bringing more information to
    consumers. Four million families will buy a home this calendar
    year, 2 million for the first time, and understanding what is
    available to them, what they can afford, what they should
    explore, helps actually reduce some of the affordability
    problems we are facing today.
        Mr. Meeks. There should be some public-private partnering
    so that we can ensure that AI is being used safely and
    effectively? Is that not correct? Do you----
        Mr. Stevens. Yes.
        Mr. Meeks [continuing]. agree with that?
        Mr. Stevens. Exactly.
        Mr. Meeks. Thank you.
        I yield back.
        Mr. Barr. The gentleman's time has expired.
        I now recognize myself for 5 minutes of questioning.
        In President Trump's July AI data action plan, he notes
    that we must develop a grid to match the pace of AI innovation.
    Unfortunately, our grid is currently unable to keep pace with
    our energy demands.
        The Department of Energy projects that blackouts could
    increase a hundredfold by 2030, with one study saying that the
    Mid-Atlantic and Great Plains regions could face 400 hours of
    power outages annually.
        The growing demand for power, with AI, is increasingly a
    major strategic challenge for the United States.
        Ms. Manfra or Ms. Whitmore, what is the threat to U.S.
    national security and global leadership in the AI space if we
    cannot meet the supply of energy needed to power AI data
    centers in the United States?
        Ms. Manfra. Thank you, sir, for the question and raising
    this important topic and I am happy to go and continue with you
    and your staff in more discussion on this.
        Yes, overall, we need to ensure that we are able to keep
    pace with the demand for energy in order to support American
    competitiveness in this space. We also need to ensure that we
    are doing that responsibly and in partnerships with those
    companies that are involved in the distribution and provision
    of electricity.
        Mr. Barr. Well, what considerations go into a firm's power
    strategy, your firm's strategy, when it comes to training and
    developing frontier AI models?
        Ms. Manfra. We are heavily invested in optimizing our
    infrastructure, so our technical infrastructure, and the way
    that we run and power the machines that power AI for energy
    efficiency. So we spend a lot of time, and that is a key
    principle of ours, is to optimize for energy efficiency.
        I hope that answers your question.
        Mr. Barr. Well, let me ask the question a different way.
        Our self-inflicted energy crisis is a direct result of bad
    policy--Green New Deal, Paris climate accord, Network for
    Greening the Financial System, the SEC's climate disclosure
    rule, the regulators' ``Principles for Climate-Related
    Financial Risk Management,'' chokepoint, the push for the
    greening of the financial system.
        Mr. Cohen, can you talk about the importance of traditional
    financing of the most reliable and affordable, dispatchable
    sources of power--that is, fossil energy and nuclear? Why is
    that important for us to win the global race for AI leadership?
        Mr. Cohen. I think, at the end of the day, it is important
    to note that Nasdaq does not operate its own data centers and
    so, when we think about power--and we operate our markets, for
    instance, out of New Jersey, a data center we have been in for
    a very, very long time--we are working hand-in-hand with our
    data-center providers on the power strategy.
        As you just heard, we, as a public company, a for-profit
    company we want to be as cost-effective as we can, making use
    of all sources of power, and ensuring that we have the
    portability----
        Mr. Barr. Well----
        Mr. Cohen [continuing]. and mobility to move should we----
        Mr. Barr. I am going to reclaim my time and make an
    editorial comment.
        China has 33 nuclear power plants under construction, with
    an additional 200 in planning. Coal has been the largest source
    of global electricity for 125 years, and it will be for decades
    more in the future. It is by far the largest source of
    electricity in China. They built 100 coal plants last year.
        Now, we need diversified energy sources. I am not
    advocating for exclusively fossil energy or exclusively
    nuclear, but we have to get with the program. If we are going
    to win the race for AI, we have to look at what our competitor
    is doing, and we cannot put our heads in the sand and continue
    to regulate our energy sources into oblivion, if we want to win
    the AI race.
        Final question: We all acknowledge the risks of AI that Mr.
    Branch talked about--AI-generated fraud, deepfake abuse,
    consumer deception, et cetera but he criticizes this proposed
    AI preemption language as an effort, quote, to undermine State
    AI protections. There are more than 160 State-level laws and
    AI, by its very nature, is interstate commerce.
        Mr. Cohen, can you talk about the importance of Federal
    preemption to avoid a patchwork of conflicting and inconsistent
    AI regulations that would stifle innovation?
        Mr. Cohen. We think Federal preemption is extremely
    important, and we think it needs to be principles-based as
    well. We do not think we need a new central regulator while we
    consider that. I think the reasons for that are: If you cause
    confusion, if you raise the cost of doing business, it will
    simply just go overseas.
        Mr. Barr. Well, I agree, we do not need a new central
    regulator, but we do need Federal preemption so that AI
    innovation can fight crime, detect and stop fraud, democratize
    finance, promote financial inclusion, and protect our markets.
        I yield back.
        Now we recognize the gentleman from Illinois, Dr. Foster,
    for 5 minutes.
        Mr. Foster. Thank you, Mr. Chair, and thank you, witnesses,
    for your really excellent testimony.
        You know, Congress is currently stuck in this issue with
    federalism and Federal preemption. You know, we are struggling
    between the nightmare of having 50 independent standards for AI
    regulation and the fact that we have a do-nothing Congress that
    is so paralyzed by the thing that--even straightforward things
    that we ought to be able to agree on, nothing is happening. We
    are seeing consumers suffer greatly already, and it is not
    going to get easier.
        There is a middle ground on this, which is something that
    we are going to be circulating draft legislation on and I would
    like to comment on, which is simply a proposal to let
    coalitions of States form their own standard, so that any
    coalition would have to have, for example, 20 or 25 percent of
    the population, and so there would be significant coalitions,
    and companies would only face at most two or three sets of AI
    standards.
        This sort of cooperative federalism, if you will, I think,
    is the thing that we may be able to agree on in this and so I
    just--I am tossing that out. There will be more detailed
    legislation, but it is a pretty simple concept.
        Most of you who work internationally already deal with a
    dozen different countries and their regulations, so if the U.S.
    had two or three standard markets for things like--you know,
    for everything AI--privacy, all this sort of stuff--I think it
    would actually work well and we would preserve the laboratory
    of democracy of the States on this.
        Also the big thing that is coming at us is agentic AI. This
    is--you know, it is the future, not only of financial services
    but everything consumer-facing and Business-to-Business (B2B)
    transactions.
        In the near future, most businesses are going to be facing
    not their customers but their customers' agent, and that is
    going to change everything. Customers' AI agent will not care
    if you have a pretty website or an easy-to-use customer
    interface or a friendly smile, you know? Most consumer agents
    are just going to be given instructions to get the best price.
    Customer loyalty will go to zero and this could destabilize our
    banking system--for example, with AI-driven bank runs. Agents
    are going to squeeze the margins out of all consumer-facing
    businesses, and that will be extremely disruptive. You know,
    for example, to pick an unsympathetic example, used-car dealers
    will no longer have an infinite supply of clueless customers to
    take advantage of.
        However, it may also really simplify consumer protection
    law, because so much of consumer protection law is to protect
    unsophisticated customers. If there are no unsophisticated
    customers because you are dealing with their AI, it will change
    everything.
        All right. So what can Congress realistically do about
    this?
        First off, preventing identity fraud by enhancing the
    deployment of digital driver's licenses and mobile ID. This is
    being done by most of the States right now. The EU, the U.K.,
    Asia, everyone has--almost every country has the ability to get
    out your cell phone, to deploy a federally issued real ID, a
    driver's license or a passport or equivalent, and prove they
    are who they say they are in an online transaction. We have to
    do everything we can to get this adopted.
        What we need here are not--you know, one of the legislation
    is to study best practices for sandboxes that we are not yet
    going to build. What we actually need is a pilot program for
    KYC customers using a REAL ID digital driver's license, which
    would just simplify all kinds of things and prevent a lot of
    the identity fraud.
        Second, I think, if we were able to define United States
    standards for agent-to-agent communication.
        You know, right now, there are multiple competing
    standards. There is the Model Context Protocol from Anthropic.
    There is an Agent-to-Agent (A2A) coming out of Google.
    Actually, Google has two. You just in your testimony referenced
    AP2, where I spent a while on the website last night. It is
    actually, I think, a much better step in the direction of--
    because what we need is a well-defined, legally precise
    language to communicate things like what is the privacy, what
    is the data retention, what is the logging of the interaction,
    and all the legally things in order to have reliable
    transactions between agents.
        There are policy decisions there that we can defer. What we
    should do is have NIST or--you know, probably NIST and a number
    of partners in industry come together and define those
    standards. The White House is correctly appointed to the
    advantage of having the U.S. lead that. What I would like to
    see is to have NIST and the United States lead the world in
    defining agentic communication standards the same way we led
    the world in defining the personal ID standards, digital ID
    standards, that are now at Android and IOS and everything else.
        Anyway. So these are--there are lots of things to talk
    about here.
        I just want to thank you. You had really high-quality
    testimony, and I learned a lot reading it.
        Thank you.
        Mr. Davidson [presiding]. I thank the gentleman.
        I now recognize myself for 5 minutes.
        Artificial intelligence does not change our fundamental
    regulatory framework which allows innovation while protecting
    consumers from abuse or fraud. As AI becomes more deeply
    integrated into our financial services and housing markets, it
    is essential that existing consumer protection laws covering
    privacy, fair lending, data security, fraud, et cetera,
    continue to apply fully regardless of the technology used. The
    principles should remain simple. If a practice violates the law
    without AI, it should not be permissible with AI.
        At the same time, AI's increasing reliance on large,
    sensitive data sets raises important questions about whether
    our current data protection frameworks are sufficiently clear
    and durable for the modern economy. In my view, privacy is the
    base layer for ethical AI, and we need to update our privacy
    laws. We need it to do a much more robust job of that. Congress
    should reassess how consumer data is collected, used, shared,
    and safeguarded, without imposing rules that freeze innovation.
        Finally, we should also know that a fragmented patchwork of
    State AI mandates risks undermining both innovation and
    privacy. A harmonized Federal framework can protect consumers
    while giving innovators the clarity and certainty they need.
        This needs to be a more thoughtful approach, though, and
    Congress should have the debate and make the decision, not
    something that can be done with a two-sentence addition to some
    bigger bill on another topic that basically says, ``We can do
    whatever we want.'' It does require a more thoughtful approach,
    in my opinion.
        Mr. Stevens, what specific categories of consumer data are
    being used to train and operate AI systems today?
        Mr. Stevens. Many. Thank you for the question.
        I totally agree, especially in housing, how important
    privacy is. This is the biggest financial transaction of your
    life; there are many factors that go into it. All our consumers
    report to us that they want to really make sure their personal
    data is theirs.
        We use basically things like signals on what preferences
    they have, financial data they give to us when they are
    thinking about what they can afford. They work with real estate
    agents who use some of our tools and tell us about their hopes
    and dreams. We make sure that all of the above is meeting the
    Federal standards and laws.
        When we work with other companies, as an example I gave
    during verbal with OpenAI, we continue to make sure that is
    still the case. So, when ChatGPT might want to violate fair
    housing, our classifier makes sure that does not happen and
    that all of the consumer's information is kept as theirs.
        Mr. Davidson. How do you ensure that you protect personally
    identifiable information (PII)? What kind of transparency is
    there? What kind of recourse do consumers have?
        Mr. Stevens. Yes. The easiest way is to not--or, is to
    first keep it safe, so keep it on our systems that are
    protected. Only certain humans have legal ability to view that
    data or work with that data. With third-party partnerships, we
    usually do not share that information. If we do, under limited
    license, that is then deleted upon completion of the
    transaction.
        Mr. Davidson. Thank you.
        Mr. Cohen, there is a lot of discussion about the
    importance of winning the AI race against China. Frankly, I am
    concerned--to beat China, we should not try to be more like
    China. I do not want to be like China. I want to be like
    America but yes, on the technology end, we want to win the AI
    race.
        Can you give us a sense of how you view this discussion
    through the lens of our capital markets and give us your
    perspective on the level of AI adoption Nasdaq is seeing from
    the broader market and China in particular?
        Mr. Cohen. Thank you for the question.
        Our focus continues to be, as an operator of markets here
    in the U.S., to make sure that the markets here are the most
    robust, vibrant, most attractive in the world and we will use
    AI to continue to ensure that we do that.
        We do it--and I spoke about this before--by introducing
    whether it is order types of functionality that democratize
    access, that allow smaller broker-dealers to participate in our
    markets so we have a healthy ecosystem, or we root out
    financial crime to make sure that there is investor confidence
    and market integrity, or we use surveillance to detect
    increasing sophistication in market abuse.
        All of those require us to use AI, to invest in AI, to make
    sure that we are partnering with folks around here, the
    colleagues that I have here on this panel and others, to ensure
    that we ensure that the U.S. capital markets remain the most
    robust in the world.
        Mr. Davidson. Yes. I mean, in the private sector, of
    course, there is a lot of incentive to keep up with this arms
    race. On the other side, you are dealing with regulators that
    sometimes have not kept up with a lot of the technology.
        So, when we think about it here in Congress, what are the
    most important things we could do to make sure that America is
    the most competitive place for capital to be deployed?
        Mr. Cohen. We talked a little bit about it before. The need
    for sandboxes, the need for the ability to continue to innovate
    in a safe and responsible manner, I think, is extremely,
    extremely important and not having overly complex regulation
    that we have to patch together is also very important.
        Now----
        Mr. Davidson. My apologies. I did not give you much time to
    answer that. If you want to provide a longer answer----
        Mr. Cohen. I can certainly do that.
        Mr. Davidson. My time has expired, and I respect everyone
    else's time.
        I now recognize the gentleman from California, Mr. Sherman,
    who is also the ranking member of our Capital Markets
    Subcommittee.
        Mr. Sherman. I want to commend Mr. Foster for his digital
    ID bill. I know it is running into some opposition, but,
    ultimately, if we cannot identify ourselves in this new digital
    age, this will be a problem.
        I also commend him for leading a letter asking that the
    Financial Stability Oversight Council (FSOC) to look at the
    financial risks of an AI bubble.
        Ms. Manfra, do you see signs of an AI bubble out there?
        Ms. Manfra. Sir, respectfully, I am not a market analyst.
        Mr. Sherman. Okay.
        Ms. Manfra. I am responsible for risk and compliance,
    like----
        Mr. Sherman. Thank you.
        Ms. Manfra. So I will yield.
        Mr. Sherman. But you are from Google and there is someone
    else who works at Google, Ray Kurzweil. I think he was once
    your chief technology officer, and he was before the Science
    Committee some 22 years ago. I asked him how long it will take
    for non-biological intelligence to surpass human intelligence,
    and his estimate then was 26 years. So we are almost there.
        First of all, you hire very smart people, because to
    predict something and to be that close--we are spending
    trillions of dollars, as a species, to make AI more powerful. I
    cannot find a program anywhere in the world--and I would
    support it if I could find it--designed to monitor and prevent
    self-awareness and ambition, to determine what can be done to
    prevent AI from developing its own objectives, which I think
    would be hostile to ours.
        There is a book--you may not have to read the book--great
    new book coming out--it just came out. It says, if anyone
    builds it, everyone dies.
        Is Google--do you have a department, do you have a budget,
    for monitoring, for preventing self-awareness, ambition, or AI
    creating its own goals?
        Ms. Manfra. We invest a great deal in ensuring that the AI
    capabilities we are developing is in line with human values----
        Mr. Sherman. I have always heard that, but I cannot find a
    single scientist who is looking for self-awareness and
    ambition. Obviously, you do not want criminals stealing our
    data or our money, and what you describe fits into that
    category.
        Can you name one person whose job it is at Google to
    prevent AI from becoming a creature rather than a tool?
        Ms. Manfra. So let me get back to you. We do have
    researchers that are heavily invested in this topic. So, if I
    can, I will get back to you. It is not my area.
        Mr. Sherman. Please. Please get back to me because I have
    not been able to find anyone. Then you ask about these issues,
    and you get this vague ``align with our goals,'' and the goal
    usually is, ``Well, look, we can make trillions of dollars by
    making AI more powerful,'' and there is no money in preventing
    AI from taking over the world. That is the next generation's
    problem.
        One problem we have--and I will feed you one more
    question--is AI doing its analysis and then reflecting the
    discrimination that has existed in our society for many
    centuries. For example, you could say, if somebody grew up in a
    particular ZIP code, they are more likely to default on their
    rent.
        What do you do at Google to make sure--and I think there
    was another study that says, if you want to analyze who is
    going to be successful at Yale, it turns out the best predictor
    is being named, I think, ``Jared'' or some other name
    associated with the elites in our society.
        What do you do to make sure that the prejudices of the past
    are not built into the computers that will control the world in
    the future?
        Ms. Manfra. Thank you for the question. This is something
    we care deeply about and issued our responsible AI ethics
    principles nearly a decade ago.
        So our approach, first, is to ensure that humans are
    involved in the setting of the parameters, if you will. We are
    constantly designing, redesigning, and then monitoring to
    ensure that we have an unbiased approach and we are always
    iterating. It is not----
        Mr. Sherman. Do you have a program that----
        Ms. Manfra. Yes.
        Mr. Sherman [continuing]. prevents the ZIP code that you
    were born in from influencing the decision that is made?
        Ms. Manfra. I know we look at those things to ensure that
    we do not have--I will get back to you on a specific program.
        Mr. Sherman. I will ask everyone to get back to me on
    whether you are focusing on preventing AI from becoming a
    creature.
        I will yield back.
        Mr. Steil [presiding]. The gentleman yields back.
        The gentlemen from Tennessee, Mr. Ogles, is recognized for
    5 minutes.
        Mr. Ogles. Thank you, Mr. Chairman.
        Thank you to the witnesses.
        You know, obviously I serve on Financial Services. I also
    serve on Homeland Security, where I am the chairman of Cyber,
    which would obviously overlap with AI. This obviously, when you
    look--and, quite frankly, Mr. Chairman, when you look at AI,
    what really got my interest in this topic was the national
    security side of the financial services conversation.
        As we look at artificial intelligence in the context of
    financial services, I think it is important we recognize that
    we are dealing with a technology that does not fit neatly into
    traditional policy frameworks. AI is not a product or a single
    system; it is a capability layer that will influence everything
    from market structure to fraud prevention to consumer
    decisionmaking.
        That means the questions in front of this committee are not
    simply about regulating a new tool. They are about whether our
    existing financial architecture, our risk models, our
    supervisory expectations, disclosure rules, or even our
    assumptions about human judgment are prepared for a world where
    some of our core analytic work is done by systems that learn
    and adapt at scale.
        At the same time, AI gives institutions the ability to
    detect threats faster than humans ever could, analyze complex
    data sets that were previously unusable, and offer consumers
    services that are more personalized and more efficient.
        The challenge for Congress is understanding where AI is
    simply accelerating what firms already do and where it
    fundamentally changes the nature of a financial decision, a
    compliance obligation, or a market signal.
        My goal today is not to choose a side between regulation
    and restraint. It is to ensure that, as AI becomes more
    embedded in our financial system--and, as said previously, it
    is here, it is not going away--that we understand the concrete
    risks, the real opportunities, and the limits of the technology
    and, I might argue, the unlimited potential of the technology.
    We need clarity where clarity is necessary, flexibility where
    innovation requires it, and a realistic view of how these
    systems operate in practice.
        Ms. Manfra, you mentioned grounding and outcome-based
    evaluations in AI risk management. Can you give a real-world
    example of how grounding has corrected or improved financial AI
    models' output and how regulators should think about evaluating
    whether grounding was done properly?
        Ms. Manfra. I can get back to you on a specific example,
    but I would say, in general, the core to what we need to focus
    on is having transparency and explainability.
        So, as--which we have invested a lot in and work with a lot
    of our financial consumers as they use AI for things like fraud
    detection, anti-money laundering these various decisions that
    have this high risk, to be able to ensure that they can go back
    and understand why the model made the decision or the
    recommendation that it did and enable the compliance with
    existing laws that do apply to these scenarios as well.
        So that is a space that I think is very important, is
    ensuring that transparency and that explainability, in
    particular for financial services, though they are not the only
    industry, but to enable AI for these heavily manual but
    oftentimes high-risk, with a lot of false positives that put a
    lot of burden onto organizations.
        So I believe it actually can--if organizations implement it
    responsibly, you can reduce your compliance obligations, better
    manage your risk, and get better outcomes for consumers and
    partners.
        Mr. Ogles. I think for those who are watching at home the
    key here is that transparency and understanding because the
    financial institutions are required--there are compliance
    requirements. If they are using AI to achieve those
    requirements, to be in compliance, they need to understand how
    AI is helping them get there but then also understand where it
    may make mistakes or hallucinations, right?
        Mr. Cohen, your Dynamic M-ELO AI-powered order type adapts
    to real-time market conditions. What specific signals or data
    streams do they rely on? And should investors have standardized
    transparency into how these AI-assisted order types behave?
        Unfortunately, you have 40 seconds.
        Mr. Cohen. I might come back to you with details in
    writing.
        Mr. Ogles. Give us that overview.
        Mr. Cohen. So we take in 130 different signals from the
    market to determine how that order type should operate. Every
    30 seconds, we take that in because real-time market
    conditions, they give you a sense--we are processing millions
    of messages per second. To be able to process all of that, we
    need to use AI to ensure that we are staying one step ahead of
    the market conditions that then allow our investors to get the
    kind of execution they require.
        Mr. Ogles. Yes, sir.
        Mr. Cohen. Remember, we are highly regulated, so everything
    we do, we need to have the Federal Communications Commission
    (FCC) approve. They need to make sure there is explainability
    and reproducibility on what we provide.
        Mr. Ogles. Mr. Chairman, I will just say that, as we look
    at the national security landscape, the financial landscape our
    adversaries are leveraging AI without any guardrails, and we
    have to be prepared and ready to, quite frankly, go on the
    offense.
        Mr. Chairman, I yield back.
        Mr. Steil. The gentleman yields back.
        The gentleman from Missouri, Mr. Cleaver, who is the
    ranking member on the Subcommittee on Housing and Insurance, is
    now recognized for 5 minutes.
        Mr. Cleaver. Thank you, Mr. Chairman.
        If I could also thank Mr. Lynch for kind of leading this
    effort on our side.
        I just have one question. My interest in AI grows daily,
    and I am even more concerned today than I was yesterday.
        If you look at what is happening in the Federal Government,
    being reshaped by the President and the Supreme Court, 75
    percent of fair-housing staff is gone. It is not, like, a
    couple people, but 75 percent has gone since January.
        Because I have been around, I know that when we are talking
    about trying to deal with housing, which is easily the number
    one or number two most significant domestic issues we face, I
    am further--my paranoia has grown stronger because I am
    concerned that AI could also be an excuse--``Well obviously,
    this is fair.''
        It goes along with something that was raised earlier. Fair
    housing, I mean we are--``Do not worry about it. AI is going to
    make sure that everything is fair. I mean, after all, AI cannot
    discriminate.''
        It is infuriating, first of all, that this is already
    taking place in the government, and then we have to deal with
    AI possibly doing even more damage to fair housing. If
    everything was clear and equality was a part of everyday
    thought I would not even need to raise this issue but that is
    just not the case.
        So, at a time when civil rights and fair housing are
    issues, growing issues instead of being subsided, somebody help
    me understand how--why AI will not make this worse.
        Mr. Stevens. I would be happy to, Congressman.
        Working at Zillow day to day, we totally believe that fair
    housing is paramount. I would say, an opportunity that AI--we
    have already seen is, you can start describing what home you
    are interested in a more natural language. No one wakes up and
    goes, ``I want three bedrooms, comma, big backyard, comma,''
    like a computer would standardly want. With generative AI, you
    can say, ``Hey, I am looking for that big backyard, close
    commute to work,'' that kind of thing.
        The problem is, there are certain fair-housing questions
    that are not legally supposed to be answered and that is what
    led to our Fair Housing Classifier. It is probably one of the
    more complicated models we have developed at Zillow. Instead of
    going, ``Okay, we will just keep that to our ourselves,'' we
    felt that the right thing to do to help other researchers,
    other institutions, was to make that available to others. They
    can contribute back and, of course, use it in their own
    systems.
        So that means every deployable AI in the housing sector can
    take advantage of a fair-housing-compliant method.
        Mr. Cleaver. Well--and I appreciate your response. I am
    wondering about the others on our esteemed panel today.
        Do any of you have a suggestion?
        Mr. Branch. Representative, may I respond to your question?
        Mr. Cleaver. Yes.
        Mr. Branch. There is an assumption built in with a variety
    of companies when they discuss the positives of AI, and one of
    those built-in assumptions is that AI is going to remain
    aligned to human values or American values. What they are not
    acknowledging is the possibility of AI drift. That is when AI
    starts to drift away from what some of our values actually are.
    That is when you see instances of kids being encouraged to harm
    themselves or discrimination in banking decisions and that is
    why it is important to have these regulations in place to
    ensure that these companies are being regulated and ensured
    that they are making sure that their algorithms are working in
    place.
        There are ways to do that. We have spoken a lot about China
    today. I do not think Americans have to choose between winning
    a hypothetical AI race in China or being protected from harmful
    AI products. I think that is a false narrative and a false
    binary.
        Mr. Cleaver. Thank you.
        Thank you, Mr. Chair.
        Mr. Steil. The gentleman yields back.
        I recognize myself for 5 minutes.
        I think we have seen a real positive shift in AI policy
    from the previous administration to the Trump Administration.
    The Biden Administration's precautionary approach was stifling
    innovation. The Trump Administration's AI action plan try-first
    approach is the right approach, in my opinion.
        I think financial services have been leading the way in
    applying AI in the real world for decades, so I just wanted to
    get a couple instances on the record.
        I will start with you, if I can, Mr. Cohen. Can you just
    describe a couple ways that Nasdaq is utilizing and has been
    utilizing AI in the financial services space?
        Mr. Cohen. Thank you for the question.
        I will go back to my opening comments, in that we focus on
    three core principles when we employ AI, and I talked about
    liquidity, transparency, and integrity.
        With regards to integrity, our anti-financial crime
    platform, Verafin, is allowing banks to operate with confidence
    when it comes to fraud management and it is allowing them to
    achieve goals that they otherwise could not achieve on their
    own when they limit fraud in the market.
        Mr. Steil. So it is essential to manage fraud, illicit
    trading broadly in the risk space.
        Mr. Cohen. Absolutely. It does more than any single
    institution can do on its own, because it is cloudinated, AI-
    enabled, and uses consortium data.
        Mr. Steil. Let me jump to you, Ms. Manfra, if I can, a good
    Wisconsonite. Good to see you here.
        Can I ask you to pinpoint, how are firms in particular
    thinking about this when they are deploying this new
    technology?
        Ms. Manfra. What I would say, and reiterate what you
    probably heard before as well, is, the financial services firm
    has a long history in managing model risk, and there are
    processes and regulations and oversight in that place.
        So what we see with our financial services customers is, as
    you noted, very innovative uses for these set of capabilities,
    but also very thoughtful. So they think about things like, how
    do I ensure a human in the loop, how do I ensure I have proper
    governance in place, how do I ensure that I understand how I
    can meet my risk management and compliance goals and----
        Mr. Steil. So, knowing that these firms are going through
    this deliberative process to think through how they are
    balancing reward and risk in utilizing AI, I want to come
    back--because we discussed the sandbox concept earlier--I want
    to come to you, Mr. Cohen, with a short question here.
        Earlier this year, Chairman Hill and I introduced the
    bipartisan Unleashing AI Innovation in Financial Services Act,
    which enables regulatory sandboxes at the Federal financial
    agencies that are targeted in size and scope.
        These sandboxes, they have a handful of things: They have
    to be approved and overseen by Federal regulators; they require
    compliance strategies and risk management, which we were just
    discussing; they must not impose systemic or national security
    risks--obvious to make sure we are doing that.
        So Federal regulators will impose appropriate limitations
    or conditions on them. Additionally, fraud and unsafe and
    unsound practices will remain prohibited under the sandbox.
        So the idea that these sandboxes create a free-for-all and
    allow participants to flout all the rules they just do not
    like, it is simply not true, right?
        So, rather, the sandboxes provide a more secure environment
    to experiment with AI, enabling innovation, with built-in
    guardrails, Federal oversight and so this is how I think we
    learn from best practices for governance while exploring the
    applications that will improve American financial life.
        So here is the question: Would these AI sandboxes enable
    regulators and market participants to responsibly experiment
    with AI and learn best practices?
        Mr. Cohen. Yes, the key qualification is what you said; it
    needs to be controlled, it needs to be targeted, it needs to be
    time-boxed, and it cannot be used to circumvent--and we have a
    very practical example of that.
        We, as a highly regulated institution, often run pilots
    with the SEC, where we are really transparent about the results
    of that pilot, and we ensure that informs decisionmaking and
    allows for better outcomes for investors in the markets.
        Mr. Steil. So it is not really a free-for-all. It is a
    sandbox that has a regulatory structure in place. Underlying
    laws apply. Ms. Manfra laid out how firms are thinking about
    applying AI. They are going through a deliberative, thoughtful
    process.
        In your opinion, do sandboxes help create innovation and
    development in the United States in a thoughtful, structured
    way?
        Mr. Cohen. They have. They have. They have been critical to
    our innovation. Again, if it is controlled, time-boxed, and
    targeted and transparent, it yields positive results.
        Mr. Steil. I appreciate that.
        Ms. Whitmore, I wanted to take my time and come to you and
    discuss, in particular, how AI can eliminate fraud, deepfakes,
    and really help us. In 10 seconds, can you add to your previous
    comments?
        Ms. Whitmore. We will be happy to follow up with a written
    response to that. Thank you.
        Mr. Steil. Thank you very much.
        I yield back.
        I now recognize the gentlewoman from Ohio, Mrs. Beatty, who
    is also the ranking member of the Subcommittee on National
    Security. She is now recognized for 5 minutes.
        Mrs. Beatty. Thank you, Mr. Chair and Ranking and thank you
    to the witnesses.
        Wow. A lot of good testimony. I never thought about asking
    my grandchildren, when we move, what they would like to take
    into a new house. So you put it in a whole new perspective for
    us.
        A lot of words today, on both sides of the aisle, and they
    all come together. You know, we have heard things like we need
    to strike the right balance, we should not discriminate with
    AI, we should have accountability, it should be well-defined,
    there should be responsible AI, we need clarity, we need legal
    and policy definitions. All of that is true, whether it came
    from the Republican or the Democratic side.
        I do not know that I have heard a lot of responses that are
    definitive to say, ``Here is how we are having responsible
    AI.''
        I have a great concern. I serve as ranking member on the
    National Security Subcommittee, and I am focused on preserving
    the safety and the security of our financial systems and
    protecting consumers from fraud.
        So my first question--and I am going to try to get through
    a series of them.
        Mr. Branch, we will start on this end. How is AI used in
    anti-money laundering and fraud detection, and how effective is
    it as a tool in our illicit finance arena?
        Mr. Branch. Representative, thank you, but I apologize, I
    am not an AI fraud analyst. That is outside of my expertise,
    but I could have my colleagues get back to you.
        Mrs. Beatty. Okay.
        Anybody else want to take a stab at that?
        Mr. Cohen. I can try.
        So we operate a financial crime management platform called
    Verafin. I have spoken about it here. Again, it is cloud-
    native, AI-based. We have over 2,700 clients. We have 725
    million accounts, just to give you a sense of how large that
    pool of consortium data that we dig into is.
        We use that--we use that to protect small and medium-size
    institutions, small banks, super-regional banks from the cost
    of fraud so they can operate and provide everyday customers and
    investors and clients of theirs with the security that they
    deserve when they invest their money and then have transactions
    associated with that institution.
        So that solution is really paramount to creating safety and
    responsibility along the traditional rails.
        Now, we also are looking at nontraditional rails like
    crypto rails and others that consider how payments and
    transactions may be--may be--subject to fraud as well. So that
    is where we are extending it, but it is incredibly important
    and valuable for small and medium banks.
        Mrs. Beatty. Okay.
        I will go to another question.
        We talked about--or someone, on both sides, said that we
    should not discriminate. One of my primary concerns about the
    use of AI, especially in financial services, or maybe not
    necessarily with just financial services, is the potential for
    bias in algorithms and that they lead to unfair results. You
    can only be as good as the individuals that put the data or
    gather all the folks in it.
        So, as we look to develop a comprehensive AI regulatory
    framework, how do we ensure that the use of AI does not lead to
    discriminatory lending or pricing or underwriting or any other
    area that we use algorithms?
        Anybody want to take a stab at that?
        Mr. Stevens. Thank you, Congresswoman, for the question.
        I will say, I was first hired by Zillow to work on the
    Zestimate and two answers would be: additional data and then
    also a national right-sized framework that we can apply across
    the country.
        On additional data, that is the benefit of GenAI. For the
    Zestimate, we can now look at additional comparables maybe
    outside of your direct neighborhood to get a more balanced and
    fair evaluation. We can also look at the listing images, the
    listing description--all this unstructured data that was not
    possible to be ingested before.
        At the same time, we have to do a lot of checks that is the
    right data and there is no inherent bias within it and that is
    where we use, like, the NIST Risk Management Framework (RMF) to
    make sure, before we deploy, that it is a fair evaluation.
        Mrs. Beatty. Thank you for adding and saying that because
    you--the outcome has to be that you have people from other
    communities and there is a diverse pool of folks at the onset.
    So it would help us not have discriminatory practices with
    that.
        Thank you, because I know, for example, women have not
    always been included in trials, and so when you are building
    these algorithms, you have to have people in examples that you
    put in. So that is something that I am watching carefully.
        My time is up. Thank you, Mr. Steil. I yield back.
        Mr. Steil. The gentlewoman yields back.
        The gentleman from Florida, Mr. Haridopolos, is now
    recognized for 5 minutes.
        Mr. Haridopolos. Thank you, Mr. Chairman.
        I appreciate this thoughtful conversation. I know
    Congressman Liccardo and I are working across bipartisan lines
    and trying to find solutions of this and become a hyper-
    partisan issue which is, I think, all of our goal today,
    especially as we face this increasing threat from China. We
    just had a hearing in my subcommittee that I chair on the
    threat in space and some of those challenges you face, of
    course, here on Financial Services, a little bit different
    model.
        If I could, Ms. Manfra with Google, if I could ask this
    question. One of the things that I have been concerned about--
    and this goes across the gamut as far as age groups too--are
    these deep fakes, this idea where AI has used--someone's
    reputation can be literally eliminated in a day, because there
    is some type of fake video. What does a huge company like yours
    do to try to identify these to take them either offline or to
    identify that these are deep fakes?
        Ms. Manfra. Well, I will say just to start--first, thank
    you for the question, and it is something that has been
    concerning for us for a long time, and we have invested a lot
    in technical measures to be able to better detect as well as
    making a lot of these measures and processes that we have
    learned from available to others to be able to do that and to
    rapidly be able to take those down, and happy to get more
    details if you are interested in more of the specifics on the
    technology that we use to be able to----
        Mr. Haridopolos. I think that would be very helpful
    because, again, this is a reputational issue.
        Ms. Manfra. Absolutely.
        Mr. Haridopolos. It takes a lifetime to generate a
    reputation. It can be destroyed in a minute.
        Ms. Manfra. Absolutely.
        Mr. Haridopolos. I think that this is especially true not
    only in politics but just everyday life. We have seen how
    people get hazed, bullying happens, especially at the middle
    schools and high schools, and this could easily be done with
    just a basic technology. So any advice y'all have would be
    great.
        I think the second one, I will ask with Nasdaq, if I could.
    There are existing rules of the road. Are those guide rails
    sufficient for what you do every day in the investment world
    and are there enough recommendations that our committee is
    putting out and you are, of course, producing and providing for
    us--where are we? Are the guardrails sufficient now? Are there
    a lot more guardrails needed, in your opinion, to make sure the
    financial markets are protected from AI in a negative way?
        Mr. Cohen. Our markets, as you know, are highly regulated
    and have a lot of transparency to ensure that we run our
    markets in a way that is enduring from a trust perspective.
    ``Trust'' is not a word we have spoken about a lot, but trust
    is extremely important when we talk about our markets.
        So the fact that we are highly regulated, run mission
    critical infrastructure, requires us to have certain standards
    outside of just thinking about AI. I think we can use that as
    the foundation, whether it is the securities law, it is Reg
    SCI, it is following this, all of that is foundational.
        What I mentioned earlier, and I think is incredibly
    important, as this technology advances and maybe creates gaps,
    we need to make sure there is information sharing, that we have
    a safe space to share information with one another about what
    we are seeing, about what we are seeing within our own four
    walls and across the industry. So I think that would be what I
    would advocate for to make sure that we have what we need on a
    principle-base and from a Federal level. Those are the two
    things that I think we would also want to see.
        Mr. Haridopolos. Thank you.
        Ms. Whitmore, what keeps you up at night about AI? What is
    the biggest concern that is out there, in your opinion? You see
    this every single day. We deal with a lot of issues every
    single day, but you are focused on AI. What is the fear factor
    that you have, wondering, God, I hope they do not do this, or
    this is the threat that is most prevalent out there, and in
    both the business community and the general internet space?
        Ms. Whitmore. Thank you for the question, Congressman.
        First, I think, just the challenge that attackers are
    leveraging AI for, which is primarily speed and scale. So now
    we are looking at reduced timeframes to execute a ransomware
    attack to 25 minutes, and that includes from initial access
    into an environment to the time that they encrypt or steal data
    in that environment.
        The second is the concern that attackers are specifically
    targeting AI to then misuse it intentionally, right? That
    requires a use of some specific guardrails to put in place but
    in particular, one, we can--to solve these two challenges,
    right, we can--we need to be fighting machines with machine
    speed. That is the only solution that we are going to have to
    transform the way that we detect and respond, and to truly get
    to decreased numbers in both of those categories.
        Second is the capability to ensure that as these systems
    are running within environments, that we have got the critical
    levels of visibility into the actions they are taking so that
    when an attacker does decide to change the functionality of an
    agent and have it go rogue, that we can detect that and stop it
    as quickly as possible.
        Mr. Haridopolos. Well, I appreciate the thoughtful answers
    to those. Mr. Chairman, I yield back.
        Mr. Meuser [presiding]. The gentleman yields. The gentleman
    from Illinois, Mr. Casten, is now recognized for 5 minutes.
        Mr. Casten. Thanks so much. I appreciate y'all.
        I want to focus specifically on securities regulation. I am
    going to oversimplify, but I am going to--Mr. Cohen, I will ask
    you to correct me if I have got it wrong. We have got Federal
    securities regulation through the SEC. We have got an
    additional State layer with registrations and licensing laws,
    and then, of course, the rules that the individual stock
    exchange has put in. Are you generally supportive of that
    structure? Having those three layers of regulation?
        Mr. Cohen. We are and do not forget Financial Industry
    Regulatory Authority (FINRA).
        Mr. Casten. Yes.
        Mr. Cohen. So we have SEC, FINRA, and then obviously we
    have our own, if you will, responsibilities as an exchange,
    highly regulated exchange, listing qualifications, for
    instance.
        Mr. Casten. Fair point. I raise that only because I had
    some concerns about in your testimony, when you supported
    Federal preemption of State laws related to AI. So, I just want
    to pick at this point a little bit. I am concerned partly
    because the Trump White House has instituted massive cuts to
    SEC. So if this is a three-or four-legged stool, it is kind of
    a wobbly chair right now. I get nervous about saying let us
    shorten all the legs to make it work.
        I also have a concern as an engineer, as a guy who built
    some AI models before I came here. I do not think this is as
    big a deal as we talk about it. I think we use words--you know,
    we call it intelligence, but it is just--it is a massive
    correlation machine. It is not intelligent, per se, but I can
    plug in huge amounts of data on the markets and historic
    trading trends, and say I want this algorithm using those
    historic correlations to identify relationships and optimize
    for profit margin. It can do that. It is really cool, right? I
    can plug in the entire Taylor Swift catalog and say put let us
    take a Tom Waits song and make it sound like a Taylor Swift
    song. I can do that. It is not intelligent. It is just
    massively correlative.
        If--there is no reason why anybody using an AI tool is
    necessarily incentivized to say: Let me optimize this for
    truth. Let me optimize this for ethics; let me optimize it for
    legal compliance. I am going to optimize it for the thing that
    is valuable to me, making something that sounds like Taylor
    Swift, making--you know, making money on markets.
        I guess I do not understand why--or maybe correct me if I
    am misreading you. Would it not be wise to prevent States from
    protecting investors from AI-enabled market manipulation,
    especially if the Federal Government is not doing it?
        Mr. Cohen. Let me answer the first part of your question.
    As an exchange, we are an SRO, self-regulatory organization,
    and we have a public mandate. Our public mandate is to ensure
    that we run fair and orderly markets, and we take that
    responsibility very seriously. So our north star is just that.
    When we design solutions around our markets, we are thinking
    about making sure that----
        Mr. Casten. I want to--and I do not mean to cut you off. I
    am just nervous about the clock here. I was in the energy
    industry for a long time. I remember there was a professor who
    did this experiment with a bunch of grad students where none of
    them had information, but they were basically in a simulation
    of California power markets, and they all independently, just
    based on the other students' trading strategy, figured out how
    to collude. It was when Enron blew up, it was this thing of,
    okay, you do not actually have to have information to collude,
    but there is an incentive to collude in the structure, and
    people will figure it out.
        There was a University of Pennsylvania study recently that
    found that AI bots released into simulated markets will do--
    essentially do the exact same thing.
        As an SRO, how do you--if we are not going to regulate--if
    we are going to provide liability shields for these companies--
    not about whether the tool is good, but if we are not going to
    provide liability shields, do we not still have to get to that?
        Maybe, just to sort of put the punch line on it, and then
    you can use the rest of the time, there is a long legal history
    within the courts, within the SEC, of saying if there is not
    intent to defraud, you cannot hold somebody to account. My
    nervousness is that a lot of the people building these tools
    are saying, ``Well, I want a liability shield. I want State
    preemption.'' If the tool is not designed not to commit fraud,
    it can still be optimized and end up committing fraud. So as a
    market manager, how do you protect against that and what sorts
    of regulatory reforms would we need to do to it, modify some of
    these--this history that you need to show intent?
        Mr. Cohen. Yes. Just to be clear, in terms of Federal
    preemption, our view is around minimizing complexity. What you
    just described, you can achieve all of that at the Federal
    level. You do not have to have it as a patchwork in each State
    where it is a struggle for people to figure out how to operate
    in that State. It is not just the exchanges; it is our members
    that we think about and the industry at large.
        Again, being highly regulated, we do have a north star
    about how we want to serve and what our business interests are.
    So it is more about the complexity, and it is more about the
    patchwork that we are concerned about. We think we can solve
    for everything you just talked about at the Federal level----
        Mr. Casten. I am out of time, but I would just welcome
    comments from all the respondents about--there is a hole in
    securities laws, and any ways that we can fix those holes to
    address this question of intent I think is important and would
    welcome all of your expertise. Yield back.
        Mr. Meuser. The gentleman's time has expired. The gentleman
    yields back. The gentlemen from Wisconsin, Mr. Fitzgerald, is
    now recognized for 5 minutes.
        Mr. Fitzgerald. Thank you, Chair.
        Regulatory technology, sometimes called ``reg tech,''
    refers to innovation in technology deployed by companies to
    manage regulatory compliance. Mr. Cohen, how does Nasdaq use AI
    and other--any type of machine learning powered by reg tech
    tools to kind of enhance the regulatory compliance, such as
    trade surveillance or how much just efficiency in cost savings
    has gone on?
        Mr. Cohen. So we have two reg tech solutions that we offer;
    one is for market abuse surveillance, and the other is actually
    Axiom, which is a regulatory compliance and reporting
    application. I have not talked as much about that today, but
    that is important because it is used by all the Tier 1 banks
    across the globe. It is the standard for regulatory reporting.
        What we do, because we manage and orchestrate complex
    workflows through this tool, is we use AI for data discovery so
    they can go through complex regulations and understand and
    interpret how that piece of regulation needs to be implemented
    into the reporting obligations they have. Also, if they have
    any anomalies in their financial reporting, capital
    obligations, liquidity obligations, it may take them, a handful
    of individuals, hours, days, weeks, to identify anomalies. We
    can do that through AI very, very quickly, keep the human in
    the loop, make sure that they are center, in the center of any
    decisionmaking that can occur, but they do not have to do the
    heavy lifting to understand that there is an anomaly.
        Also what we have done from a product development life
    cycle perspective, we are now taking regulations, reg all the
    way to code, through agents and allowing agents to hand off
    work to one another to take a piece of regulation that could be
    4-to 500 pages long, and allow them to implement it in our tool
    and then deliver it more quickly to our clients. This is
    incredibly important in a world where the regulation is
    exponential in terms of the obligations that one needs to meet.
    They are changing. There are reforms. There is a different view
    in America versus where Europe and Asia are going.
        So just as a Tier 1 bank or Tier 1 institution to keep up
    with it, it requires a lot of operational spend. We are trying
    to reduce your operational spend so you can do the things that
    you need to do, put money back into your balance sheet that you
    can then use for loans or for businesses that help small
    businesses and their customers.
        Mr. Fitzgerald. Very good.
        Mr. Stevens, let me ask you the question first, and I was--
    just maybe fill in afterwards. I am kind of switching it on the
    paper here. How are AI-driven underwriting models helping to
    safely expand access to credit for a broader range of
    borrowers? Because there are concerns, I know, that AI has led,
    maybe, to some riskier borrowers--I do not know how else to
    describe it--or that it has kind of changed, kind of the
    perspective on borrowers compared to the due diligence that--
    you know, the human that sits down and fills out the form and
    does everything that we are typically used to. So I was
    wondering if you could comment on that idea?
        Mr. Stevens. Yes. Thank you, Congressman, for the question.
        The way AI is helping is by looking at additional data
    sources that, if we just left it up to humans, they might not
    have time to consider and that, I think, is helping
    particularly first-time buyers qualify.
        At Zillow, we make sure the actual decisionmaking in the
    underwriting process, what we are prequalifying you for, for
    example, is completely human-driven. So we still rely on humans
    making that ultimate judgment.
        Mr. Fitzgerald. Very good.
        Ms. Manfra, the 1945 McCarran-Ferguson Act left insurance
    regulation largely to the States. With all the talk of freeing
    financial services from the onerous State regulation of AI,
    notwithstanding the Act, can the business of insurance benefit
    from being included in any national approach to AI?
        Ms. Manfra. I cannot speak to the specific regulatory
    framework of the insurance. It is just not an area of my
    expertise but absolutely there are lots of benefits for AI in
    the insurance industry, and I think would benefit from being a
    part of a national conversation and framework for standards of
    transparency and explainability.
        Mr. Fitzgerald. Has it advanced quick enough or far enough
    to really be a tool, or is it something that is just kind of a
    sideshow right now?
        Ms. Manfra. For insurance companies?
        Mr. Fitzgerald. Yes, for insurance companies.
        Ms. Manfra. I would say insurance companies are using AI
    tools--it is very different in terms of the customers and--but
    absolutely, they are using AI and innovating around that for
    sure.
        Mr. Fitzgerald. Very good. Thank you all. I yield back.
        Mr. Meuser. The gentleman yields back.
        The gentlewoman from Massachusetts, Ms. Pressley, is
    recognized for 5 minutes.
        Ms. Pressley. Thank you to our witnesses for joining us
    today.
        AI is everywhere: our phones, our classrooms, our
    hospitals, our bank loans and job applications, every facet of
    our lives. That is why we must ensure that AI works for
    everyone, and that instead of deploying biased AI, which can
    create harm or compound existing harm, it needs to benefit
    everyone, all people, regardless of race, gender, income level,
    medical conditions, or other parts of our identity.
        Mr. Stevens, should we prohibit the use of AI that
    discriminates on the basis of race, gender, or other factors?
    Just a yes or no.
        Mr. Stevens. Especially in housing, I believe yes. Fair
    housing, make sure that we eliminate all forms of racism,
    different disparate impact, that kind of thing.
        Ms. Pressley. Okay. I will ask the question of everyone,
    just for the purposes of the record. So just a yes or no.
    Should we prohibit the use of AI that discriminates on the
    basis of race, gender, or other factors? Yes or no? Ms. Manfra.
        Ms. Manfra. Yes.
        Ms. Pressley. Mr. Cohen?
        Mr. Cohen. Again, we apply three principles. We try to
    promote transparency, liquidity----
        Ms. Pressley. Yes or no.
        Mr. Cohen [continuing]. integrity in our markets and as a
    highly regulated institution, we are making sure that we
    prevent that in our algorithms.
        Ms. Pressley. Mr. Stevens, yes or no, again.
        Mr. Stevens. Yes.
        Ms. Pressley. Ms. Whitmore?
        Ms. Whitmore. Yes.
        Ms. Pressley. Mr. Branch.
        Mr. Branch. Yes.
        Ms. Pressley. Okay. Thank you.
        We need, urgently, civil rights laws for the 21st century
    in the age of AI, which is why I have joined with Senator
    Markey and also with Congresswoman Yvette Clarke to introduce
    the AI Civil Rights Act. It is urgent because the truth is that
    we are already behind. People are already being exploited and
    discriminated against with the use of algorithms.
        Now, let us take an issue like housing, for example, which,
    in my opinion, is a human right. Everyone deserves more than
    just shelter. They deserve to have a home. It is safety, it is
    dignity, it is health, it is mobility. In 2025, the Trump
    Administration has gutted the key agencies that protect against
    housing discrimination: the Consumer Financial Protection
    Bureau (CFPB) and Housing and Urban Development (HUD) Fair
    Housing Enforcement Offices. They are even trying to undo
    consent orders that are already in place, like the Townstone
    discrimination case in Chicago. This gap in civil rights
    protections is an opening for continued discrimination. One
    study found that mortgage lenders are 80 percent more likely to
    reject Black applicants compared to White applicants with the
    same qualifications.
        Mr. Branch, should we have additional oversight tools, such
    as assessments, to test the algorithms out before companies can
    use AI on the public? Yes or no?
        Mr. Branch. Yes.
        Ms. Pressley. Do any of our witnesses, other than Mr.
    Branch, disagree with that? Okay. Let the record reflect that
    no one disagreed.
        We know that bias exists in our Nation. We see the
    inequities all around us. I represent the Massachusetts 7th, a
    vibrant, diverse, dynamic district, and one of the most unequal
    in the country. We are in a 3-mile radius from Cambridge, home
    to MIT, Harvard, and AI advancement, to Roxbury, the blackest
    part of my district. Life expectancy drops by 30 years and
    median household income by $50,000. Now, that is the result of
    intentional lawmaking, which is why I believe we have to be
    just as intentional in undoing the harms and charting an
    equitable path forward, because AI is trained on data that is
    already biased and by humans that have biases. It can replicate
    and exacerbate these harms unless we have oversight and prevent
    it. We must not allow AI innovation without AI protections. In
    a world of artificial intelligence, we really cannot lose sight
    of what is real, and that is the people, the people and their
    livelihoods and their lives. If Republicans are serious about
    protecting our elders from fraud, and consumers from
    discrimination, then Congress must pass the AI Civil Rights
    Act.
        Thank you. I yield back.
        Mr. Meuser. The gentlelady yields. The gentleman from
    Indiana, Mr. Stutzman, is now recognized for 5 minutes.
        Mr. Stutzman. Thank you, Mr. Chairman. Appreciate you all
    being here today.
        Before I begin, though, I would like to address some
    concerns raised by my colleagues across the aisle. The
    Unleashing AI Innovation in Financial Services Act enables
    federally regulated entities to experiment with AI in secure
    environments, overseen, approved, and subject to the conditions
    of their Federal regulators, including for compliance and risk
    management. Importantly, the sandboxes must not present
    systemic or national security risk; two, unsafe and unsound
    practices and fraud remain prohibited; three, the sandboxes
    under unleashing AI would be targeted, time limited, and
    subject to regulatory approval and oversight; finally, the
    participants' compliance strategies must be approved by the
    appropriate regulator, and who would retain enforcement
    authority subject to the terms the regulator sets.
        So it has been great to have President Trump back in the
    White House for many reasons, but especially for the reason we
    are discussing here today. During the Biden Administration, AI
    innovation was viewed primarily as a threat to the American
    people. While President Trump has set the country back on track
    toward innovation and American AI dominance on the world stage,
    we cannot have Biden's allies in anti-innovation States, like
    California and Massachusetts, setting the trend on
    overregulating AI.
        I will follow Ms. Pressley's format, and I will begin with
    each of you. Do you think that a patchwork quilt, or a
    patchwork of inconsistent AI laws would help encourage
    innovation or stifle it? Yes or no? Stifle or unstifle?
        Ms. Manfra. It would stifle innovation and add complexity
    and burden to----
        Mr. Stutzman. All right. Mr. Cohen.
        Mr. Cohen. Add complexity and stifle.
        Mr. Stevens. Add complexity and stifle.
        Ms. Whitmore. I think too much complexity largely benefits
    attackers and not the defenders of our environments who are
    also looking to innovate.
        Mr. Stutzman. Mr. Branch.
        Mr. Branch. I do not believe that this stifles innovation.
    There is a lot of talk about different States that have
    different models. However, oftentimes, these State laws
    overlap, and so, it is not a fragmented 50-State analysis that
    needs to be done. In fact, oftentimes, many of these States
    overlap. So, they are not competing with a variety of State
    laws. The language in the laws overlap, and we see that in
    company valuation. If this innovation is being stifled, then
    these companies would not be worth trillions of dollars, and
    America would not be leading in the AI race, which we have been
    since the inception of this AI race.
        Mr. Stutzman. But you do not think that having Federal
    guidelines and then having States having their guidelines, that
    is going to create complexity and actually people just finally
    say, ``We are out?''
        Mr. Branch. Well, respectfully, Congressman, the States
    have had to step up because we do not have Federal guidelines.
    They are actually begging for the U.S. Federal Government to
    pass some form of regulation, but they have not passed that
    regulation. That is what the American people are waiting for
    Congress to do.
        Mr. Stutzman. All right. Ms. Manfra, is it not true that
    many of our existing risk management frameworks and governance
    practices already apply to AI?
        Ms. Manfra. Yes.
        Mr. Stutzman. So following up a little bit, can we update
    our guidance to be fit for AI without reinventing the wheel
    here?
        Ms. Manfra. Absolutely. As has been noted, the existing
    laws do apply. However, there are areas, such as ensuring
    transparency and explainability, as we have discussed,
    maintaining human in the loop, some of these other areas where
    it is important to clarify in existing rules. But we do not
    need new----
        Mr. Stutzman. All right. I want to talk a little bit about
    the risks a bit. We have had algorithmic trading in this
    country for decades, and these models are increasingly
    incorporating AI. One concern I have heard is that AI and
    machine learning could exacerbate herding. This is where
    trading models end up encouraging the same activity across
    firms because firms are using the same or similar models.
        Mr. Cohen, what do you make of the risk posed by model
    similarity?
        Mr. Cohen. That risk existed before AI. So you might have
    individuals that work for one firm go to another firm and
    design models that are very similar to one another, and
    therefore, exacerbate volatility or have the herd effect.
        Post some market events over the last 10 to 12 years, we,
    the SEC, and the industry have taken action to ensure that we
    have volatility guards. We put in Reg SCI. We put in a number
    of different rules to protect the marketplace but maybe most
    importantly, we have asked those that are introducing
    algorithms to test them and then monitor them throughout their
    lifecycle because it may be that a trading strategy is in the
    market for 3, 4 years, but a change in market condition then
    snaps the algorithm.
        So we are--we are really focused not just on the testing of
    it when it comes into the market but also on the monitoring and
    the transparency we have once it is in the market. FINRA has a
    responsibility to go into all of these broker dealers and make
    sure they have written procedures that they test it, they
    monitor it, and they are taking care of it in a judicious
    manner.
        Mr. Stutzman. Do you know, is that happening frequently, or
    is it just on occasion that you are seeing a herding strategy?
    Is it daily? Is it weekly? Monthly? I mean, does it happen--how
    often?
        Mr. Cohen. I would not know the exact details around that.
    We can come back to you on that question.
        Mr. Stutzman. All right. Thank you. Thank you, Mr.
    Chairman. I see my time has expired. I will yield back.
        Mr. Meuser. The gentleman yields. The gentlewoman from
    Texas, Ms. Garcia, is now recognized for 5 minutes.
        Ms. Garcia. Thank you, Mr. Chair, and thank you to all our
    witnesses here today.
        AI will impact every industry, every line of work, and
    every type of business in the near future. I think we can all
    agree to that. AI has potential to save employers on labor
    costs and increase productivity, but it can also disrupt
    workplaces and lead to loss of millions of jobs. At home, in my
    district as a working class district, 77 percent Latino, many
    of my constituents are concerned--in fact, deeply concerned--
    about how AI will impact their jobs and their livelihood.
        Mr. Branch, could you quickly discuss what the private
    sector, Congress, and the public sector can do to best address
    workforce challenges as AI becomes more widely adapted?
        Mr. Branch. Thank you, Congresswoman.
        They can speak with these workers and allow for worker
    input. They can speak with unions. Many unions are discussing
    algorithmic pricing. They are discussing wages and the
    necessity to ensure that there are fair wages as well. So I
    would encourage them to collaborate with unions and their
    employees.
        Ms. Garcia. Thank you.
        You also mentioned in your testimony how important it is to
    include the States when crafting AI policy. I mean, I think you
    just said that you need the national framework and then the
    States will act, and States actually have acted because the
    Federal legislation has not acted.
        In 2025, 38 States have adopted or enacted around 100
    measures. More than that, Colorado and Texas both enabled
    omnibus legislation regulating AI. In fact, our Governor just
    signed a bill to that effect, and will go into effect in
    January, and it is a new regulatory framework that applies to
    developers and deployers of AI systems.
        Many of the witnesses today spoke about the importance of
    cooperation between the Federal and State policymakers. Despite
    this, President Trump shared his intent to enact some type of
    AI moratorium through an executive order via social media just
    yesterday.
        As someone responsible for Nasdaq's financial technology
    division, Mr. Cohen, would it be practical--what would be the
    practical impact of such a moratorium on consumers and
    investors?
        Mr. Cohen. At Nasdaq, we do not serve retail investors
    directly. Institutions are our customers. From our perspective,
    again, we want to make sure that there is a balance between the
    types of rules and regulation that come in, more principle-
    based, allow us to advance the technology and innovate while
    making sure it is safe and responsible. We think from the
    perspective of the governance we have internally, the
    foundation of the rules that we have in the marketplace today,
    following this gives us that great foundation that we can build
    off of.
        Ms. Garcia. But what would a moratorium do?
        Mr. Cohen. With respect to----
        Ms. Garcia. The impact on Nasdaq and the work that it does.
        Mr. Cohen. I think we have been operating in a space with
    uncertainty already. So I do not think that it would have a
    significant impact on us if there was a moratorium.
        Ms. Garcia. The Trump Administration's AI Action Plan and
    recent actions pursue an aggressive deregulatory approach to
    AI, dismissing the role of both Republican and Democratic
    States. Rather than leveraging existing regulations and
    enacting new risk based and proportional regulation, President
    Trump and congressional Republicans have tried and failed twice
    to pass broad Federal preemption.
        Mr. Branch, can you shed some light on how State AI laws
    have helped to fill the current Federal enforcement gap in
    areas like algorithmic discrimination and consumer protection?
        Mr. Branch. Yes. Thank you for the question.
        States have stepped up to the bat in response to Congress
    not passing Federal legislation, and they have listened
    directly to their constituents. They have passed laws in terms
    of deep fake nonconsensual images. They have passed laws with
    regard to pricing as well as discrimination and this would
    essentially be usurping the States' abilities to protect their
    own consumers. This is something historically in the United
    States has been a right reserved to the States. There has never
    been a previous administration that has directly assaulted the
    States' abilities to protect their own consumers. So this is a
    very unique situation.
        Ms. Garcia. Thank you.
        Ms. Whitmore, I know I served on the AI task force working
    group that was put together from this committee and it always
    struck me, the final question really becomes who is auditing
    and who is monitoring and making sure that the AI--who is
    monitoring AI, and who is going to make sure that everything
    they are doing is safe, secure, and something that will not
    negatively impact everyday Americans?
        Ms. Whitmore. I think you are articulating the criticality
    of security being closely coupled with AI innovation so that we
    can answer those questions very clearly and ensure that the
    communications that are occurring are legitimate.
        Mr. Meuser. Thank you. The gentlelady's time has expired.
        Ms. Garcia. Thank you.
        Mr. Meuser. The gentleman from Texas, Mr. Williams, the
    chair of the House Small Business Committee, is now recognized
    for 5 minutes.
        Mr. Williams of Texas. Thank you, Mr. Chairman. Thank all
    of you for being here today.
        Artificial intelligence and the banking sector are two
    industries that are working together hand in hand. For
    financial institutions, artificial intelligence is a valuable
    tool that allows them to navigate risk assessments and stress
    test, detect and prevent fraud, assist with regulatory
    compliance, and several other critical operational duties.
        My district back home in Texas, Fort Worth and around in
    the metroplex and out West, loves their community banks, who
    are the backbones of their communities. As artificial
    intelligence continues to change the landscape of banking, it
    is crucial that we ensure that AI is empowering rather than
    complicating the work of community banks. So, Ms. Manfra, what
    would we be thinking about to ensure that this technology can
    reach community bankers and allow the importance they offer to
    grow and flourish and help people like myself in Main Street
    America?
        Ms. Manfra. Thank you for the question, sir.
        I think one of the opportunities, one of the greatest
    opportunities with AI and cloud and associated technologies is
    the ability to bring access to data and capabilities that
    historically was only reserved for people with deeper
    pocketbooks, as it were. So being able to empower community
    banks and other smaller organizations that are more resource-
    constrained, AI can give them access to that data in more real
    time. It also allows them to benefit from those efficiencies
    with the limited staff to be able to provide better services to
    their customers and maintain that trust that you noted in
    communities.
        I think what is important is ensuring that we do not create
    regulatory burdens that unfairly impact organizations that have
    less resources to manage those, would be my final point there.
        Mr. Williams of Texas. Thank you.
        AI tools are reshaping how businesses grow and enter
    capital markets. I see that in my business. I am in the car
    business and tools that help companies analyze market trends
    and reach investors prepare for fundraising used to be
    available only to the largest firms with unlimited resources.
    Now artificial intelligence has the potential to level the
    playing field by giving smaller and emerging companies better
    insight, data, and more efficient ways to market themselves to
    investors.
        Mr. Cohen, how is AI changing the ways smaller companies
    prepare for and access public markets?
        Mr. Cohen. You touched on a really important point, which
    is if we get AI right, it will democratize the way that small
    businesses can access and have access to the same tools as
    large businesses. They do not have the engineering talent,
    maybe the R&D budget, and maybe the sophistication of the
    larger firms. So it is up to companies like us to provide
    capabilities, make them available to those companies, whether
    it is in the capital raising side of the house or in a
    secondary trading side of the house, to ensure that when we put
    these capabilities out there, it is for all investors. It is
    for all of the individual and corporations that we serve and
    that allows them, if you will, to level the playing field and
    then grow their businesses.
        Mr. Williams of Texas. Great.
        Ms. Manfra, in your testimony, you highlighted Google's use
    of artificial intelligence to combat money laundering, fraud,
    and scams. Across the world, organized fraud syndicates are
    stealing billions of dollars a year from hardworking Americans.
    One of the biggest challenges is the speed at which these
    schemes operate. It takes only seconds to steal personal
    information, drain bank accounts, or even compromise identity
    verification systems.
        So we must give law enforcement the tools to keep up with
    the pace of these scams and improve the flow of information
    between victim to agency and across the agency. So my question
    here in the remaining time is could you explain how Google is
    using artificial intelligence to improve information sharing in
    the case of fraud and scams? Specifically, how does this real-
    time data sharing of the banks and credit card companies stop
    funds from flowing to these overseas scam rings faster?
        Ms. Manfra. There are two aspects to it. The work that we
    do internally across all of our various different platforms to
    stopping phishing scams, fraudulent websites, fraudulent ads,
    fraudulent reviews, building an ecosystem around Android that I
    talked about in my testimony. So we have invested a lot in the
    use of AI and ML to be able to prevent those for those who use
    Google platforms.
        We have also taken those capabilities and working with
    partners, in particular, in financial services, to be able to
    do things like using AI for preventing money laundering, and we
    are seeing huge benefits in that. Fraud detection has been a
    use case for machine learning for a very long time and
    continues to grow. So reducing those false positive rates so
    that investigators are able to spend their time on useful and
    productive leads.
        Also just being able to--we have one customer, a banking
    customer, who experienced a fourfold increase in the detection
    of suspicious activity; at the same time, reducing by 60
    percent the volume of false positives. So they are identifying
    more and having more productive investigations as a result.
        Mr. Williams of Texas. Thank you very much. I yield my time
    back.
        Mr. Meuser. The gentleman yields.
        The gentlewoman from Michigan, Ms. Tlaib, is now recognized
    for 5 minutes.
        Ms. Tlaib. Thank you, Mr. Chair.
        You know, one of the things that I continue to hear
    obviously is all the great things about some of the technology,
    including AI, but I think we are not realizing the way the
    corporate America is and is set up is very profit-driven, and
    the abuse is going to be very clear that everything is going to
    be around profit first.
        No matter how much people are like, It is going to make
    this easy or that easy, just New York Times publishing article
    after article about some of the abuses we already see. I am
    really concerned, of course, on the cost of prices, and the
    fact that private information is being used to price groceries.
    Groceries. I am going to walk into a place, and they are going
    to gather my private information and use that to price it
    differently than the person that comes right after me. It is
    discriminatory. It is private data and information. It does not
    belong in grocery stores.
        I know, Mr. Branch, you know about Stop Price Gouging in
    Groceries Act that I introduced. I would like for you to talk
    about surveillance pricing. I was with seniors and I was trying
    to explain to them how surveillance pricing is going to be used
    in a way not to reduce costs, but actually to be able to price
    higher on costs because they know where they work, they know
    what income they have, what they were searching online. So, for
    folks that are out there right now, can you talk about what
    that means right now? The use surveillance pricing by some of
    the big grocers?
        Mr. Branch. Yes. Thank you for the question, Congresswoman.
        In this new age of AI, the power is data driven. The
    largest companies on earth own all of that data and from a
    consumer perspective, there is a mismatch there because
    consumers only see the price that is in front of them, but the
    large companies have all the information from the consumer.
        So Consumer Report actually just came out with a report
    just the other day that showed that grocery prices based on
    algorithmic pricing can increase individual consumers' grocery
    bills by $1,200 over the course of a year. We are going to find
    ourselves in a situation where Americans are struggling and
    struggling to make ends meet, and these companies are arguing
    that it is just about everything else when, in reality, it is
    the algorithms that are setting the prices and manipulating
    them and taking advantage of the data that they have that the
    consumers are not privy to.
        Ms. Tlaib. That is in combined to the digital pricing. You
    know, explain to folks--so, bye-bye, tags that they have. It is
    going to be digital. Explain how that is connected.
        Mr. Branch. Right. So digital pricing can involve the price
    of certain goods, and those goods can differentiate between
    different groups of people, oftentimes based off of shopping
    behavior. So if you happen to like Wheat Thins, for example,
    Wheat Thins are going to be more expensive for you by maybe a
    quarter, maybe a nickel, whatever it is. But that little
    incremental amount----
        Ms. Tlaib. Yes.
        Mr. Branch [continuing]. ends up adding up over the course
    of the year and over the course of your grocery bill itself.
        Ms. Tlaib. Mr. Chair, I ask unanimous consent to enter into
    the record an article, ``Goodbye, Price Tags, Hello, Dynamic
    Pricing. Shopping has always been a game, and now it is being
    rigged against you.''
        Mr. Meuser. So ordered.

        [The information referred to was not submitted prior to
    printing.]
        Ms. Tlaib. I want to now talk about the discriminatory
    factors that are going to be at play, because I see this in the
    auto insurance industry that they already use and collect
    data--non-driving factors, like your marital status, your
    education level, your credit score--used toward auto rates
    right now without the kind of technology, and adding that and
    compiling that on to decide how much to pay--charge somebody
    for auto insurance in Michigan. It is mind-boggling that your
    son's GPA or your child's GPA has anything to do with driving,
    but they are asking for that data as well.
        What happens when AI models now--again, using this
    technology--are trained on historic or contemporary data that
    reflects past or present discrimination? Does not this risk
    create models that perpetuate or reinforce discriminatory
    practices? Because it is teaching it to go around, telling on
    themselves.
        Mr. Branch. That is correct, Congresswoman. I mean, the
    thing that a lot of folks do not necessarily understand about
    AI is that its goal is to move toward its goal as efficiently
    as possible. So, that can be replicating discrimination. That
    can be just trying to get the best price possible for the
    corporation or for the business at hand. Again, I think that
    leads back to the fact that a lot of these companies have the
    data and the power----
        Ms. Tlaib. Mr. Branch, I need my colleagues to know this.
    They are not going in there and saying, how can we make this
    cheaper for the consumer? They are saying how we can make it
    cheaper internally so we can--but they are going to charge our
    residents more, using this technology. We need to face the fact
    that is exactly what is going to happen. This thing of
    pretending it is going to make things easier--no, it is not. It
    is going to charge people more because they are going to know
    all this private information, know that they need these
    products and charge them more. Even if it is 10 cents, that is
    10 cents too much for our residents. Thank you.
        Mr. Meuser. The gentlelady's time has expired. I now
    recognize myself for 5 minutes.
        Thank you all very, very much, by the way, for being here.
    It is a terrific panel and highly informative.
        I think you are aware that many of us, the leadership of
    this committee, as well as the Trump Administration, is
    committed to unleashing AI's full potential, so we, indeed, in
    the United States, wins the AI race with investment and energy
    dominance to support the AI infrastructure. My home State of
    Pennsylvania is doing everything we can to draw in as much AI
    infrastructure as possible.
        However, it brings risks that we are talking about and
    exposing here, which are happening now and we want to mitigate
    for the future. They definitely include fraud, scams,
    profiling, and seem to be growing more sophisticated.
        AI is proving to be an incredibly strong tool for detecting
    and shutting down these very threats, but as well, creating
    them.
        Ms. Manfra, I would like to first start with you. First
    off, thank you to Google for the good work that--you folks seem
    to be in the lead of mitigating and finding out scams and
    addressing them in the--in the manner that they should be,
    because it is a really serious problem facing all walks of life
    and our constituents and consumers throughout the United
    States, on the international level and on the local level. I
    know I do not have to tell you that.
        You recently highlighted the use of AI to uncover a
    Chinese-linked operation known as Lighthouse which reportedly
    targeted Americans with E-ZPass, postal service, fraudulent
    messages, demanding payments to settle fines, and very other
    common scams that my constituents often encounter. Can you
    describe how Google--what you did to identify this Lighthouse
    operation and what role AI detection played in surfacing those
    scam campaigns?
        Ms. Manfra. Sure. First, let me say that we would be happy
    if you are interested in further deep dive to follow up. But
    generally what we do is we have threat intelligence,
    individuals, and detection capabilities that use advanced
    technologies, including AI, that are increasingly more finely
    tuned and able to spot both scale, but also be able to identify
    networks of organizations that are using our infrastructure and
    our services. So it is a combination of all of these things
    coming together to identify this. Then you know, of course, the
    partnership with law enforcement to ultimately bring this down.
        Mr. Meuser. Well, that is great. Congratulations.
        How do you envision and even how is AI playing a role in
    financial fraud and scam prevention?
        Ms. Manfra. I think AI and ML is--because of--it is a
    scale, and it has been quite helpful for many years now, and
    the newer technologies in AI even more so, again, in being able
    to detect various different networks in addition to being able
    to be very good at filtering out the signal from the noise, if
    you will, which is a huge challenge for fraud detection, and
    also getting very good at being able to reduce the noise around
    false positives, which organizations spend a lot of time--when
    they get an alert, they have to go chase that down, go
    investigate that.
        So as our AI and ML capabilities are getting better, they
    are becoming more targeted. They are reducing that noise that
    financial organizations have to deal with, and they are better
    able to deploy their investigators and their teams----
        Mr. Meuser. Be great if that formula could be socialized
    elsewhere and not be a competitive model within fraud. That
    would be great for that to serve and help others. But I need to
    move on. Thank you very much.
        Mr. Cohen, Nasdaq has been at the forefront of deploying AI
    to markets. Last year Nasdaq introduced Dynamic M-ELO, which
    stands for, as you well know, Dynamic Midpoint Extended Life
    Order, designed to ensure the best trade execution for the
    investor. Can you briefly walk us through it?
        Mr. Cohen. Yes. So Dynamic M-ELO employs a delay that--it
    is almost like a timer that we put on the order so that
    institutions who are trading large blocks of shares can avoid
    price impact when they try to execute those shares. So we take
    in 130 data points to help that institution to identify the
    ideal time to trade. As a result of that, after they trade,
    there is no price movement. The result of that is the
    individual investors that sit behind those large institutions
    get a better price, get better price execution, and benefit
    from that order type. We have grown that order type by 50
    percent because of the success we have had.
        Mr. Meuser. Well, it sounds terrific. Thank you. My time is
    expired. I yield back.
        Now I want to recognize the gentleman from California, Mr.
    Liccardo, for 5 minutes.
        Mr. Liccardo. Thank you very much, Mr. Chair. Appreciate
    the testimony and thank all the witnesses for taking time.
        Ms. Manfra, it is good to have somebody from a local
    neighborhood business, from District 16 here. I should tell Mr.
    Stevens my wife is probably one of the people who crashed the
    system when Zestimate came out. She is a big Zillow addict. Ms.
    Whitmore, I know you employ many of our residents, even though
    you are just next door. So we appreciate having you all here.
        I am brand new here to Congress, but I observed in my 45 or
    so weeks here that there is essentially a paralysis in Congress
    about AI. It seems to be borne of a few basic challenges. One
    is that we generally do not regulate tech very timely or
    effectively anyway. We have been waiting about 30 years for any
    kind of regulation or statute governing digital privacy. Folks
    have been waiting for us to update Section 230 to make social
    media platforms safer, and we have waited about a quarter
    century for that.
        So that is a basic challenge when we approach AI, to be
    sure. I think there are a lot of doubts about whether or not
    Congress is really terribly effective or competent at this. I
    think many of us are still trying to learn to spell AI. We are
    still--I think we recognize this technology has moved very
    quickly; we cannot possibly legislate at the pace of the
    technology that is changing.
        In the absence of this congressional action, we now see 50
    States rushing in. I think this year alone, we have had 36
    States that have approved more than 100 pieces of legislation
    governing AI. I appreciate that is mightily difficult for an
    industry to navigate. Now, the counter or the reaction is we
    need a moratorium on everything States are doing and that is an
    understandable reaction. I do not support a moratorium without
    some kind of sensible Federal regulation to actually supplant
    it, but I certainly understand or appreciate why industry would
    need and want it, given the fact that we are an international
    arena, and certainly China does not have 50 States trying to
    regulate the industry as we do.
        I think--look. As was pointed out, 97 percent of Americans
    do support some regulation in AI, and that is for good reason.
    I would like to see how we can move beyond this binary debate
    that has us stuck between moratorium or no moratorium. I think
    it is preventing sensible legislation and sensible regulation
    from moving forward. I wanted to sort of imagine that we took a
    different approach. I would be interested in any feedback you
    might offer. The approach we would take would focus on
    outcomes. I do not think anybody here--I certainly do not know
    how exactly to regulate algorithms or model weights, and I
    think outcomes are something we can measure. The good news is
    we have laws that regulate outcomes. Whether it is
    discrimination or fraud or anything else, we should be able to
    do that.
        We should be relatively tech neutral when we talk about
    financial service providers who use AI, just like any other
    tool. They should be held to the same standards under, for
    example, Fair Housing Act or anything else that governs whether
    to use AI or not.
        Certainly, large language models (LLMs), we know--since we
    have one representative from that community--you know, we
    recognize there are real challenges here in trying to simply
    get involved in the machines. I do not think most LLM
    developers are particularly good or will admit--I think they
    admit that they are not terribly good at explainability of the
    models. We would not have hallucination if we thought we could
    transparently eliminate it.
        So, perhaps a better approach might be to take away from
    Congress and have an independent commission of some kind,
    industry experts, academics, and others who can set,
    essentially, industry best practices, establish what is the
    technology, whether it is around security, privacy, a host of
    other measures, fraud detection, whatever it might be, here is
    the industry best practices, and if the model meets it, great.
    You have preemption. You also have a standard of care that you
    have met and if you do not, then good luck navigating the
    thicket of 50 different rules.
        I guess I will ask--I will start with Ms. Manfra. Do you
    have any sense about is that something that is viable, in terms
    of how we can move forward with sensible regulation?
        Ms. Manfra. Yes, sir. Absolutely. Thank you for the
    question. I think this is already happening. Industry is
    working together through formal and informal channels at
    Google, with partners across industry. We have created the
    Coalition for Secure AI. So committing to research--and this is
    with Microsoft, with Amazon, with other partners across
    industry--to drive best practices aligned with what we have
    learned internally in the development of large language models
    and others, we have deployed tool kits for our partners and
    guidance based, again, on what we have learned. I do agree with
    you is focusing on outcomes is, I think, the priority and----
        Mr. Meuser. Time has expired. That is an important answer.
    But----
        Ms. Manfra. Sorry.
        Mr. Meuser [continuing]. thank you very much. Gentleman
    yields back.
        Mr. Liccardo. I yield.
        Mr. Meuser. The gentleman from Georgia, Mr. Loudermilk, is
    now recognized for 5 minutes.
        Mr. Loudermilk. Thank you, Mr. Chairman. I thank everybody
    for being here.
        This is an extremely important discussion we need to be
    having, and I think it is timely as well.
        I spent 30 years in the information technology industry, 20
    years in public service in the State legislature and here in
    Congress. When I was first elected to State legislature in
    2005, I was probably one of the few technologists in the State
    legislature at the time, so I took the lead on a lot of policy
    initiatives. One thing that I brought up at the time--if you
    look at the device here, everybody has got one of these, right?
    The least used part of this, ironically, is what we call it.
    The phone, right? That is the legacy part of this. I literally
    talk to people in younger generations than mine that have
    literally never used the telephone feature of this device. Part
    of the reason is, it was not the phone that led to the massive
    technological development that we have seen today. It was the
    internet and it was broadband. Ironically, those were the least
    regulated aspects of technology.
        If you go back and you look at the massive growth in
    technological advancements; it was not over the wired network.
    It was over broadband, the cable television network, which was
    not heavily regulated as the big cell--or the big carriers
    were.
        So with that in mind, what we have seen from history is
    government can seriously stifle innovation if it is overly
    regulated. So, it is kind of a balance that we strike because
    we do have a need to put guardrails up but to leave this
    sandbox available for innovation. I think that is where we need
    to be looking, especially when it comes to tools like AI. There
    are benefits to that, and there is also reason to be fearful of
    the misuse of AI.
        Ms. Manfra, modernization of the Bank Secrecy Act is an
    area of great interest to me. I have put a lot of effort into
    that over the past several years, and especially the
    significant obligations that the Bank Secrecy Act imposes on
    financial institutions to monitor and report potential illicit
    finance activity. It is especially impactful on smaller
    financial institutions.
        Keeping in mind, we have not modernized this thing since
    the early 1970s when the thresholds of $10,000 were put in
    place which, if it had been adjusted, would be $80,000 today.
    How do platforms like Google Cloud help financial institutions
    automate processes for generating currency transaction reports
    (CTRs) and suspicious activity reports?
        Ms. Manfra. We have a couple of different offerings.
    Particularly around anti-money laundering tools that we have
    co-developed with our partners to ensure that they are able--
    and again, this goes back to the questions around transparency
    and explainability of the model to ensure that it is getting to
    the right decisions. But as I mentioned, our customers are
    seeing huge improvement in detection as well as a reduction in
    the false positives as well, which has been hugely impactful to
    actually finding the true risks, the true illicit finance, and
    being able to more effectively deploy their investigative
    resources and more successfully report on suspicious activity.
        Mr. Loudermilk. I think it is really important with
    suspicious activity reports (SARs) especially because what I
    hear from a lot of financial institutions is we just do not
    want to get dinged by the Federal Government, right? So they
    may report a SAR that the local bank president may know is not
    suspicious for that particular customer, but the same action
    may be suspicious for someone else, but to save the integrity
    of the bank from any action by regulators, they will report it
    anyhow.
        Can you talk about how Google Cloud's Anti-Money Laundering
    (AML) AI system uses explainable risk scores and ensures they
    are auditable for regulators?
        Ms. Manfra. Thank you for the question.
        We have invested a lot in explainable AI and so in the end,
    getting to a point where the customer or auditor or regulator
    is able to go back and look at how was a decision arrived at so
    that you can redress of a decision or auditability for the
    purposes of compliance.
        We do offer that and work very closely with our customers,
    especially in financial services, in particular for AML, but it
    is used in other areas as well.
        Mr. Loudermilk. All right. Thank you.
        I yield back.
        Mr. Meuser. The gentleman yields.
        The gentleman from New York, Mr. Torres, is now recognized
    for 5 minutes.
        Mr. Torres. Thank you, Mr. Chair.
        One of the greatest challenges confronting America is the
    utility affordability crisis. The proliferation of data centers
    is one of the drivers--not the only one, but one of the
    drivers--of rapidly rising electricity costs.
        In America, utility rates and returns depend not on
    efficiency or affordability, not on performance, but on capital
    spending. The more a utility spends on infrastructure, the more
    profit it earns and the more customers pay. The electricity
    cost of new data centers is therefore socialized.
        So, given our broken system of utility rate setting in
    America, how do we ensure that the tech companies, rather than
    working-class families and small businesses, are the ones
    shouldering the cost burden of new data centers?
        So Mr. Branch?
        Mr. Branch. Thank you for the question, Congressman.
        Public Citizen believes it is critically important to be
    collaborating with local communities to ensure that they are
    looped into the process of when these data centers may or may
    not be built. We argue, and we are seeing it across the
    country, that a lot of these communities are fighting back
    against these data centers.
        But, just as importantly, we need to ensure that these
    large tax incentives are not provided to these data centers,
    because that ultimately ends up being disbursed throughout
    consumers, and that is why Americans across the U.S. are seeing
    their electricity bills skyrocketing.
        Mr. Torres. You see, I am pro-AI. My whole life is
    integrated with ChatGPT and Gemini. So, for me, the question is
    not whether we should have AI. Of course we should have AI, and
    we need data centers to enable AI. The question is, who should
    pay for it, right? Should the cost be borne by working-class
    families and small businesses, or should it be borne by the
    owners and operators of the data centers?
        What say you?
        Mr. Branch. I think these data centers are worth a lot of
    money, these corporations make a lot of money, and they should
    be footing that bill responsibly.
        Mr. Torres. There are tens of millions of Americans who
    have next to no credit history. TransUnion estimates the number
    at 45 million; FICO, more than 50 million; Experian, more than
    60 million.
        In the Bronx, I have thousands of constituents who have
    paid their rent and utilities on time and in full for decades,
    who maintain steady income and sufficient bank deposits, and
    yet who remain deprived of a credit score.
        Without a credit score you have no access to credit.
    Without credit, you have no access to home ownership and
    without home ownership, you have no means of building equity
    and passing down wealth from one generation to the next. So
    America's exclusionary model of credit scoring has done
    irreparable intergenerational damage to working-class
    communities in places like the Bronx.
        Does anyone here have any thoughts on how we can harness
    the power of AI to build an underwriting system that is more
    predictive, more inclusive, and more representative of the full
    financial reality of working-class Americans?
        Mr. Stevens. I will take a stab, Congressman. I really
    appreciate the question.
        Of the 4 million families that are going to buy for the
    first time this year, most of them are previous renters. At
    Zillow we are big fans of working with TransUnion, Equifax, and
    others to consider those on-time rent payments as part of the
    larger equation.
        We believe that AI is that ability to look at disparate
    sources of information that otherwise a loan officer or
    underwriter would not have time to consider, and then
    ultimately leave it to them as humans to make the right
    decision on what to underwrite.
        Mr. Torres. Because we should harness the power of AI not
    only to disrupt but to democratize----
        Mr. Stevens. Exactly. Equal access.
        Mr. Torres [continuing]. finance and access to credit is
    exhibit A.
        The Federal Government is drowning in vast oceans of data.
    For example, in Fiscal Year 2024, FinCEN received a staggering
    4.7 million suspicious activity reports, an average of 12,870 a
    day.
        Now, there is no agency in the Federal Government that has
    sufficient human resources to thoroughly review millions upon
    millions of reports, and so valuable information can easily
    disappear into the black box of bureaucracy.
        You know, AI can process vast quantities of data that the
    human mind cannot process. It can recognize patterns that the
    human mind cannot readily recognize.
        Do any of you have thoughts on the role of AI in
    facilitating not only finance but also financial regulation and
    oversight?
        Mr. Cohen. So Nasdaq employs AI in its surveillance
    solution, so we are trying to root out market abuse through our
    surveillance solution, and we use AI to do that.
        One great example--and it just keys off of what you said--
    is, a human investigation analyst combs through many, many
    possible market manipulations. How do they know which ones to
    pay attention to? How do they know which ones to let go?
        We use AI to help you, number one, do all the manual tasks,
    the series of tasks that you would otherwise go through to make
    that analysis, and we provide you with a risk score, so then
    you can focus on the bigger issues, the most important issues
    affecting our markets. That is how we use AI.
        We always--again, we have the human making the complex
    judgment decision around that, but we are just reducing the
    manual workload to get to that decision.
        Mr. Torres. Thank you.
        Mr. Timmons [presiding]. Thank you.
        I now recognize myself for 5 minutes.
        Thank you to the witnesses for being with us today.
        Like other areas under the Digital Assets jurisdiction,
    artificial intelligence is a transformative technology that
    will shape how American companies, consumers, and investors
    engage with our financial system. If the United States is going
    to remain a global leader in innovation, we must adopt clear
    and harmonized rules that support technological progress while
    also protecting consumers and preserving market integrity.
        Industry leaders are increasingly concerned about the
    growing patchwork of State laws related to artificial
    intelligence. These State requirements often conflict with one
    another, whether in the form of impact assessments,
    documentation standards, or definitions of high-risk systems.
        For firms that operate across the country, these
    inconsistencies can create significant operational challenges,
    especially when artificial intelligence supports critical
    functions such as fraud detection and cyber defense.
        Without a unified Federal framework that replaces
    duplicative and contradictory State rules, we risk higher
    compliance costs, slower innovation, and weaker protection for
    consumers.
        Ms. Manfra, Google Cloud supports financial institutions
    that rely on artificial intelligence for essential operations.
    What regulatory obstacles or outdated requirements are
    hindering firms from deploying artificial intelligence
    responsibly at scale, particularly when those challenges grow
    under inconsistent State laws?
        Ms. Manfra. Thank you for the question.
        I would say, first of all, it is important to be able to
    have a national framework that is addressing all of the issues
    that we have been talking about and not have that patchwork
    that does pose undue burden and unduly impacts smaller and
    medium-size players and creates higher barriers to entry that
    would otherwise be available to them with all this new
    technology. So I think that is very important.
        I also think it is important to recognize that existing
    laws and frameworks are in place and apply to these new
    technologies, but to be able to provide spaces for financial
    institutions to innovate and to test how these tools might be
    applicable into higher-risk areas is also very important.
        Mr. Timmons. Thank you for that.
        A follow up question. Many State proposals include
    different documentation and testing requirements for similar
    artificial intelligence systems. From an operational point of
    view, how difficult is it for a cloud provider or a financial
    institution to adjust model governance and controls to comply
    with several conflicting regimes?
        Ms. Manfra. It is very difficult, and it would be
    incredibly resource-intensive onto an organization in putting
    personnel and other resources into positions that are not
    enabling the core business of that organization.
        Mr. Timmons. I feel like we have seen this before. We
    watched Europe pass the General Data Protection Regulation
    (GDPR), and then now we have the California Consumer Privacy
    Act (CCPA), and we have all these different data privacy and
    data security standards. I think everyone agrees that we need
    to have a Federal standard that preempts everything and leads
    in the global economy, and we are not learning from past
    mistakes.
        So not only do we need to lead here in Congress on AI, but
    we also need to get with the program and address the patchwork
    framework that is costing incredible amounts of money for
    compliance for businesses. I feel like the larger businesses
    have a better capacity to deal with the patchwork frameworks
    for cybersecurity, but in AI it is just going to stifle growth
    and innovation. We are competing in the global economy, and we
    cannot lose this fight.
        Although there are real challenges in establishing clear
    rules for artificial intelligence, there are also significant
    opportunities. AI can strengthen the safety and soundness of
    our financial system by improving anomaly detection,
    accelerating the identification of fraud, enhancing
    cybersecurity, and enabling faster, more accurate risk
    analysis.
        At the same time, compliance expectations continue to rise
    for institutions of every size. Smaller banks, credit unions,
    and broker-dealers feel this burden most. AI tools can help
    reduce manual compliance work, streamline reporting, and allow
    institutions to devote more time and resources to customer
    service and innovation.
        Mr. Cohen, Nasdaq relies on AI to support its market
    operations and oversight responsibilities. From your
    perspective, how is AI helping strengthen the integrity,
    stability, and resilience of the U.S. financial system?
        Mr. Cohen. All markets operate on the mandate of trust and
    investor confidence. If we compromise either of those, then we
    will not have the standing we have today in our markets.
        So we use our surveillance solution, and we employ AI in
    it, to essentially allow investigation analysts to scale up,
    focus on the high-value activities, and also focus on the
    sophisticated manner in which market abuse is being conducted
    today.
        What we are seeing is, market abuse today, it is the same
    outcome, but the techniques used are hard for any single human
    or a series of humans to detect. What used to be, if you will,
    clear patterns are now noise. To make them and convert them
    back into signals, we have been using AI to help, if you will,
    regulators, exchanges, and broker-dealers around the globe
    protect the integrity of their markets.
        Mr. Timmons. Thank you for that.
        The gentleman from Texas, Mr. Green, is now recognized for
    5 minutes.
        Mr. Green. Thank you, Mr. Chairman.
        I thank the witnesses for appearing.
        I especially thank the chair and the ranking member for
    allowing me this privilege to ask this question.
        I have intelligence indicating that over the next 10 years
    approximately 100 million jobs will be lost to AI. I know that
    is a large number. Perhaps you have a number that is somewhat
    different but let us just assume that this number is accurate.
        My question is, how do we provide for 100 million jobs
    being lost, in terms of how they will impact people? Jobs
    usually have people associated with them. So how do these
    people maintain their lifestyles? Will there be some emolument
    accorded them by way of the Federal Government?
        Let us start with you, Mr. Branch, if you do not mind, and
    we will go to your right and down the line.
        Mr. Branch. Thank you for the question, Congressman.
        If I had the answer to that, I would be a very rich guy,
    because there is currently not really an answer to this
    solution, and that is the big problem.
        A lot of these tech companies are moving full-fledged
    forward with this theory of having AI take up a variety of
    jobs, but there is no solution for when those folks end up
    being laid off or unemployed.
        Mr. Green. All right.
        Next, please?
        Ms. Whitmore. Thank you, Congressman.
        So I work in the field of cybersecurity, and we have
    historically had a pretty significant job shortage. I think,
    right now, any estimates would probably be over 1 million jobs
    within our industry that are not filled and that is due to a
    lack of skills with that labor shortage.
        In that regard, I think we are looking at cybersecurity
    usage of AI as really being able to put on an exoskeleton for
    our defenders, making them more capable, being able to conduct
    tasks that are much more efficient, and defend our networks in
    a way that we are not able to do at human scale today.
        Interestingly, in the field of cybersecurity, I think we
    are actually providing a lot more job satisfaction to our
    industry analysts and defenders, who primarily deal in very
    repetitive tasks.
        Mr. Green. Well, I appreciate your answer, but, if I may, I
    am posing a different question. You have answered a question
    associated with how well and how efficacious your business
    model is. My question has to do with the people who are
    displaced. How will they make a living?
        Ms. Whitmore. I think that is a great question, but as a
    cybersecurity expert, I am not sure that I am in the best
    position to answer that, so I may turn it to Mr. Stevens.
        Mr. Green. All right.
        Mr. Stevens. Thank you, Congressman. I will talk briefly
    about how important humans are to housing and then also offer
    how we can improve building the next-generation U.S. workforce.
        In housing, ultimately, where people are going to live is a
    very human-based decision. We find time and time again; people
    want a real estate agent to sit across the table and give them
    advice on if they are making a good decision. So, in housing,
    we continue to see humans being very important.
        Outside of that, I think something that has come up briefly
    is, we really need to invest more in training the next
    generation here at home to work on these models and help really
    improve them over time. And that is----
        Mr. Green. Excuse me, if I may.
        Mr. Stevens. Of course.
        Mr. Green. A hundred million people, 100 million jobs--100
    million.
        Mr. Stevens. I have not seen that stat, but I certainly
    believe, if we gave equal access----
        Mr. Green. Okay.
        Mr. Stevens [continuing]. of this technology, that----
        Mr. Green. Okay. Thank you. I think that your model is a
    good one, but we are talking about 100 million.
        Mr. Stevens. Yep.
        Mr. Green. Yes, sir?
        Mr. Cohen. It is a great question. The first thing that we
    have to do is be honest about the workforce transition that we
    are going to go through as a country.
        What we are doing in terms of our part is, we are trying to
    make sure that we upskill/reskill employees and we are actually
    creating roles of the future. So we are trying to do our part
    to make sure that our workforce comes with us into the next
    generation.
        Then you also have to believe in the power of AI to
    generate new business models, new opportunities and that is why
    it is important that we capture them here so that we can make
    sure that those individuals that----
        Mr. Green. Okay.
        Mr. Cohen [continuing]. may be displaced have opportunities
    in the future.
        Mr. Green. I have to go to the last speaker.
        Please, ma'am.
        Ms. Manfra. Thank you, sir. It is a very important topic
    and one that we care a lot about.
        Similar to our colleagues, we are very invested in ensuring
    our own workforce has the tools to be able to be successful.
    This includes our engineers using AI to be more productive.
        I would say more broadly is that we are seeing more job
    creation, we are seeing businesses grow as a result. It is
    important that we invest in education at the lowest levels of--
    into elementary school to make sure our kids are----
        Mr. Green. The chairperson is admonishing me.
        Let me thank you, Mr. Chairman, and simply indicate that
    this is a question that we have to give some serious
    consideration to.
        Mr. Timmons. The gentleman's----
        Mr. Green. A hundred million jobs.
        Mr. Timmons [continuing]. time has expired.
        Mr. Green. I thank you, Mr. Chairman. I yield back.
        Mr. Timmons. The gentlewoman from California, Mrs. Kim, is
    now recognized for 5 minutes.
        Mrs. Kim. Thank you, Chairman and Ranking Member, for
    convening our hearing today.
        Earlier this year, the State of California, where I am
    from, passed SB 54 that would unfairly regulate AI and impose
    heavy compliance burdens on companies. Now States across the
    country are looking to this California model as a basis for
    developing their own artificial intelligence regulations.
        Therefore, there is an urgency for Congress to establish a
    Federal framework for AI. That is why I support legislation
    like Chairman French Hill's Unleashing AI Innovation in
    Financial Services Act that would create Federal regulatory
    sandboxes.
        Ms. Manfra, if that bill is signed into law, will those
    Federal protections be respected if you are simultaneously
    fighting a patchwork of restrictive State laws, particularly
    the anti-innovative regulatory framework in my home State of
    California?
        Ms. Manfra. Thank you for the question, ma'am.
        Yes, we absolutely think the need for a national framework
    is critical and having to navigate a patchwork of regulations
    does not help either our company or our customers.
        Mrs. Kim. Thank you.
        What has made America a leader compared to other countries
    is our philosophy of trying first, rather than regulation
    first. So the longer we fail to act, the regulatory standard
    will be set at the State level, not--and the innovation will
    suffer.
        So thank you.
        Another area where AI has high value is in its application
    in the field of cybersecurity.
        So I want to ask you, Ms. Whitmore: By leveraging AI, how
    have banks been able to save time and protect themselves
    through cybersecurity efforts?
        Ms. Whitmore. Thank you for the question.
        So I think one of the most measurable areas we see with
    financial institutions is actually the applications to
    cybersecurity and, in particular, measuring outcomes in how
    quickly we detect attacks and how quickly we respond to them.
        As we have seen the use of AI increase, we have seen
    ransomware attacks that have been completed from initial part
    of the attack to stolen data or encryption of data in 25
    minutes. We have worked with customers in the financial
    services industry who have taken a mean time to detect from 24
    hours to as little as 10 minutes.
        So those types of measurable outcomes are incredibly
    critical in our ability to defend financial services networks.
        Mrs. Kim. Thank you.
        You know, scams are an issue that I hear constantly about
    from my constituents. We know that modern financial scams are
    rarely isolated to a single app. They may start with a text on
    a phone, move to a fraudulent website, and follow up with an
    email.
        Ms. Manfra, let me come back to you. How does Google
    connect the technical dots in the background to prevent those
    foreign scammers?
        Ms. Manfra. Thank you.
        We have invested a lot in this space and seen a lot of very
    positive results going to the ability to identify a fourfold
    increase in activity that we are then able to detect and take
    down on a variety of our platforms.
        We do not want fraudulent or scam information that is
    sitting on our platforms or that is impacting our customers.
    So, every day, we are identifying millions and taking down
    millions of fraudulent ads, preventing scams, preventing
    phishing through the use of AI and ML technologies.
        Mrs. Kim. So let us say, if AI detects a threat in Gmail,
    how does that transfer across Android or Chrome to ensure that
    it is neutralized across a person's accounts?
        Ms. Manfra. Well, in some cases we use common tools, and
    many of our platforms and tools reside on common
    infrastructure, so oftentimes we are able to leverage that
    scale to be able to identify commonality.
        In other cases, we do have to use unique tools, but we are
    sharing the outcomes, the detections, the things that we are
    finding, to make sure that other products and platforms are
    available to use that.
        Mrs. Kim. Uh-huh. Well, thanks for your work on that.
        Let me quickly shift gears to AI. It has a large role to
    play in preventing financial crime as well.
        So, Mr. Cohen, I want to give you a chance to answer. How
    does Nasdaq utilize AI to address compliance and prevent
    financial crime?
        Mr. Cohen. So we were saying earlier that we have a leading
    financial crime platform. It is cloud-native, AI-enabled and
    what we do is use consortium data to detect patterns that no
    single institution can do on its own. We have 725 million
    accounts.
        What we are able to do is reduce false positives and help
    investigators focus on real crime and identify real crime----
        Mr. Timmons. The gentlelady's----
        Mr. Cohen [continuing]. that is otherwise a drain on----
        Mr. Timmons. The gentlelady's time has expired. Sorry.
        I now recognize the gentleman from New Jersey----
        Mrs. Kim. Thank you.
        Mr. Timmons [continuing]. Mr. Gottheimer, for 5 minutes.
        Mr. Gottheimer. Thank you, Mr. Chairman.
        AI is being used, as we all know, to detect fraud and stop
    seniors from getting manipulated, those especially who are
    targeted by scams involving deepfakes and other AI-powered
    tools.
        According to the Federal Trade Commission (FTC) report
    released just last week, adults over 60 lost $2.4 billion to
    scams and fraud in 2024. Alarmingly, this is the same report
    that found that older adults who reported losing more than
    $100,000 increased more than five-fold between 2020 and 2024.
        I am very grateful to Chairman Hill and the Capital Markets
    Subcommittee chairwoman, Ann Wagner, for recognizing the
    importance of my Senior Security Act and including it in the
    INVEST Act, which the House will vote on this week, to put a
    new senior-protection-focused cop on the beat at the SEC.
        Ms. Whitmore and Mr. Cohen, what specific safeguards should
    be considered to prevent and detect elder financial fraud and
    abuse related to AI and how should industry coordinate with
    Congress and regulators and law enforcement and others to
    ensure those protections keep pace with rapidly evolving AI-
    enabled threats?
        Ms. Whitmore. Thank you, sir, for the question.
        So I think specific to the demographic you are talking
    about, education is a huge concern and really an issue. If we
    can raise awareness that these scams are being conducted and
    can create a level of education that does not necessarily exist
    that widespread today, I think that really helps with that.
        Additionally, what we are also talking about is visibility
    at large scale to protect the transactions on the back end and
    that is where organizations like ours truly focus, is looking
    at the packets that are moving. So, in your specific use case,
    we will be looking at the financial services transactions and
    ensuring that our detection capabilities are not facilitating
    those types of crime.
        Mr. Gottheimer. Mr. Cohen.
        Mr. Cohen. Thank you.
        There are two important elements here.
        There is the public-private partnership. We need
    information-sharing, because these scams scale incredibly fast
    in the world that we live in today. It should not be that the
    elderly find out on Facebook from their friends about a scam.
    We should have this information-sharing at a scaled level to
    make sure that we can prevent it and not just deal with it
    after the fact.
        The second thing is, we can use AI to detect it at scale.
    Right now, what we are doing is, we are coming into it, and we
    are trying to deal with it on a one-off basis. We need the
    tools and the automation to be able to stop it the second we
    see it and not let it propagate through the system, and AI can
    help us do that.
        Mr. Gottheimer. Are we seeing that?
        Ms. Manfra, are you taking steps to actually make sure that
    happens, from a Google perspective?
        Ms. Manfra. Absolutely. We are identifying, blocking, and
    taking down fraudulent use of our platform--fraudulent
    postings, fraudulent ads, scams that are targeting various
    customers, phishing attempts. Gmail alone blocks 180 million
    phishing attempts every day and so we are investing in AI as a
    critical component to being able to do that.
        Mr. Gottheimer. Thank you very much.
        Regulators have used innovation hubs and sandboxes to
    encourage responsible experimentation in other areas of
    fintech. I am personally very focused on this area.
        For the entire panel, what features and guardrails do you
    think are essential for AI pilots to truly encourage innovation
    and protect consumers and communities in this space?
        Josh, do you want to start?
        Mr. Branch. Thank you for the question, Congressman.
        Well, I think these sandboxes need to be time-limited; they
    need to be specific, and I think those are two of the primary,
    sort of, components that are at least not seen currently in the
    sandbox offerings.
        Mr. Gottheimer. Ms. Whitmore.
        Ms. Whitmore. Thank you.
        So I think we look at it from three categories.
        First is defining a risk-based approach with consistent
    definitions. The focus is on actual threats and not necessarily
    those that are hypothetical and those, certainly, that are more
    high-risk applications.
        Second would be distinguishing between the developers and
    the deployers.
        Third is making sure that defense is a priority, so having
    a voluntary standards, a framework that then looks at making
    sure we are protecting our most critical information and not
    getting it in the hands of our adversaries.
        Mr. Gottheimer. Thanks.
        Nick.
        Mr. Stevens. Thank you.
        I would say briefly that laws stay enforced, that we test
    responsibility in these sandboxes. Independence checks still
    happen, bias testing and the like. Last, I would add,
    transparency is obviously paramount.
        Mr. Gottheimer. Yes.
        Mr. Stevens. Regulatory reviews, consistent----
        Mr. Gottheimer. Tal, you agree?
        Mr. Cohen. Controlled, targeted, and time-boxed, and do not
    allow for circumvention of approval process.
        Mr. Gottheimer. Jeanette.
        Ms. Manfra. I agree with Mr. Cohen and the previous
    statements.
        Mr. Gottheimer. Great.
        Thanks so much.
        I yield back.
        Mr. Timmons. The gentleman from York, Mr. Garbarino, is now
    recognized for 5 minutes.
        Mr. Garbarino. Thank you, Chairman.
        Thank you all for being here today.
        As chairman of the House Committee on Homeland Security,
    one of my top priorities is reauthorization of the
    Cybersecurity Information Sharing Act of 2015, also known as
    CISA 2015. It is a vital framework that allows for voluntary
    exchange of cybersecurity information between the Federal
    Government and private entities.
        From energy companies to major financial institutions on
    Wall Street, the private sector's first line of defense against
    malicious cyber adversaries in CISA 2015 is critical to
    ensuring those threats are not realized.
        Ms. Manfra, as someone who previously served as Assistant
    Secretary for Cybersecurity at CISA, how important is the
    reauthorization of CISA 2015 to ensure our cybersecurity needs
    are met to combat AI-powered threats?
        Ms. Manfra. It is very important, sir.
        Mr. Garbarino. Can you give me some examples of what could
    happen in the financial services space if CISA 2015
    authorization were to lapse--like it did under the shutdown,
    but it would lapse for a longer time?
        Ms. Manfra. The framework that the legislation provides
    CISA to be able to share information in protected spaces with
    its partners, to include financial services, means that they
    are actively reducing risks in those protected conversations
    and information-sharing environments and so not having that is
    detrimental to those partnerships.
        Mr. Garbarino. Yes. I mean, financial services companies
    are hit all the time with cyber-attacks. They learn about
    vulnerabilities before--because they are the front line of
    defense. They cannot share that information with the Federal
    Government. The Federal Government cannot then share it with
    everybody else. Which means those vulnerabilities cannot be
    fixed and we are just facing more and more threats, correct?
        Ms. Manfra. Correct.
        Mr. Garbarino. Thank you.
        Ms. Whitmore, is our existing financial data privacy and
    cybersecurity framework well-suited for the current era of AI,
    or are reforms needed?
        Ms. Whitmore. I think we can leverage existing regulation,
    but what we are really advocating for, moving forward, are
    actions that benefit the defenders and not the attackers.
        So the more that we look at streamlining regulation and
    harmonizing it, the better we make it for defenders to really
    focus on truly firefighting and not filing the complexities of
    too much paperwork.
        Mr. Garbarino. Yes, harmonization. I had lunch with Jamie
    Dimon once, and he said more than 50 percent of--his
    cybersecurity employees spent more than 50 percent of their
    time on compliance instead of actual cybersecurity defense,
    which is wild to me.
        Ms. Whitmore. Yes.
        Mr. Garbarino. So harmonization is another thing that I
    know I have spoken to Chairman Hill about as something we
    should focus on here as well.
        Switching gears a bit, the mortgage industry has spent
    decades under Federal oversight, starting with the Great
    Depression, when agencies like the Federal Housing
    Administration (FHA) and Fannie Mae were created to stabilize
    the housing market and expand access to home ownership, and
    intensifying again after the 2008 financial crisis through the
    Dodd-Frank Act and then the creation of the CFPB. Today,
    Federal rules shape nearly every part of the mortgage process.
        Mr. Stevens, as AI enters mortgage lending, should we
    extend existing Federal financial services regulatory
    frameworks to cover AI?
        Mr. Stevens. Definitely. Any law or regulation that humans
    have to follow, AI definitely should too.
        I think, to the first part of your question, AI has that
    ability to take on a lot of the administrative burden that
    slows everyone else down. If you shadow a loan officer, they
    spend hours just collecting documents, copying and pasting
    data. AI can do that so they can focus on the right decisions.
        Mr. Garbarino. Absolutely.
        So what should Congress do to ensure--or maybe not do--to
    ensure that you can continue to innovate here?
        Mr. Stevens. I think national right-sized frameworks. I
    mean, we rely a lot on the risk AI management framework, and so
    anything that has a consistent baseline for all of our States
    will help us.
        Mr. Garbarino. Are there any--and maybe it is the States
    but are there any regulatory structures that are currently
    standing in the way of AI delivering on the promise to expand
    access to affordable housing?
        Mr. Stevens. It is something we could certainly follow up
    on.
        I think, in housing, a lot of the regulations were written
    before the age of the internet. For example, we have a whole
    database at Zillow that is just mindful of when e-sign is
    allowed depending on the State you are in. So I would say a
    comprehensive review of some of those older frameworks would be
    useful.
        Mr. Garbarino. Yes. If you could get us more ideas, that
    would be----
        Mr. Stevens. Yep. Would love to follow up.
        Mr. Garbarino [continuing]. more details, that would be
    great.
        From your vantage point, Mr. Stevens--last question--
    building consumer products, how can technology and
    modernization, not more regulation, actually help lower the
    costs for buyers and renters?
        Mr. Stevens. Yes. Both the efficiency, we were talking
    about, with loan officers and real estate agents. I would say
    also, the democratic--democratization of data access.
        If you have a nationwide framework, that means we can give
    guidance about what home to buy across State lines, which many
    consumers consider all the time.
        Mr. Garbarino. Uh-huh.
        Mr. Stevens. If it is a patchwork, that means we have to
    build very custom models depending on the State you are looking
    in right now and that kind of efficiency helps people make
    better decisions at the right price.
        Mr. Garbarino. Thank you very much.
        I am out of time. I yield back.
        Mr. Timmons. Thank you.
        The gentleman from Nebraska, Mr. Flood, is now recognized
    for 5 minutes.
        Mr. Flood. Thank you, Mr. Chairman.
        I really think about AI in, really, two buckets: Number
    one, as it relates to entities that are already regulated in
    the financial services lane, whether it be by the Fair Housing
    Act, the Fair Credit Reporting Act, the Gramm-Leach-Bliley Act,
    or statutes that apply to financial actors.
        Basically what I tell people is, if we find you
    discriminating, we will fine you, and we will enforce the law.
    In other words, if you are using AI to break consumer
    protection law or discrimination law, we need to ensure that
    our regulators are equipped to track and identify that.
        I have this idea that someday we are going to begin an
    enforcement action, and the regulated entity will say, ``Well,
    we did not intend to do that. It was all in our AI model, and
    this is the outcome.'' Well, we are not going to fall for that.
    Like, if you break the law, you broke the law, and you had a
    model that did it.
        Number two, we need to work together to identify these bad
    actors, these folks that use generative AI to scam people out
    of money, to let fraudsters fool people and steal their cash,
    and law enforcement needs to be able to respond to these
    threats. We absolutely need the help of technology companies in
    preventing AI from being used by those who do it to lie, cheat,
    and steal.
        That is really where I come from on this.
        To all of you on the panel today, can you just describe the
    intentions with the--the interactions, I should say, with the
    regulators you work with on AI, both at the Federal and the
    State level? Do you see in those regulators sufficient
    expertise in your counterparts in government to make you feel
    they are up to the task of overseeing this changing landscape?
    In other words, as you engage with regulators, do you feel like
    they have the tools they need to do the job, given the changed
    landscape with artificial intelligence?
        We will start here.
        Ms. Manfra. We will say, those that we interact with,
    whether as customers or partners or regulators, I do think that
    they show a willingness and an openness to learn. I do think
    that, like many other organizations, they do need more skilling
    in this space to be able to best focus on the outcomes but also
    be able to apply that to new technology.
        Mr. Flood. Mr. Cohen.
        Mr. Cohen. Just to frame your question, public-private
    partnerships are incredibly important, and they are what will
    keep us ahead.
        Two things, if I could take the opportunity. The
    Cybersecurity Information Act that needs to be extended so that
    we continue that on the cybersecurity side.
        In terms of the regulators and the way that we work
    together, we do feel like they appreciate the issues; there is
    a deep level of understanding around the issues. The one thing
    we worry about is regulatory arbitrage. So you need to make
    sure that, as we go to the regulators, we do not want to go to
    the regulators that understand the least to try to get
    something approved.
        That is why at a Federal level we are going to need some
    clarity there, so there are no regulatory arbitrage and people
    do not try to go to the regulator that understands the least.
        Mr. Flood. Well-said.
        Mr. Stevens. Thank you. I appreciate the question.
        I would say, certainly education could help when we partner
    at the Federal and State level but even more so, we see
    successful partnerships when they are outcome-focused, when
    they are customer-first, when they are transparent.
        I could sit down and explain all the fraud models behind
    Zillow Rentals, but if we just agree that no customer should
    see a fraudulent listing or have a fraudulent payment, then we
    can collaborate even more efficiently.
        Ms. Whitmore. Thank you for the question.
        So not only do we work on behalf of public-private partners
    within cybersecurity and all our counterparts on the government
    side, but also on behalf of our customers when we are
    investigating these major cyber breaches. They are then
    challenged so many times with the different regulatory
    frameworks that they need to report these breaches in.
        I think the one area I would really like to share there is
    that more complexity never benefits the defenders who are
    trying to fight the fire and stop the breach while they are
    also worried about the paperwork. So that really benefits the
    attackers. I think the more we can streamline, the better off
    we are going to be.
        Mr. Flood. Mr. Branch.
        Mr. Branch. Thank you, Congressman, for the question.
        Many agencies have lost jobs due to various layoffs, and so
    these agencies are playing catchup and are in a bit of a
    difficult position to try to enforce some of the laws but,
    also, President Trump's AI action plan has predominantly been a
    deregulatory effort. As such, the agencies are not really
    empowered to enforce some of the laws that exist.
        The last thing that I would say, as well, is that some of
    the sandbox bills that have been proposed allow the companies
    to sort of self-regulate. So, from that end of things, these
    companies are sort of guarding their own laws, and it is not
    allowing the regulators to essentially do the jobs that they
    are required to do.
        Mr. Flood. Thank you very much.
        With that, I yield back.
        Mr. Timmons. Pursuant to the previous order, the chair
    declares the committee in recess, subject to the call of the
    chair. We will reconvene immediately after the floor vote
    series.
        The committee stands in recess.
        [Recess.]
        Mr. Fitzgerald [presiding]. The Committee on Financial
    Services will come to order.
        I would like to thank all the witnesses for their testimony
    today.
        Without objection, all members will have 5 legislative days
    to submit additional written questions for the witnesses to the
    chair. The questions will be forwarded to the witnesses for
    their response.
        Witnesses, please respond no later than January 14, 2026.

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

        Mr. Fitzgerald. The hearing is adjourned.

        [Whereupon, at 2:38 p.m., the committee was adjourned.]

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