[House Hearing, 119 Congress]
[From the U.S. Government Publishing Office]
UNLOCKING THE NEXT GENERATION OF AI
IN THE U.S. FINANCIAL SYSTEM
FOR CONSUMERS, BUSINESSES,
AND COMPETITIVENESS
=======================================================================
HEARING
BEFORE THE
SUBCOMMITTEE ON DIGITAL ASSETS, FINANCIAL
TECHNOLOGY, AND ARTIFICIAL INTELLIGENCE
OF THE
COMMITTEE ON FINANCIAL SERVICES
U.S. HOUSE OF REPRESENTATIVES
ONE HUNDRED NINETEENTH CONGRESS
FIRST SESSION
__________
SEPTEMBER 18, 2025
__________
Serial No. 119-43
Printed for the use of the Committee on Financial Services
[GRAPHIC NOT AVAILABLE IN TIFF FORMAT]
www.govinfo.gov
__________
U.S. GOVERNMENT PUBLISHING OFFICE
63-432 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
------
SUBCOMMITTEE ON DIGITAL ASSETS, FINANCIAL TECHNOLOGY, AND ARTIFICIAL
INTELLIGENCE
BRYAN STEIL, Wisconsin, Chairman
TOM EMMER, Minnesota, Vice Chairman STEPHEN F. LYNCH, Massachusetts,
BILL HUIZENGA, Michigan Ranking Member
WARREN DAVIDSON, Ohio BRAD SHERMAN, California
JOHN W. ROSE, Tennessee BILL FOSTER, Illinois
WILLIAM R. TIMMONS, IV, South JOSH GOTTHEIMER, New Jersey
Carolina AYANNA PRESSLEY, Massachusetts
MARLIN STUTZMAN, Indiana RITCHIE TORRES, New York
BYRON DONALDS, Florida SYLVIA R. GARCIA, Texas
ZACHARY NUNN, Iowa BRITTANY PETTERSEN, Colorado
TROY DOWNING, Montana SAM LICCARDO, California
MIKE HARIDOPOLOS, Florida
TIM MOORE, North Carolina
C O N T E N T S
----------
Thursday, September 18, 2025
OPENING STATEMENTS
Page
Hon. Bryan Steil, Chairman of the Subcommittee on Digital Assets,
Financial Technology and Inclusion, a U.S. Representative from
Wisconsin...................................................... 1
Hon. Stephen Lynch, Ranking Member of the Subcommittee on Digital
Assets, Financial Technology and Inclusion, a U.S.
Representative from Massachusetts.............................. 2
STATEMENTS
Hon. French Hill, Chairman of the Committee on Financial
Services, a U.S. Representative from Arkansas.................. 4
WITNESSES
Mr. Christian Lau, Co-Founder and Chief Product Officer, Dynamo
AI............................................................. 4
Prepared Statement........................................... 7
Dr. David Cox, Vice President, AI Models, IBM Director, MIT-IBM
Watson AI Lab.................................................. 18
Prepared Statement........................................... 20
Mr. Matthew Reisman, Director, Privacy and Data Policy, Center
For Information Policy Leadership.............................. 26
Prepared Statement........................................... 28
Mr. Daniel Gorfine, Founder & CEO, Gattaca Horizons; Former Chief
Innovation Officer & Director of LabCFTC....................... 30
Prepared Statement........................................... 32
Dr. Nicol Turner Lee, Senior Fellow and Director, Center for
Technology Innovation, Brookings Institution................... 50
Prepared Statement........................................... 52
APPENDIX
MATERIALS SUBMITTED FOR THE RECORD
Warren Davidson:
Op-ed for the Daily Caller................................... 96
Hon. Maxine Waters:
America's Credit Unions...................................... 99
The National Fair Housing Alliance (NFHA).................... 103
Public Citizen............................................... 111
RESPONSES TO QUESTIONS FOR THE RECORD
Written responses to questions for the record from Mr. Christian
Lau............................................................ 113
Written responses to questions for the record from Dr. David Cox. 116
Written responses to questions for the record from Mr. Matthew
Reisman........................................................ 118
Written responses to questions for the record from Dr. Nicol
Turner Lee..................................................... 123
LEGISLATION
H.R. 4801, the Unleashing AI Innovation in Financial Services Act 127
H.R. 2152, the Artificial Intelligence Practices, Logistics,
Actions, and Necessities (PLAN) Act............................ 148
H.R. 1734, the Preventing Deep Fake Scams Act.................... 153
UNLOCKING THE NEXT GENERATION OF AI
IN THE U.S. FINANCIAL SYSTEM
FOR CONSUMERS, BUSINESSES,
AND COMPETITIVENESS
----------
Thursday, September 18, 2025
U.S. House of Representatives,
Committee on Financial Services,
Washington, DC.
The subcommittee met, pursuant to notice, at 2:12 p.m., in
room 2128, Rayburn House Office Building, Hon. Bryan Steil
[chairman of the subcommittee] presiding.
Present: Representatives Steil, Hill, Huizenga, Davidson,
Rose, Timmons, Nunn, Downing, Haridopolos, Lynch, Waters,
Foster, Pressley, Torres, Garcia, Pettersen, and Liccardo.
Chairman Steil. The Subcommittee on Digital Assets,
Financial Technology, and Artificial Intelligence will come to
order.
Without objection, the chair is authorized to declare a
recess of the committee at any time.
The hearing is titled ``Unlocking the Next Generation of AI
in the U.S. Financial System for Consumers, Businesses, and
Competitiveness.''
Without objection, all members will have 5 legislative days
within which to submit additional material to the chair for
inclusion in the record.
I now recognize myself for 4 minutes for an opening
statement.
OPENING STATEMENT OF HON. BRYAN STEIL, CHAIRMAN OF THE
SUBCOMMITTEE ON DIGITAL ASSETS, FINANCIAL TECHNOLOGY AND
INCLUSION, A U.S. REPRESENTATIVE FROM WISCONSIN
Artificial intelligence is rapidly changing industries
across the world. Few sectors are more prepared and more
impacted than financial services. For decades, our financial
institutions have been at the forefront of development and
deploying AI technology, from algorithmic trading to machine
learning systems used in risk management to combating fraud in
better and faster ways.
The rise of generative AI represents the next
transformative step, which could bring new efficiencies, new
opportunities, and potential new risks to our financial
markets. This subcommittee has demonstrated strong leadership
in charting a path forward for transformative technologies,
including digital assets, most recently through the CLARITY Act
and the Guiding and Establishing National Innovation for U.S.
Stablecoins (GENIUS) Act.
Today, we turn our attention to artificial intelligence,
examining how it is being deployed in financial markets and
assessing whether the current regulatory framework is prepared
to keep pace.
The United States has long been a hub for financial
innovation, and we must ensure our policies support responsible
AI adoption, not stifle it, as seen during the Biden-Harris
Administration.
Recently, the Trump Administration released its AI Action
Plan, which emphasizes American AI leadership across all
sectors. We must look to how we can enhance American leadership
and competitiveness in financial technology.
It is essential that regulations strike the right balance,
fostering innovation while ensuring investor protection and
market integrity. Our financial regulators must be cognizant of
that technology it uses, and Congress must provide the clarity
to encourage responsible development here at home. It is
paramount our markets are not left behind in the global race
for AI leadership.
We are fortunate to have with us today a panel of esteemed
experts who bring a wealth of knowledge and experience in
artificial intelligence and its deployment in the financial
system. The information we learn today will build upon our
prior work and assist the subcommittee in shaping policies that
encourage responsible innovation in financial markets while
cementing American AI leadership.
I want to thank our witnesses for being with us today, and
I look forward to today's discussion.
I will now recognize the ranking member of the
subcommittee, Mr. Lynch, for 4 minutes for an opening
statement.
OPENING STATEMENT OF HON. STEPHEN LYNCH, RANKING MEMBER OF THE
SUBCOMMITTEE ON DIGITAL ASSETS, FINANCIAL TECHNOLOGY AND
INCLUSION, A U.S. REPRESENTATIVE FROM MASSACHUSETTS
Mr. Lynch. Thank you very much, Mr. Chairman, and thank you
for your courtesy. I had a vote on a subpoena in another
hearing and that caused my delay but thank you.
I want to thank this panel of witnesses for your
willingness to come before the committee and help us with our
work.
This hearing continues this committee's oversight work to
examine the use of AI in the financial services and housing
sectors. As evidenced by the final report issued by the
bipartisan working group that Chairman Hill and I co-chaired
last year, financial institutions, fintech companies, and even
financial regulators are already deploying AI to maximize
operational efficiency and achieve cost reduction across their
core functions, from personalized customer services and
consumer lending to fraud detection and financial crime
monitoring.
At the same time, the rapid development of AI-based
technologies has introduced serious risks into the financial
service space. The Treasury Department, the Federal Reserve,
and Consumer Financial Protection Bureau have all expressed
concerns. Other financial regulators have repeatedly cautioned
that the development of robust and trustworthy artificial
intelligence is dependent on our ability to encourage
innovation that maximizes oversight, consumer protection, data
privacy, as well as workforce protection and marketplace
fairness.
In view of these considerations, I am concerned by the
Trump Administration's decision to rescind some of the
commonsense Federal directives that sought to advance the safe
and responsible development of artificial intelligence. This
includes the reversal of an executive order directing all
Federal agencies to enforce existing consumer protection laws
and to develop additional regulatory safeguards against fraud,
unintended bias, discrimination and privacy violations only
when absolutely necessary.
President Trump subsequently issued his own AI Action Plan,
a strategy that largely reflects the divergent view that
consumer protection stands as an impediment to AI innovation, a
position with which I strongly disagree.
Regrettably, the AI Action Plan undermines State-level AI
regulation by requiring Federal agencies to, quote, consider a
State's regulatory climate, close quote, when allocating
funding, and to outstrip Federal funds from any State with a
regulatory framework that it considers burdensome.
I would note that recent legislation to impose a 10-year
moratorium on State AI regulation was soundly defeated in the
U.S. Senate by an overwhelming bipartisan vote of 99 to 1.
The AI Action Plan similarly impedes Federal oversight of
AI-based systems by directing the Federal Trade Commission to
stand down from ongoing investigations that unduly burden AI
innovation. It is the fundamental mission of the Federal Trade
Commission (FTC) to protect the American public from unfair or
deceptive business practices.
President Trump also recently issued an executive order to
limit Federal procurement of AI large language models to those
that are developed with ideological neutrality, as reported by
the independent Brennan Center for Justice.
Compliance with the order will likely require technology
companies to degrade model performance and undermine public
trust in their technology. Not surprisingly, our committee has
received regular reports from AI stakeholders who have put AI
development on hold over concerns that their integration of
fairness metrics will run afoul of the executive order.
Innovation will also not be well served by the dismantling
of the Consumer Financial Protection Bureau (CFPB), a Federal
agency that has sought to advance the development of
trustworthy AI systems in financial services through consumer
protection enforcement actions and regulatory clarity.
In stark contrast to these actions, the work of our
bipartisan AI Working Group stems from a genuine commitment by
members on both sides of the aisle to foster AI innovation in
the financial services industry while also ensuring that
regulators are equipped with the authorities and resources
necessary.
In closing, to this end I look forward to continuing to
work on a bipartisan basis with Chairman Hill, Chairman Steil,
Ranking Member Waters and our committee colleagues to advance
the U.S. leadership in this important area.
Thank you, Mr. Chairman, for your courtesy once again, and
I yield back the balance of my time.
Chairman Steil. The gentleman yields back.
I now recognize the chairman of the full committee, Mr.
Hill, for 1 minute for an opening statement.
STATEMENT OF HON. FRENCH HILL, CHAIRMAN OF THE COMMITTEE ON
FINANCIAL SERVICES, A U.S. REPRESENTATIVE FROM ARKANSAS
Chairman Hill. Thank you, Chairman Steil and I appreciate
our panel being with us.
AI has, of course, rapidly changed the way Americans live,
work, and engage with our financial system. Last Congress, we
explored AI practices at financial services firms, regulators,
and supervisors. Congressman Bill Foster and I served on
Speaker Johnson and Minority Leader Jeffries' Bipartisan
congressional AI Task Force. Ms. Garcia and Mr. Lynch and I had
an excellent work session at MIT's labs on their research in AI
and financial services as well.
In all these efforts, we saw demonstrated how AI has the
potential to boost efficiency, cut costs, and strengthen the
tools used to protect consumers from fraud detection to anti-
money laundering but as with any innovation, there are risks.
AI systems have to be trustworthy, fair, and secure.
Today we will start that exploration in this Congress on
how AI, particularly the emerging capabilities of generative
AI, can reshape our financial system.
I look forward to the discussion, and I yield back.
Chairman Steil. Thank you, Mr. Chairman.
Today we welcome an esteemed panel. We welcome the
testimony of Dr. David Cox, vice president of AI Models at IBM
Research; Dr. Christian Lau, co-founder and president of Dynamo
AI; Mr. Matthew Reisman, director of privacy and data
collection at the Center for Information Policy Leadership; and
Daniel Gorfine, CEO of Gattaca Horizons LLC; as well as Dr.
Nicol Turner Lee, senior fellow and director of the Center for
Technology Innovation at the Brookings Institution.
We thank each of you for taking your time to be here. 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 the record.
Dr. Lau, you are now recognized for 5 minutes for your oral
remarks.
STATEMENT OF CHRISTIAN LAU, CO-FOUNDER AND CHIEF PRODUCT
OFFICER, DYNAMO AI
Mr. Lau. Chairman Steil, Ranking Member Lynch, members of
the subcommittee, and staff, I thank you for inviting me to
testify today, and I am honored to participate in discussions
focused on advancing AI across the financial services
ecosystem.
In 2021, I founded Dynamo AI alongside my co-founder and
CEO, Vaikkunth Mugunthan, during our Ph.D.s at MIT. We started
off as a small group of researchers deeply interested in AI but
also keenly aware that AI risks pose fundamental challenges to
real world adoption.
Since starting as a small group of Ph.D.s, we quickly found
ourselves working hand in hand with some of the largest
financial institutions, the most cutting-edge financial
technology (fintech) companies, as well as regional banks
across America to help them navigate compliance and governance
challenges posed by AI.
Today, we not only provide the security and governance
layer for many of the largest deployments of AI in banking, but
we are also now proudly backed by 40 of the top 100 U.S.
financial institutions and a consortium of community banks
across America.
Our mission has always been to help enterprises navigate
complex regulatory environments, particularly where legal and
compliance requirements pose open technology challenges that
institutions struggle to solve. We found that, when faced with
new technology regulations or internal compliance requirements
around new technologies, enterprises are often left paralyzed,
asking themselves not only how do I comply with these new
requirements but is it even technically possible for us to
comply with these new requirements. Nowhere do we see this to
be more prevalent than with the struggles of financial
institutions striving to adopt AI.
Every day, our team has the ability and opportunity to
witness exciting new AI proof of concepts that bring new
efficiencies to the bank but for every new exciting AI proof of
concept that we encounter, we also see another AI proof of
concept fail to make it into production and deliver meaningful
value. We commonly see that these projects fail not because the
underlying AI technology cannot deliver but, rather, because
financial institutions struggle to answer open questions about
managing AI risk in heavily regulated, high-impact, and
consequential environments.
Some common and, quite frankly, sensible questions about AI
risk include: What new security and data leakage risks does AI
bring to my organization? As we hand over more autonomy to AI
agents, how can we enable those agents to comply with
established banking protocol and procedure when carrying out
tasks and what happens when an AI assistant inevitably
hallucinates or fabricates facts in its response?
While financial institutions often struggle to answer these
questions, these are not intractable problems. At Dynamo, we
work with a multitude of financial institutions to establish
effective AI risk management that accelerates rather than
blocks their AI transformation.
To truly manage these new risks and unleash AI innovation,
financial institutions and regulators must embrace technology
solutions that can help risk and compliance teams scale their
oversight, including controls like AI guardrails, red-teaming
evaluations, and auditability over AI usage.
Importantly, the AI landscape is evolving at breakneck
speed, and regulators and policymakers need to adopt governing
frameworks that keep up with the pace of innovation, rather
than falling behind on new opportunities and risks.
Regulators should expand the use of AI sandboxes, as called
for in the administration's AI Action Plan and the bipartisan
H.R. 4801, to not only enable technology teams to experiment
with AI on high-impact use cases but also bring leading
evaluation and red-teaming technology to rigorously test these
experimental AI against real world risks.
Drawing on global examples, such as Singapore's successful
sandbox programs, the U.S. can strengthen competitiveness while
promoting secure and compliant AI.
Across financial institutions and governing bodies that we
speak to every day, we are starting to see comprehensive AI
risk management take shape. We believe that this will be key to
ushering a new and exciting era of advancement for the
industry.
On behalf of the entire team at Dynamo AI, thank you for
the opportunity to testify, and I welcome the opportunity to
work with the subcommittee to create a competitive, secure, and
compliant AI ecosystem within financial services.
Thank you.
[The prepared statement of Mr. Lau follows:]
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Chairman Steil. Thank you very much.
Dr. David Cox, you are now recognized for 5 minutes.
STATEMENT OF DAVID COX, VICE PRESIDENT, AI MODELS; IBM
DIRECTOR, MIT-IBM WATSON AI LAB
Mr. Cox. Chairman Steil, Ranking Member Lynch, and
distinguished members of this committee, thank you for the
opportunity to testify. My name is David Cox, and I serve as
the vice president for AI Models at IBM Research and the IBM
director of the MIT-IBM Watson AI Lab.
While the excitement around AI has intensified in recent
years, artificial intelligence has fascinated researchers for
many decades. To give you a sense of our historical place in
this, IBM has been on the vanguard of this ongoing revolution
from the very beginning, when an IBM researcher co-authored the
proposal for the 1956 workshop, Dartmouth conference, that
coined the term ``artificial intelligence'' and gave the field
its name.
IBM also has a long history of helping enterprises,
including in the financial sector, use artificial intelligence
technologies to unlock business value, and doing so in ways
that are responsible and engender trust.
Our work spans a broad spectrum, from helping to identify
appropriate use cases, to providing tooling to govern both the
development and deployment of AI systems, to inventing new
technologies for trustworthy AI.
From the start, IBM has tested AI internally before
offering it to others, an approach we call Client Zero. By
deploying AI internally, we ensure our models are stress tested
in the real world environments before they ever reach clients.
For example, by leveraging IBM's watsonx.ai and automation
tools, we help to augment the skills of our own workforce by
eliminating repetitive tasks. This has enabled our employees to
focus on more challenging, rewarding, and impactful work with
the time they save.
For the financial industry, the implications of AI,
particularly generative AI and large language models, are
transformative. Learning Management System (LMS) are not merely
chatbots, they are multifunctional tools that can be adapted
across a wide range of financial services use cases. These
opportunities come with challenges. Large models consume
enormous computing resources, raising both costs and concerns
about energy use.
Transparency is vital. Enterprises and regulators must
understand the provenance and quality of the data underlying
deployed systems. Security is also paramount as organizations
seek to safeguard sensitive information when using cloud-based
systems but perhaps the greatest risk is hesitation. If
industry delays too long, consumers and the U.S. economy may
miss out on the early benefits of adoption.
Responsible governance is not a break on innovation. It is
a mechanism that ensures that innovation can be deployed
securely and sustainably.
In regulated environments, firms must understand exactly
what data underpins their models and be able to audit those
systems over time. That is why IBM's enterprise AI efforts are
grounded in three principles: open, trusted, and secure.
Open-source AI strengthens transparency, produces
dependence on proprietary vendors, and enhances U.S.
competitiveness. Trust is built through transparency and data
curation, training processes, and lineage between models and
data. Finally, security must be embedded throughout the AI life
cycle, from data collection to deployment, backed by continuous
oversight rather than reactive compliance.
With this in mind, I urge policymakers to support open
ecosystems, to regulate applications rather than technologies
in the abstract, and to promote transparency requirements that
allow enterprises and regulators alike to test models, evaluate
accuracy, and understand safeguards. These steps will foster an
environment where innovation can thrive responsibly.
AI is not about replacing people but augmenting them. It is
about empowering professionals, enriching consumers, and
expanding opportunity. By embracing openness, insisting on
transparency, and embedding security, we can ensure that AI
strengthens both the financial system and America's global
competitiveness.
Thank you, and I look forward to your questions.
[The prepared statement of Mr. Cox follows:]
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Chairman Steil. Thank you very much.
Mr. Reisman, you are now recognized for 5 minutes.
STATEMENT OF MATTHEW REISMAN, DIRECTOR, PRIVACY AND DATA
POLICY, CENTER FOR INFORMATION POLICY LEADERSHIP
Mr. Reisman. Thank you, Chairman Steil, Ranking Member
Lynch, and members of the subcommittee, for the opportunity to
speak with you today.
I am Matthew Reisman, director of Privacy and Data Policy
at the Center for Information Policy Leadership, or CIPL, a
data and privacy policy think tank within the Hunton law firm,
whose mission is to advance best practices for the responsible
and beneficial use of data. CIPL facilitates constructive
engagement between business leaders, data governance experts,
regulators, and policymakers around the world.
AI plays a critical role in the financial services
industry, as this committee documented well in the staff report
of its bipartisan Working Group on Artificial Intelligence. For
years, machine learning has strengthened financial services
institutions' ability to combat fraud, provide richer and more
tailored services to existing customers, and extend services to
new ones. More recently, generative AI has boosted productivity
across functions, from software development to customer service
and we are now in the early days of agentic AI, which shows
promise for enhancing the experiences of businesses and
customers alike. Potential applications are extensive, from
streamlining Know Your Customer processes, to back-office
operations like payroll and invoicing, to online banking and
agentic commerce.
AI's use in financial services also carries risks to
consumers, institutions, and the financial system. The
bipartisan staff report documents these risks well. Agentic AI
may accentuate some risks while at the same time enhancing risk
management capabilities in areas such as privacy and
cybersecurity.
To secure the advantages of AI within the financial system,
we have three broad sets of recommendations:
First, with respect to regulation, pursue a risk-based
approach that focuses on the outcomes to be achieved. Avoid
overly prescriptive measures, build upon existing foundations,
including regulations, guidance, and standards that are already
in place. When necessary, clarify or adapt their application to
emerging technologies. Consider risks and benefits in equal
measure. Incentivize organizations to adopt accountable
practices. Build trust through meaningful transparency. CIPL
has long underscored that these concepts of organizational
accountability are central to smart governance of data and
technology.
Second, enable the responsible use of data for model
training and development. To function safely, effectively, and
fairly, models must be trained and tested using rich datasets.
Regulators should apply data protection principles in ways that
ensure the quality of AI systems while preserving individuals'
privacy. Privacy-enhancing technologies, or PETs, can reduce
risks associated with the use of personal data. Examples
include synthetic data, which is artificial data that mimics
the value of real world data, and differential privacy, where
random noise is added to datasets to prevent identification of
any individual's data. Policymakers should encourage continued
research on and use of PETs.
Third, engage in cooperative dialog among regulators,
technologists, and industry. As AI continues to evolve rapidly,
stakeholders must learn from each other. Fostering such
exchanges is the heart of CIPL's mission, and regulatory
sandboxes offer an invaluable avenue for such dialog. Numerous
jurisdictions have established regulatory sandboxes over the
past decade, from the U.K. to Singapore to U.S. States, like
North Carolina and Delaware. We commend steps to promote
sandboxes under America's AI Action Plan, the proposed
bipartisan, bicameral Unleashing IA Innovation and Financial
Services Act, and other proposed legislation.
CIPL has published numerous papers on the aforementioned
topics and is continuing our research on them.
We look forward to the discussion today and to supporting
your efforts to secure the benefits of AI and financial
services for everyone. Thank you.
[The prepared statement of Mr. Reisman follows:]
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Chairman Steil. Thank you very much.
Mr. Gorfine, you are now recognized for 5 minutes.
STATEMENT OF DANIEL GORFINE, FOUNDER & CEO, GATTACA HORIZONS;
FORMER CHIEF INNOVATION OFFICER & DIRECTOR OF LABCFTC
Mr. Gorfine. Thank you, Chairman Steil, Ranking Member
Lynch, and members of the subcommittee, for the opportunity to
testify before you today.
I am the founder and CEO of Gattaca Horizons, an adjunct
professor at the Georgetown University Law Center, and the
former chief innovation officer at the U.S. Commodity Futures
Trading Commission (CFTC).
Today's topic on unlocking the next generation of AI in our
financial system is critically important. The U.S. holds
significant competitive and first mover advantages, and we must
foster continued development through thoughtful policy
approaches. AI presents tremendous opportunities to further
expand access, lower costs, and increase efficiencies and
competitiveness while also enhancing compliance and regulatory
oversight.
To level set, it is important to recognize that AI and
financial services are not new. It is part of a steady
progression of automation that began decades ago. Today, recent
advances in generative AI and agentic AI are creating new
possibilities, ranging from generating code and new content to
planning and executing complex tasks.
Responsibly developed AI is, indeed, already yielding
tremendous benefits. AI can provide more accurate and efficient
decisionmaking, detect patterns that traditional approaches
would miss, help regulators keep pace with digital markets, and
promote financial inclusion by unlocking credit for
historically underserved populations. It is further enhancing
customer service, helping to identify patterns of financial
crime and market manipulation, and making financial advice more
accessible and lower cost for Americans.
As with any area of innovation, however, there are risks
associated with AI, including the potential for perpetuating
bias, relying on poor quality data, failing to operate as
expected, and advancing fraud and scams. The mere speculative
potential or fear of future harm, however, should not broadly
block development of AI, including by those small firms and
community banks seeking to remain competitive in an
increasingly digital economy.
To this end, as a key guiding principle, I would encourage
everyone to assess new AI-based models on their ability to
improve off of a highly imperfect status quo. This principle
should apply across AI applications, since a singular focus on
risk can blind us to the greater benefits as compared to legacy
approaches.
With respect to existing policy frameworks, the financial
services industry is well-equipped to manage new technologies
and should be a model for other sectors. For decades, a robust
technology-neutral regulatory framework has governed the
adoption of emerging technologies.
For example, consumer protection laws bar discrimination in
lending, whether the decision is made by a human or by an
algorithm. In our capital markets, rules against fraud and
manipulation, along with circuit breaker technologies, help to
mitigate risks related to potential AI-driven trading activity
and our financial regulators have long provided robust risk
management guidance, principles, and frameworks that address
model IT third-party and enterprise risks.
The overarching framework to ensure safe and responsible
adoption of AI is in place. The challenge now is thoughtful
application through sound, informed, and consistent regulation.
To ensure the U.S. maintains its leadership and
competitiveness, I accordingly offer five recommendations:
First, regulators must support, not block, responsible AI
adoption. We often hear that innovation is blocked by
regulators at the examination and supervisory levels due to
vague expectations and endless inquiries that lack clear paths
to compliance. Examiners should be well-versed in the benefits
and risks of AI and provide the marketplace with clear
expectations.
Second, we must ensure financial regulators have the in-
house expertise and tools to keep pace with technology. A
recent Government Accountability Office (GAO) report found a
significant lack of technology skills at Federal financial
regulators, which is why codifying innovation offices and
equipping regulators with their own AI expertise, tools, and
capabilities are essential for effective oversight.
Third, Congress should establish a Federal data privacy
framework and ensure open access to quality permissioned
financial data. The quality of AI model outputs is inherently
tied to the quality of data inputs. Congress should work to
establish a national framework that governs data privacy and
ensures that consumers have control over how their data is
being shared and used.
Fourth, Congress should prevent State laws from undermining
Federal financial regulatory frameworks. Given the national
nature of AI development and its application in the well-
regulated financial services industry, a patchwork of State
laws can create conflict, ambiguity, and confusion.
Finally, regulators should further clarify risk management
frameworks and promote the value of well-crafted standards.
This includes making clear that the mere use of AI, even GenAI
or agentic AI, does not inherently make an activity higher
risk. Regulators can further help their efforts to keep pace
with technological change by supporting well-crafted industry
standards through regulatory recognition and safe harbors.
Thank you, and I am happy to answer any questions you may
have.
[The prepared statement of Mr. Gorfine follows:]
[GRAPHICS NOT AVAILABLE IN TIFF FORMAT]
Chairman Steil. Thank you very much.
We now recognize Nicol Turner Lee for 5 minutes.
STATEMENT OF NICOL TURNER LEE, SENIOR FELLOW AND DIRECTOR,
CENTER FOR TECHNOLOGY INNOVATION, BROOKINGS INSTITUTION
Ms. Turner Lee. Thank you, Chairman Steil, Ranking Member
Lynch, and distinguished members of the subcommittee. Thank you
for this invitation to testify on the use of AI in the
financial sector.
My name is Dr. Nicol Turner Lee. I am a director of the
Center for Technology Innovation at the Brookings Institution,
and I am also the co-author of a report that was done on AI in
the global markets by the CFTC.
Artificial intelligence brings a variety of opportunities
to the financial sector, and for years it has been used in
banking, fraud detection, mortgage applications, credit
underwriting, and data analytics. Many of these use cases are
promising, offering the potential for greater accessibility and
improved customer service, while others may introduce
challenges, including concerns about racial bias and
discrimination.
Given that the financial services industry is one of the
country's most highly regulated ones, adoption and use of AI
requires careful review. Without such oversight, risks abound
that can undermine consumers' ability to be economically
resilient, especially under changing economic conditions.
Further, inaccurate or discriminatory information that is used
to train AI models can put marginalized populations at even
greater risk, threatening to widen the racial wealth gap, limit
access to home ownership, credit, and other financial
transactions. In other words, when algorithms make poor
decisions, the quality of life for Black and Brown communities,
seniors, and even some of us are placed in reverse.
For these and other reasons, Congress must continue to
foster responsible and ethical use in the financial regulation
sector by providing safeguards that protect consumers from AI
risks now and reinforce algorithmic accountability, safety, and
security, especially when we look at incidents of fraud.
Congress can also safeguard consumers from both the
unintended and intended consequences through legislation, which
starts with comprehensive national privacy standards and
reinforcing the jurisdiction of independent Federal agencies
and State attorneys general, who enforce consumer protection
regulations in the age of AI.
For example, the former director of the Consumer Financial
Protection Bureau clarified that algorithmic decisionmaking is
held to the same standards as human decisionmaking. The
Department of Justice, the Department of Housing and Urban
Development, these agencies also previously released documents
stating the compliance with many of the provisions that apply
to either tenant screening or fairness overall.
But recent actions taken to defund and compromise the
independence of Federal agencies like the CFPB, like the
Federal Trade Commission (FTC), which have oversight over
deceptive consumer practices, can have major implications on
protecting consumers from the opaque technology of AI.
State and State attorneys general are critical in enforcing
those protections, but challenges to their State rights and
laws are not productive. Even with the rejection of a 10-year
moratorium on States' ability to develop their own laws, the
current AI Action Plan still suggests that AI-related funds
should not go to those with burdensome AI regulations.
In the absence of a national framework, States must
continue to protect the millions of seniors, children,
marginalized populations, and even farmers who are being
barraged by algorithmic discrimination, deepfakes, and other
malicious attacks on their financial well-being.
Recent proposals from legislators seek to establish
regulatory sandboxes for AI developers where companies can
apply for waivers or modifications but without strong
regulatory or enforcement consequences, companies will have
another avenue to exploit the personal information and
behaviors of consumers.
As my Brookings colleague Aaron Klein suggests, we should
be creating more greenhouses, which allow for more transparency
and sunlight into these processes, promoting collaborative
partnerships between business, government, and consumers, to
see where issues arise and how the three of them can foster
trust with one another. These approaches can also assist in
anti-bias discrimination, which is also very much imperative to
building trust.
I would like to just close with five proposals for members
of this committee to consider as we embark on the inquiry
today.
Ensure that the industry is compliant with existing legal
statutes and remedies which reinforce algorithmic
accountability. The financial sector is already regulated by
numerous Federal and State organizations. It is imperative that
it stay that way, and that they also comply with acts like the
Fair Credit Reporting Act and others to seek the prevention of
discriminatory results.
Mandate transparency guidelines and full disclosure for all
consumers. Companies need to tell people publicly when they are
using AI to make decisions. Those matter outputs should be
explainable and accurate and reliable.
Encourage, not discourage, responsible and ethical
development of financial models. Innovation and regulation can
be complementary, and I think that is to the benefit, again, of
those greenhouses, which show transparency in the ideas and
processes that we embrace in this area.
Brace for the adoption of agentic AI but do the first two.
Make sure there is consumer protection in place that we are
poised to benefit from the autonomous nature of agentic AI, but
we still have firm oversight.
Finally I would say, invest in AI financial literacy
programs so that consumers know how this industry and sector is
evolving.
These and other questions I place before this committee,
and I look forward to your questions and collaboration.
[The prepared statement of Ms. Turner Lee follows:]
[GRAPHICS NOT AVAILABLE IN TIFF FORMAT]
Chairman Steil. Thank you very much, Dr. Turner Lee.
We will now turn to member questions. I will recognize
myself for 5 minutes for questioning.
I want to start with you, Dr. Cox, if I can. Let us do a
real quick stage setting. Algorithms have been utilized in the
financial services space since the 1980s, but we have now seen
an explosive investment into AI. What is sparking this massive
investment, in a short answer?
Mr. Cox. Yes. I think what is sparking this massive
excitement and investment is that this technology is general
purpose in a way that previous technologies have not been. So
there is a lot of work to train a system for, and now the same
system can be used in many different settings.
Chairman Steil. And it can pull through massive datasets in
a way that was not possible in the 1980s.
Mr. Cox. Absolutely.
Chairman Steil. Although in the 1980s as well as today,
there is already a regulatory framework in place, fair?
Mr. Cox. Yes.
Chairman Steil. Okay. Let me continue this. I want to come
over to you, Mr. Gorfine, if I can. We are faced with something
new as we focus on kind of risks and threats. The human mind is
really good at thinking about what the risks are. It is not as
good at thinking about the potential upside.
If we think about what the Biden Administration did, they
really took an attitude to the extreme in its handling of
artificial intelligence. Fortunately, President Trump has
reversed course on this, issuing Executive Order 14179,
Removing Barriers to American Leadership in AI, and releasing
America's AI Action Plan in July 2025.
So this approach can really accelerate AI development
through a try-first approach that removes some of the red tape
and onerous regulations.
I want to come to you, Mr. Gorfine. In your opinion, how
can the AI Action Plan--or how is the AI Action Plan an
improvement over the previous administration's AI approach?
Mr. Gorfine. Thank you. Thank you for the question.
I think that what we are seeing right now is an effort to
look where there is existing regulation in place and taking an
approach of letting us allow this to develop so we can actually
identify risks and determine whether anything more is needed.
I think, as many of us have outlined, the financial
services industry is a heavily regulated industry. We have
existing laws, regulations, and guidance that have been able to
adapt and incorporate emerging technologies for decades. There
are some approaches, including around the world, that are
looking to do things kind of preemptively and in a more
prescriptive fashion verse an approach where we say, hey, we
have this scaffolding in place, we have principles in place to
guide adoption of technologies in a responsible way, let us
allow this to actually grow and develop.
I think it is critically important that the U.S. does
remain the global leader in AI, including in the financial
services context. So the environment seems set to unleash that.
Chairman Steil. Thank you very much.
Mr. Reisman, I want to build on what Mr. Gorfine just said.
In particular, let us dive into data privacy laws, something
that is heavily regulated in the financial services space, but
with the advent of AI, there are additional concerns that are
coming online.
So as financial services firms are increasingly deploying
AI in areas like lending, fraud detection, customer services,
what role will Americans' personal financial data play, and how
do we use AI to implicate existing data privacy laws? Of
course, the logical follow up would be, what should we be
looking at from an AI perspective as it relates to data
privacy?
Mr. Reisman. Thank you very much for this important
question.
It has been heartening to me to hear how much we have
talked about data privacy today, because I think what we would
say is that a sound data privacy framework is a very important
foundation for the development of AI and for America's AI
leadership. I think data is the raw material on which
artificial intelligence depends, and AI model developers depend
on having rich datasets to develop high-quality models.
One thing I think that often gets put into the discussion
is the idea of a binary between privacy or innovation, and we
would absolutely say it is privacy and innovation, and privacy
is an enabler. So a good data privacy law with modern
interpretation holds onto classic principles we have had for
decades in privacy law but allows for flexible use of data for
purposes such as model training and development.
Chairman Steil. So building on what you are saying, do you
believe that we can regulate and address AI under the current
regulatory framework or do you need a new regulatory framework,
which has been proposed by some?
Mr. Reisman. What I would say is that, as a first step, we
should look at the framework that we have, look at it closely--
and a lot of the elements, as has already been discussed today,
are there--and try and be very precise about identifying any
lacuna but first, let us look at what we have.
Chairman Steil. Dr. Cox, you are nodding. You agree that we
can in many ways regulate AI through the existing framework?
Mr. Cox. I think, certainly, the starting place should be
what are the risks, what are the outcomes that you are trying
to govern and control, and then any modifications that come for
the technology can come from there but starting fresh.
Chairman Steil. Thank you very much. I agree.
I yield back.
I will now recognize the gentleman from Massachusetts, Mr.
Lynch, who is also the ranking member of this subcommittee. You
are now recognized for 5 minutes.
Mr. Lynch. Thank you, Mr. Chairman.
This morning, a number of us had a meeting with Jack Clark,
the co-founder and head of Policy at Anthropic and it was, I
think, enlightening to hear him say how, for Anthropic, that
the investment, the advancements, the deployment of AI had
exceeded by about 5 years their expectations of the development
and advance of AI, even based on the projections that they made
3 years ago. They said, we are 5 years beyond where we thought
we would be today.
I just point to the velocity of change here in this sector
and what a problem it creates up here, in terms of trying to
regulate that and protect markets as well as protecting
consumers.
I know a couple of you have mentioned the sandbox model.
Ms. Turner Lee, the Singapore model--and we actually had a
congressional delegation from this committee go to Singapore
and kind of review the model that they had for their sandbox
back when they were doing fintech, a fintech sandbox and a
crypto sandbox.
So, interestingly enough, their sandbox has an ethical
framework which begins with a principles-based approach so that
there are elements of fairness and accountability, inclusivity,
and it sets an ethical foundation for all AI projects. It has a
bias mitigation element to their sandbox and mitigates bias in
those AI systems, and developers are encouraged to assess their
algorithms for potential discrimination.
There are data transparency practices that must be complied
with. Organizations must be clear about how data is collected,
processed, and shared--explainability requirements within their
sandbox model. So they must ensure that their AI systems can
provide understandable justifications for their decisionmaking.
There is also very strong stakeholder engagement,
encouraging a wide range of stakeholders, and massive user
privacy protections that are very important not only for
Singapore but globally and certainly for the United States as
well.
There are regular reviews and audits of that sandbox
process--sandbox process among these participants in the
sandbox, as well as blueprints and guidelines and knowledge
sharing.
Is that the formula that we should use if we are going to
go--I know the full committee chairman has an idea about
sandboxes and we are going back and forth, he and I, about,
what that should look like.
I would just like to get your feedback. You have touched on
a lot of the sensitive issues, and I would like to hear you
extrapolate a little bit more.
Ms. Turner Lee. Thank you so much for that question.
I mean, I am a fan of sandboxes. I wrote a paper in 2018,
when we were just beginning this bubble, on what algorithms
were going to do when it came to consumers and regulatory
sandboxes are a great way to experiment. In fact, here in the
United States, we have used sandboxes to cultivate the fintech
marketplace but because of the velocity of speed in which AI is
gathering our personal information, the velocity in which we
are going into transformational models where we cannot
determine the beginning and the end. When we actually deploy a
sandbox model in this day and age, it is important to have many
of those verbals that the Singapore Government has put into
place.
It allows for accountability, continuous monitoring,
transparency, and it also allows us to ensure that consumers
are baked into the process, as opposed to providing waivers and
exceptions to companies to experiment and then come back and
tell us how it is all going to work, which is the current model
from which we exercise right now when it comes to AI.
Mr. Lynch. Thank you. One problem that I noticed in the
Singapore model was the number of participants was limited, so
it never scaled up. Some of the problems that we see with AI is
when you get that scaleability.
Is there a way for us to--I know the auditing and review
after the sandbox is ongoing, but is there any way we can sort
of make sure that scaleability effect is implemented inside of
a sandbox?
Ms. Turner Lee. Well, I think one of the areas in which
many of us who have fought for consumer protections are
interested in----
Chairman Steil. The gentleman's time is--I will ask you to
write that for the record----
Ms. Turner Lee. No problem.
Chairman Steil [continuing]. cognizant of the time.
The gentleman yields, and the gentleman from Michigan, Mr.
Huizenga, who is also the vice chair of the full committee, is
recognized for 5 minutes.
Mr. Huizenga. Thank you, Chairman Steil.
Dr. Lau, we are talking about sandboxes. Do you have
anything? I know you have been commenting on this in the past.
Do you have any additional thoughts that you would like to----
Mr. Lau. Definitely. We are actually participants in the
Singapore AI sandbox that they have set up, the AI Verify
Foundation. It has actually been working exceedingly well, in
the sense that these types of government agencies need to bring
the latest technologies to actually evaluate the latest risks
that are emerging with AI agents and new AI technologies.
So by bringing in innovative technologies to do that type
of red teaming and testing, they are able to stay ahead. Think
about here in the U.S., where we have regulatory agencies that
are still being educated about the technology, whereas in
Singapore they are able to unleash a lot more in terms of AI
potential through bringing in new technology to test and
evaluate it. I think there are a lot of benefits there and also
to the point that Congressman Lynch mentioned around
scaleability, red teaming is a very labor-intensive problem,
right. So, you have thousands of tests that enterprises have to
perform. If the Federal agencies can provide more guidance and
tooling around that, it is going to accelerate AI adoption.
Mr. Huizenga. Okay. That is helpful. Thank you.
Staying with you here, we often hear of an AI arms race
with China that, if lost, would threaten U.S. national security
and the United States global economic dominance.
In your opinion, is the United States currently--how are we
currently faring, I guess, in this arms race? Are we failing at
it? If so, how? More importantly, how can Congress and the U.S.
Government writ large ensure that the United States outpaces
China in the AI space?
Mr. Lau. I think there have been different paradigms
emerging here. One is where you have a Federal Government that
can choose and pick and invest in winners of the space versus
creating an open marketplace where different, say, large
language model providers or vendors can compete within this
marketplace, right.
I think what we are seeing when we talk to folks within the
Department of Defense (DOD), or Department of War now, we see
actually a movement toward opening up these marketplaces where
many different vendors can compete and the best solution rises
to the top.
Again, I will move back. We are actually evaluating which
are the best solutions for use cases that is going to drive
America's national security interests. It really comes down to
can we do the right evaluation of those large language model
providers or vendors.
So combining open marketplaces that foster innovation allow
different folks to compete, and then we get to choose the best
ones for our use cases. It is absolutely essential here for us
to be competitive in this arms race you are talking about.
Mr. Huizenga. By the way, I deeply appreciate how you are
succinctly wrapping up some very complicated and detailed
things and allowing me 2 minutes yet to ask other questions.
That is important up here when we only get 5 minutes.
Mr. Gorfine, I want to talk a little bit about the impact
of U.S. regulation and how it may threaten the use of AI in the
financial services space. We had the Securities and Exchange
Commission's (SEC's) predictive data analytics proposal under
the Gensler SEC, which I believe would have had some
significant impact.
Are there other things like that we need to make sure that
we are aware of and avoiding?
Mr. Gorfine. Yes. I appreciate you raising the predictive
data analytics rule. I mean, that was rescinded and I think
that was an example of a non----
Mr. Huizenga. Rightly so. Rightly so. As we saw with that
SEC chair, he was expansive in his pursuit of covering every
territory he possibly could.
Mr. Gorfine. Right. I would agree it was not technology-
neutral, and that should be kind of a guiding principle. It had
incredibly broad----
Mr. Huizenga. So it should be tech-neutral.
Mr. Gorfine. Absolutely should be technology-neutral and
importantly, like the soft power too and the way that
regulators communicate to the marketplace is important. So what
the----
Mr. Huizenga. Oh, in other words, you do not want to be
pounded into the ground and threatened with being put out of
business? That does not foster innovation?
Mr. Gorfine. That can be a challenge.
Mr. Huizenga. Allow the sarcasm to be mine.
Mr. Gorfine. That can certainly be a challenging
environment, I think, especially when you are talking about
smaller firms. When I think about regulatory messaging, it is
the small firms, it is the community banks that do not have
large armies of compliance teams that are able to parse certain
types of messaging that comes from regulators. So it can serve
as a big deterrent to adoption.
Mr. Huizenga. In the last 30 seconds, how can these smaller
community financial--you mentioned community banks or credit
unions. How can they use AI to compete?
Mr. Gorfine. Well, so I am a big believer--and I say this
in my written testimony--in standard setting organizations.
Like, industry-driven standards that regulators can effectively
recognize or even create safe hardware would allow small firms
to know that, if you are compliant with these best practices or
standards, that is, from a diligence perspective, a route you
can pursue. I think that exploring those types of models in the
U.S. is going to be critically important as--I am out of time.
Mr. Huizenga. I yield back. Thank you.
Chairman Steil. The gentleman yields back.
The gentleman from Illinois, Dr. Foster, the ranking member
of the Financial Institutions Subcommittee, is recognized for 5
minutes.
Mr. Foster. Thank you, Mr. Chairman, and to our witnesses.
I guess Mr. Reisman and maybe others have mentioned the
importance of privacy enhancement techniques. Several years ago
when I was chairing the AI Task Force on this committee, we had
dragged in a witness to talk about homomorphic encryption and
some of the differential privacy techniques and I noticed just
recently--and it was clear back then that these were not ready
for prime time. There was a huge penalty for performance, and
the privacy was not actually that great.
I noticed recently Google just announced this Google Vault
Gemini, which sounds like they are actually implementing
training with differential privacy.
So I was wondering if anyone on the committee could say
something about are these techniques really ready for prime
time, and is there the possibility of a regulatory safe harbor
for firms that commit to using high-quality differential
privacy type tools?
Mr. Reisman. Thank you for the question.
It has been quite extraordinary to see the progress on
privacy enhancing technologies over just the last few years. I
think you are right that not so long ago many of them were not
ready for prime time, but the curve has been quite steep in
terms of many of them becoming much more feasible.
Part of it is that many privacy enhancing technologies are
compute-intensive, but our computing power collectively is
growing a lot stronger, so we are able to handle that. I think
there was also a sense that a lot of them were complex and
maybe out of reach, especially for smaller companies.
There is a whole ecosystem now of expert companies that are
able to consult and offer what you might call off-the-shelf
privacy enhancing technologies, to make them much more
available much more broadly. So----
Mr. Foster. It would make sandboxes easier to implement.
Any other sort of comments on the state-of-the-art? Would
anyone disagree that this is something that is pretty promising
at this point?
Mr. Cox. Things like fully homomorphic encryption provide
very strong guarantees and you are correct that they are slower
than conventional computing, but the leaps and bounds that they
are making in that technology, it is orders of magnitude,
thousand times better. Every time I turn around, it has gotten
better.
So I think that is moving very fast, and the harbor is
going to ultimately start to close that gap as well. So I think
we are going to benefit from that wave.
Mr. Foster. That could be a key component to a safe sandbox
in a lot of these things.
Mr. Cox. Absolutely.
Mr. Foster. Relating to one of the things we struggle with
is the small bank versus large bank, trying to level the
playing field if we can and it is one of the things that AI can
potentially help us with.
Soon all of us will have in our pocket the best team of
lawyers that has ever been assembled. So it means if you ever
get in a fight with a billionaire, a legal fight, the
billionaire will not be able to get a better legal team than
you have essentially for free.
Now, similarly, a small bank should, through AI, be able to
have access to a really good AI risk adviser, a team of risk
adviser AIs, so that even if it is a pretty small bank, this
team will know every way that any bank has failed in the
history of this country and just have a tremendous amount of
knowledge and make it easier to operate a small bank.
Similarly, regulations, regulatory technology (RegTech), I
think, is another real advantage. I did an interesting
experiment a couple weeks ago where I said, okay, Claude, or
whoever I was using, give me the balance sheets for three banks
that have just failed for typical reasons, and it just knocked
it out of the park.
Then I said, now give us a resolution plan for each of
those three and it was great. I mean, it just said, okay, in
this case set up a bridge bank. In this case, try to merge it.
In this case, just liquidate it. They were very sophisticated
things, and I was impressed.
I was wondering if any of you have a feeling for whether
that is really kind of the future of regulation, that even
small banks have all of their records electronic.
If there was a standard interface for the accounting
software that gets run that could report up to risk management
and to the regulators, you could have real-time stress testing
against dozens of scenarios every single night for the smallest
bank, and that would be a real leveler.
Any thoughts? Is there a reason why things cannot evolve in
that direction?
Dr. Lau.
Mr. Lau. Thank you. Yes, certainly. We work with a lot of
community banks and regional banks, backed by a number of them
and we see this every day, right. So this new AI technology can
empower them to automate or streamline a lot of these
compliance work flows that are very manually intensive that
they just do not have staffing to do, but also, as you
mentioned, unlock really new types of auditing and risk
controls, so 24-hour continuous monitoring and observability
into certain types of work flows that are highly regulated.
That is all really exciting and actually being realized/
materialized today across a lot of these smaller banks but I
would say on the flip side, you also want to make sure that as
these AI technologies enter into these regulated work flows,
they are also having the right guardrails in place and they are
being used for those work flows appropriately and aligning them
with proper bank policies and procedures. So that is something
that actually requires technical expertise.
Chairman Steil. The gentleman's time is expired. We ask you
to complete that in writing.
The gentleman from Ohio, Mr. Davidson, who is also the
chair of the Subcommittee on National Security, Illicit
Finance, and International Financial Institutions, is
recognized for 5 minutes.
Mr. Davidson. Thank you, Chairman.
Clearly, AI represents a transformative force, one that can
exponentially enhance productivity, bolster our financial
system's global edge, and it will no doubt influence our
culture. Technology never exists in a vacuum, and I have long
warned about the erosion of our Fourth Amendment protections in
the digital age.
The Uniting and Strengthening America by Providing
Appropriate Tools Required to Intercept and Obstruct Terrorism
(PATRIOT) Act, for example, massively expanded domestic
surveillance. The Bank Secrecy Act had long ago obliterated any
real claim to privacy in your financial dealings. Most State
bureaus of motor vehicles are monetizing the personal data
citizens are required to provide just to drive or obtain ID.
Now, the government is outright buying data that would
otherwise require a warrant or a subpoena, sidestepping the
Fourth Amendment entirely.
Because we are serious about fostering innovation, and we
are serious about our Constitution, we also need to recognize
that AI should serve the American people without turning it
into another tool for unchecked surveillance or data
exploitation.
Mr. Chairman, I would like to submit this document for the
record. In my recent op-ed for the Daily Caller, I emphasize
privacy is the foundational layer for ethical AI.
Chairman Steil. Without objection.
[The information referred to can be found in the appendix
on page 96.]
Mr. Davidson. Thank you, Chairman.
Without it, we are handing the keys over to a surveillance
state on steroids. I mean, we need to update the regulatory
framework for AI or build it in Congress, and we certainly need
to address privacy, because that is the dataset that AI is
using. Urgent questions, including who is liable if AI is
misused? Who profits and how when someone else's data or
intellectual property is indexed or shared with AI? When does
law enforcement need a warrant, if ever?
These are not abstract questions. They are urgent, and
Congress needs to be proactive, not reactive, to set clear
boundaries. We cannot let AI become another excuse for Big
Brother to pry into our lives.
So what safeguards do we need? We should learn from other
jurisdictions, but we should, of course, chart our own course.
The EU's AI Act, for instance, takes an approach with outright
bans on real-time biometric surveillance and social scoring,
measures that align with protecting individual liberties from
invasive tech but in other ways, the European Union has become
pretty Orwellian with some of their privacy approaches and
speech limitations.
So positively framing safeguards around AI is important.
Freedom surrendered is rarely reclaimed, so I am encouraged by
the Trump Administration's AI Action Plan with its emphasis on
accelerating innovation, building infrastructure, and leading
globally. It promotes open source development, cuts red tape,
and streamlines permitting without smothering the private
sector.
Mr. Reisman, how are financial regulators protecting the
vast amounts of sensitive financial data that the government
already collects from AI exploitation?
Mr. Reisman. Thank you for the question.
First of all, I just want to--to your point about raising
data privacy and this very important foundation for a sound
environment, not only for keeping people safe but for
innovation. I would underscore our sense that having a sound
privacy framework in the United States is actually not only
consistent with but important for achieving the goals of the AI
Action Plan. It is encouraging to see progress in that area.
I think others may have more experience than me with what
is going on inside the agencies for what they are doing to keep
data safe, so I would defer to others.
Mr. Davidson. Anyone else?
Mr. Gorfine. I am happy to take that. I would agree that
data is the one place screaming for Federal legislation to
create a proper baseline for how we secure data that will be
consumed by AI models.
With respect to your question, what can regulators do? We
were just talking about privacy enhancing technologies, things
like encrypting data. Ultimately, financial regulators need to
be recognizing that there are these developing privacy
enhancing tools and making sure that regulated financial
institutions can use those technologies.
So data can be encrypted. We can avoid creating honey pots
of information that are being sent every which way. I mean, the
reality is today all of our information--driver's license
photos have been emailed, sent to every single provider. There
is a better way to move encrypted information and limit access
to who actually gets it and when.
Mr. Davidson. Thank you. I think the computing architecture
is really important. I have always liked blockchain
technologies. I like zero knowledge proofs and ways to protect
data that way. The government in some cases has artificially
limited that.
I will submit written questions for the record. I am really
curious how you protect intellectual property, copyrights,
trademarks, and maybe monetize that for other people down the
way. Maybe blockchain does it. Maybe there are other ways but
thank you for your expertise and attention.
Thanks for this hearing, Chairman, and I yield back.
Chairman Steil. The gentleman yields back.
The gentlewoman from California, the ranking member of the
full committee, Ms. Waters, is now recognized for 5 minutes.
Ms. Waters. Well, thank you very much.
Dr. Turner Lee, we have seen development of new AI
technologies like agentic AI, which can operate independently
and autonomously. Agentic AI is capable of self-directed and
complex behavior and can also carry out multi-step tasks.
Financial services companies are now experiencing and
experimenting with agentic AI in investments, credit
decisioning, and more. However, I am concerned about the new
scale of risk and vulnerabilities from all of this.
Dr. Turner, can you elaborate on the potential risks for
agentic AI and whether a liability framework can help us
mitigate those risks? Who is responsible for the actions of an
AI agent if something goes wrong?
Ms. Turner Lee. Thank you so much, Congresswoman, for that
question.
I think most of the conversation we have had today is on
this promise of AI as it relates to efficiency in the financial
sector and agentic AI as an extension of that. The more and
more financial institutions can become more autonomous through
chatbots and other tools that allow people to not necessarily
talk to a financial counselor but to talk to AI to be able to
get through their steps. It makes sense on the productivity
side, but it does not make sense for the consumer.
The consumer has a lot of reputational risk that comes with
this, a lot of financial risk and in an industry where we know
that consumers are at the heart of any type of harm or
potential harms that come through misadvice or miscalculation,
it could have a huge effect not only on people who are wealthy
but people who are experiencing the wealth gap.
So I appreciate your question. I do think we need a
liability risk structure for this, one in which Congress thinks
through the same type of consumer protections that we are
defunding right now and diminishing, even with many of the
actions today to append those regulatory agencies like the FTC
and the CFPB.
As we move into agentic AI, without those guardrails and
the ability of States, in particular, to control how people
respond to this new technology, it is actually going to go
further than we can catch up with it.
Ms. Waters. Wow.
Dr. Turner Lee, the Republican regulatory sandbox proposal
for AI we are considering today appears to be just more
deregulation framed as innovation and lacks requirements for
public disclosure, harm mitigation, and other important
protections, with an unlimited scope and virtually no
limitation, putting Americans, consumers, and other market
participants at risk.
I am deeply concerned that regulatory sandboxes may remove
safeguards from a rapidly developing AI market that is already
lacking meaningful Federal regulations and oversight. In fact,
we have yet to fully understand the consequences of deploying
this technology without any safeguards and are already dealing
with countless public safety issues.
Despite the fact that you have alluded to some of this,
what risks do regulatory sandboxes pose? If Congress were to
consider regulatory sandboxes for AI, what kind of standards
should responsible sandboxes meet?
Ms. Turner Lee. Thank you for that question as well.
I mean, I think we have mentioned already the role of
sandboxes in helping us to cultivate new products and services
within the sector--and among other sectors. We actually see
sandboxes in healthcare.
The challenge is, without the right variables that we are
actually constructing to make sure they are safe, ethical,
fair, inclusive, as has been mentioned, sandboxes will turn out
to be an exceptional exploitation of consumers.
What that means is, without any guardrails--which, the AI
Action Plan is suggesting there should be modifications, there
should be waivers. We really need sandboxes to be very
transparent. We need clear goals and questions about what it is
trying to solve. We need protections against consumers who are
part of those sandboxes to ensure that any harm that they face
is--there is retribution for that as well.
In my opinion, we can actually create this without putting
people at risk in terms of the end product, and we can do it in
the light, as opposed to in the dark, when it comes to
companies and government working together on those policy
concerns.
Ms. Waters. I am so pleased that you are here today.
Ms. Turner Lee. Oh, thank you and I am pleased you are here
today too.
Ms. Waters. I am going to call on you to continue the kind
of education that is needed with Members not only of this
committee but this entire Congress. I am particularly concerned
about the possibility for wrongdoing, the possibility for
discrimination----
Ms. Turner Lee. Yes.
Ms. Waters [continuing]. all of that and I want to learn
more about the databases that are being used and how they are
significant in determining the outcomes.
Ms. Turner Lee. Yes.
Ms. Waters. Thank you so very much.
Ms. Turner Lee. Thank you.
Chairman Steil. The gentlewoman yields back.
The gentleman from Tennessee, Mr. Rose, is recognized for 5
minutes.
Mr. Rose. Thank you, Chairman Steil, and thanks to Ranking
Member Lynch for holding this important hearing.
Thank you to all of our witnesses for taking time to be
with us today.
I want to start with you, Dr. Cox. Could you please
describe the concept of technological singularity and share
your perspective on whether you believe AI will achieve
singularity and provide an estimate on the possible timeframe
for this development?
Mr. Cox. Thank you for the question.
So the idea of a singularity is, we reach a point where the
technology is able to advance its own progress faster and
faster and faster, such that it can get sort of a positive
feedback loop and then we suddenly have an explosion of
capability.
One of the things that is interesting that--I just dusted
off a copy of ``The Singularity is Near'' by Ray Kurzweil that
happened to be on a shelf that I was cleaning up. One thing you
will see about futurists is, they often get the shape of things
right, but the years are difficult to predict. I would not
hazard to make a guess about when that is going to happen or if
it is going to happen but I would just say that I do not think
we are anywhere near--as somebody who works with this
technology day-in and day-out, I do not think our biggest risks
come from, sort of, some eclipse of ``AI is better at
everything than humans are,'' but certainly our labor-market
issues that we have to deal with as sub-tasks of a job are
displaced but I do not think we are in imminent danger of
anything so extreme as a singularity.
Mr. Rose. Thanks. I appreciate the insight.
Dr. Lee, I found an interesting article from the nonprofit
Cash Essentials that states, quote, Some AI-driven systems may
treat cash transactions as suspicious, leading to increased
scrutiny and regulatory pressure that discourages cash use,
unquote.
How can we ensure that AI does not discriminate against
individuals who use cash? Additionally, what measures can be
taken to prevent AI systems from falsely flagging cash
transactions as suspicious, thereby avoiding undue pressure on
companies and regulators to favor cashless payments over cash
transactions?
Ms. Turner Lee. I do appreciate that question because I
think we are seeing, based on the regulatory sandbox that we
used to create the fintech industry, a lot more transactions,
especially among the unbanked or underbanked.
To your point, here is why I think AI could be an
interesting tool to help us combat fraud. Improved analysis
through AI detection systems could actually be helpful there.
Using AI in ways that the AI sort of cleaves with the data
about the underbanked or unbanked and combines that with
traditional legacy systems could also be helpful.
We also talk about AI as just taking people's data, but it
also considers other externalities, like where you live, what
your ZIP Code is, these other proxies. Oftentimes those proxies
are for the worse, in terms of bias and discrimination, but
they could actually also be helpful in helping us understand
what the new, modern economy looks like in banking.
So I would suggest that there are some techniques on the
technology side, but there is also room for people like me, as
a sociologist, to come sit at the table and help determine how
we do this better.
Mr. Rose. We already see--without the benefit of AI, we see
cash transactions being discriminated against in a number of
ways. It is a very real concern to me.
Mr. Reisman, with the increasing prevalence of artificial
intelligence, it is clear that AI-assisted fraud will likely
escalate rapidly, employing novel tactics to deceive consumers.
In my view, it is essential to warn the public about emerging
AI-driven fraud schemes as soon as they are identified.
How can private companies and industry stakeholders
collaborate proactively to anticipate and combat the evolving
threat of AI-assisted fraud?
Mr. Reisman. Thank you for the question.
I share your concern about fraud, and I think I have heard
a lot of folks say we are in a moment where the defense has to
keep up with the offense, right? We want to make sure that our
financial institutions are empowered to have the same tools to
detect fraud that the fraudsters are using to advance it.
That is the most important, is that our institutions,
whether they are small banks, whether they are large ones, have
access to that state-of-the-art screening technology.
I think--one thing that I talked about in my opening
testimony was the importance of dialog and I think making sure
that we have spaces, whether it is through sandboxes or whether
it is through other mechanisms that are set up by the Congress
or by the regulatory agencies, to constantly be sharing
information about these advances in technologies and about
these fraud capabilities so that we are all collectively
working together on solutions.
Mr. Rose. Thank you very much. I appreciate those insights.
I yield back.
Chairman Steil. The gentleman yields back.
The gentleman from California, Mr. Liccardo, is recognized
for 5 minutes.
Mr. Liccardo. Thank you, Mr. Chair.
Thank you all for your testimony and for taking the time to
help educate us, myself in particular. I know this is a fast-
moving area and I really had questions targeting really
primarily Dr. Cox and Dr. Lee. I would be interested in your
views.
We have several concepts that have been presented in
legislation here before us, and I wanted to see if there was--I
would like to tinker a little bit with a couple of these bills
from Chairman Hill, the sandbox concept, and from my colleague
Congresswoman Pettersen, the task force concept, and combine
them in a way, if we could.
I represent Silicon Valley. Obviously, we have a lot of
concerns--or I hear lots of concerns in my neck of the woods
about having government involved in regulating the code and the
algorithms, particularly since government is not terribly good
at it. We would expect that the technology is evolving so
quickly that we will not be good at it but, on the other hand,
I think there is a widespread embrace of the notion that we
need to be mitigating the worst of the harms.
So the question would be: If we were to create perhaps even
more than a task force but an independent body of folks in
industry, academics, experts, financial regulators, and others
that were to establish what the best practices are in the
industry--the best practices around, for example, curated data
sets that I know you mentioned, Doctor, that IBM utilizes, or
testing and reporting, or fraud detection, watermarking, data
security--a whole host of best practices in the industry, and
then hold that up as the standard, and establish, essentially,
if that is going to be the negligence standard--or,
essentially, the standard, if everyone complies with that
standard, then you are exempt from liability. If you are not
meeting that best-practices standard, then good luck with the
lawyers and the regulators.
What about an approach that would combine those two?
I would ask either Dr. Lee or Dr. Cox, if you would like to
jump in.
Ms. Turner Lee. I will jump in first.
Mr. Liccardo. Yes.
Ms. Turner Lee. I think that is a tremendous idea that we
should consider, because we seem to be at the same stalemate
every time we talk about these issues.
There is a technical side of it, and then there is this
consumer output, and I do think that government regulators are
good at the latter, the consumer protection side of it,
because, guess what, that is the side where our constituents
are coming to us and saying, ``Something has harmed me.''
With regards to what you are saying, I would just like to
offer to you that we did start that process under the previous
administration. The Blueprint for an AI Bill of Rights was
actually a nice glide path for getting to the same protections
that you are speaking of, as well as collaboration. The
executive order, which was soon after appended, had a lot of
that conversation on how do you actually bring different bodies
together from various disciplines, industry sectors,
government, and civil society so that we actually solve this
together.
To date, we have seen a lot that sort of eroded in the new
administration as well as in the AI Action Plan, where we are
primarily competing against ourselves, when we actually just
see China as our only force of nature.
So I would suggest--I agree with you. I think there has to
be more conversation, more collaboration, more disclosure among
the various entities to get to the space that you are talking
about and I think a task force would not be a bad idea. That
was also recommended under the prior administration.
Mr. Liccardo. Thank you, Doctor.
Dr. Cox?
Mr. Cox. I think that there are some interesting things
emerging already. The ISO 42001 standard for just the entire
creation process of a large language model is one example of
something emerging, and I think that gives you a little bit of
a sense of how these things are playing out. It is a voluntary
standard, you get audited against it, and it is some sort of
mark of whether you have the proper hygiene there.
Now, that is different than regulating the algorithm,
though, right? Like, it is----
Mr. Liccardo. Right.
Mr. Cox [continuing]. more about regulating processes and
controls----
Mr. Liccardo. Right.
Mr. Cox [continuing]. and documentation and auditing, as
opposed to saying, this technology, this algorithm is
intrinsically worrisome somehow.
I think that is one thing that I think many of the
panelists have raised, is that there is nothing intrinsic about
the technology; it is, how are you using it? It always has to
be in the context of how it is being used and we have use-and-
risk-based regulation. That is a normal thing that we already
have. So combining that with things like cybersecurity hygiene
standards, which are now then being transported to the world of
AI, I think that is--I think that is an evolution of what we
already have to a good outcome.
Mr. Liccardo. All right. Thank you. Thank you both.
I yield.
Chairman Steil. The gentleman yields back.
The gentleman from Montana, Mr. Downing, is recognized for
5 minutes.
Mr. Downing. Thank you, Mr. Chair.
Thank you to the witnesses.
This is a really exciting topic for me. The thoughts of
AI--I came out of technology--the opportunities are incredibly
exciting, but it is also a little bit scary in where it goes,
and it is interesting--I was talking to Eric Schmidt last year.
He wrote a book on AI with the late Henry Kissinger, and he
made a comment that I thought was really interesting, about
being very light on how you regulate it so that we are not
blown out of the water by our competition. Then he said
something that really caught my attention: ``But you need to be
ready to unplug it.'' I was not exactly sure what that meant,
but it was an interesting comment that I am still dwelling on.
As a former regulator, we had to deal with a lot of the
issues with artificial intelligence as a tool for industry and
really what the implications were. Some of the interesting
things we did as an insurance regulator is--we were of the
opinion that, once you wrote down a rule or a law or something
prescriptive, it was probably already stale, because this is
just evolving so quickly.
So what we tried to do to inform industry on how we were
looking at it is, all the work we did on that committee was--it
was concept space, non-prescriptive, because you wanted to kind
of guide how we as regulators were looking at it without
saying, ``This is what you have to do.''
A lot of the questions that came up there were is the
machine biased? Is the machine bad? Is the machine--a lot of
this kind of concern about what was coming in it.
You know, me, you know, I think about the data. As a former
researcher, I think garbage in, garbage out. What is the data
you are training it with and what are the thought processes on
how broad and how deep that data set is and what kind of
results you have?
But the results coming out of it--a lot of folks were
saying, ``Well, at least in terms of a regulator, we need to
have causation and not correlation.'' You know, I do not agree.
I think if you have a high statistical probability of getting
the same results from this system--a lot of our life is based
on correlation and not causation--I think you can use that as
reality or close to reality.
Then, if it is giving you a result you do not like, like it
is affecting a protected class, if it is something that you do
not like, that is the public-policy decision, not ``machine
bad.'' Then you have the real, honest conversation about that
public policy.
Another thing that folks would say on the regulator side
very often is, ``Well, we need to be able to look into that
black box.'' I think about, well, that black box--once you have
an N-equals-close-to-infinity access neural network, no human
can understand that. What tools do you need to understand that?
You know--this is my opinion--the tool you need to understand
that is probably generated through AI.
So you have AI doing that, and it comes back to the ancient
question of, ``Quis custodiet ipsos custodes?'' ``Who is
watching the watchers?'' You know? It is an interesting problem
to come up with.
I am going to start--sorry about--thank you for indulging
me on that.
One of the things that I think about a lot is, as a State
regulator, States' rights are very important to me and there
has been a big conversation about that. I strongly favor
allowing States to lead on regulation where feasible but at the
same time, it is essential that this technology has the legal
and regulatory flexibility to continue innovating and develop
in the United States.
So I am going to start with Mr. Reisman here.
What role do you think the States should play in regulating
AI or are you of the opinion that this is something that
Congress needs to tackle?
Mr. Reisman. Thank you for the question.
I think the first question that we should be asking is, do
we need more regulation on AI at all? We have had an
interesting discussion about looking at what powers we already
have, whether that is under Federal law or under State law,
that allows us to address a lot of the questions that we may
have about AI.
I think that there is--one thing that we have seen is, for
both consumers and for businesses, it can be hard when there is
a kaleidoscope of different State regulations and so there is a
value in having interoperable Federal standards. I think there
is an interesting question to be explored further about whether
there may be particular elements that States need to address as
a----
Mr. Downing. Right.
Mr. Reisman [continuing]. complement but not substitute for
that.
Mr. Downing. Yes. Thank you on that.
In the interest of time, I am going to move on.
The Biden Administration seemed determined to stifle AI in
any way it could, focusing more on potential threats than its
clear benefits and I do believe the benefits are strong.
So I am going to move to Mr. Gorfine, please.
Could you describe some of the most concerning aspects of
the Biden Administration's approach to AI, from executive
orders to a so-called AI Bill of Rights, and discuss what the
impact would have been had Congress passed what the Biden
Administration proposed?
Chairman Steil. The gentleman's time has expired, but we
will ask the witness to provide that for the record.
Chairman Steil. We thank you.
The gentleman yields back.
Mr. Downing. On that, I yield. Thank you, Chair.
Chairman Steil. The gentlewoman from Massachusetts, Ms.
Pressley, is recognized now for 5 minutes.
Ms. Pressley. Thank you.
Thank you to our witnesses for joining us today for what is
really a timely hearing.
When I am in my community in the Massachusetts Seventh,
people are very animated about the future of artificial
intelligence. I hear it all, ranging from fears of AI bias to
excitement for innovative opportunities, to concerns about
implications on the future of work, to confusion about what is
next. I am proud that we are confronting these issues head-on
today.
September 26 to October 3 marks Boston AI Week. Startups,
investors, researchers, and students will all convene to
explore the role they play in the evolving AI landscape.
Leaders in AI, like the Mass Technology Leadership Council,
will host educational and networking events to build on State
investments in the next generalization of leaders, educators,
and workers in this rapidly growing field.
I am proud to represent a district that is trailblazing the
development of the AI industry. In Massachusetts, jobs in the
technology sector make up 14 percent of our labor force,
compared to 10 percent nationally and we know diversity in AI
jobs is essential to confront bias, to maximize opportunity,
and to make good decisions that help everyone benefit from
these cutting-edge technologies.
Dr. Turner Lee, I want to focus in on your research about
the role diverse teams play in AI development and deployment.
What are some ways diverse teams may be helpful to promote
ethical and innovative uses of AI?
Ms. Turner Lee. Thank you so much for that and I
congratulate you on the AI Week in Boston.
So I would just say there that it is important to have
representative groups for a couple of reasons that we have
spoken about today. I think, on both sides of this
conversation, there is this tremendous excitement for the
opportunities and then a tidbit of fear--and, depending on who
you are, more or less, right?--based on what AI can do.
It is important that we have people who have the lived
experiences of the variety of impacts that AI can have. Why
that is important, with any other internet technology that we
have had, is because AI, particularly the financial sector, is
dispersed to be very individual to that person.
So, when we talk about data, data that is traumatized or
discriminatory, that actually takes the account of the wealth
gap that is experienced by, for example, Black populations,
where they have been denied credit, loans, and other
eligibility, shows up in the data. When you do not have people
who understand that lived experience, that experience passes on
generationally, and the AI tends to then affect their quality
of life. Loan denials continue, et cetera.
So I think it is really important to have not only
background diversity but diversity of various people from
various disciplines, various sectors, sitting at the table. How
are we building financial-sector AI without people who actually
understand how community banking works or sit in the roles of
experienced financial literacy counselors, for example? You
have to have everybody there.
Ms. Pressley. Thank you.
Dr. Turner Lee, how do you believe the Federal Government
has a role to play in this and what is that, to ensure that
people from all walks of life--women, people of color, low-
income folks--can have careers in AI, especially in the
financial services sector?
Ms. Turner Lee. Well, I think you pointed it out really
well when you talked about trying to empower local
entrepreneurs. It is important for the Federal Government to
create low benchmarks to entry. We see on this panel a young
man who has his own company that is doing incredible things
when it comes to data architecture, AI infrastructure, et
cetera.
Giving opportunities for a variety of people to participate
in this ecosystem is really important, and I think the
government could actually find ways to incentivize the private
sector to be more inclusive of diverse founders and
entrepreneurs and small businesses that want to participate in
this space.
I also think the Federal Government could do more on AI
literacy, what does a national AI literacy initiative look
like, so that people know that this is not about being an
engineer; this is about actually experiencing this
behaviorally. The way that AI is dispersing, this is not going
to be just about experiencing it on your computer; it is going
to actually show up in your refrigerator and other places.
People need to know that.
I would say the other thing that Congress should do is
close the digital divide. We keep talking about AI, but we
actually have not closed the basic infrastructure issue. We
cannot build the compute facilities and data centers without
that.
Ms. Pressley. Thank you, Doctor, for being so prescriptive
there.
Whether it is a hearing in Congress or a meet-up at the
Museum of Science, AI is the topic of conversation, and we have
the responsibility to ensure that all voices are included,
because AI works best when it works for all.
I yield back.
Mr. Downing [presiding]. The gentlewoman yields.
The gentleman from Iowa, Mr. Nunn, is now recognized for 5
minutes.
Mr. Nunn. Well, thank you, Mr. Chair.
I want to thank the panel for being here.
Chair, as a fellow Air Force fellow, it is good to see you
in that seat.
As we look at what Congress has been trying to grapple
with, this idea of artificial intelligence--I have only been
here two terms, and we have talked about it every single year.
In fact, we were on the AI Task Force, something this committee
helped lead to be able to address real solutions but I think
Washington does a lot of talking, and we should be doing a lot
more listening, particularly to practitioners in the field who
have been addressing this--combating it, addressing it, and
finding new solutions for it--and incorporating your experience
into the way that we do policy. As opposed to DC. being the one
who writes the rules of the road and then expects you to
execute them, we should be doing this in collaboration.
Mr. Chair, it is one of the reasons I am leading a piece of
policy called the Artificial Intelligence Practices, Logistics,
Actions, and Necessities (AI PLAN) Act, or the Artificial
Intelligence PLAN Act, to ensure that we here in Washington are
incorporating and getting our own house in order before we
start telling the innovators, the pioneers, and, candidly, the
defenders how best to do it under a government auspice.
I look back at where we were with cybersecurity when I was
Director of Cybersecurity at the National Security Council
(NSC). Many of the solutions were already being provided out of
the public sector and the private sector when we worked
together.
With that, I want to be able to dive into a couple of
issues that have been addressed when it comes to artificial
intelligence.
First and foremost, the idea of misinformation and how it
happens not only in the financial space but across our
government. It has lowered the barrier to entry. It has allowed
misinformation to expand at an accelerated rate. We saw even
this week, tragically, with the death of Charlie Kirk, Russian
bots providing inception for everything from conspiracy
theories to misaligning our own communications right here in
our own country.
Dr. Christian Lau, you have been a leader at Dynamo AI. You
have made an effort to really bring this technology on board. I
want to ask you, what should Congress be doing right now to do
those things like combating state-sponsored terrorism,
including information warfare?
Mr. Lau. Yes. That is a great question.
Particularly, you mentioned the PLAN Act, which you
proposed, which I think is a really great step forward in
looking at these different risks--how AI can be used for
misinformation, but also used for different types of financial
crimes, right?--and threaten systemic risk to our system.
One thing that I would emphasize is that keeping up with
the pace of the technology is key, and talking to practitioners
about the latest risks is absolutely essential to making
effective legislation, right?
One thing that I had an opportunity to talk to your team
about is the advent of AI agents and particularly, you brought
up cybersecurity risks----
Mr. Nunn. Right.
Mr. Lau [continuing]. right?
So AI agents is where you give these systems more autonomy
to carry out end-to-end workflows and tasks. A lot of times,
they can go in many different directions, but this also opens
up new risks for agents to potentially exfiltrate data or for
State actors to leverage agents and actually send what we call
``prompt injection'' or ``jailbreak'' attacks to actually
exfiltrate information from protected systems in financial
services or elsewhere.
So an AI agent can pull an email, that email could have an
embedded hidden instruction or prompt injection, and that agent
can then be overridden to actually perform malicious tasks like
crash a system or exfiltrate sensitive information.
So I think the work that you have been doing on this act,
as well as talking to leading practitioners, is absolutely key
to keeping up with these risks.
Mr. Nunn. I could not agree with you more that the
challenge here is, the technology is going to move much faster
than the policy is able to keep up. So being able to provide a
framework versus prescription is something the AI PLAN Act
intends to do but also brings in best practitioners in this.
I want to talk with you, Matt Reisman. You are at the
Center for Information Policy Leadership. You have seen some of
these innovations come on board. Talk to us a little bit about
how not only we incorporate them to have a better safeguard for
our government and our citizenry but, where the innovation is
really being pioneered a lot of times by our private-sector
partners, how should we be incorporating that into how we work
here in Washington, DC.
Mr. Reisman. Thank you for the question.
I think, number one, one thing that has been encouraging to
us to see is that, on the one hand--you have said, the
technology is moving quickly. Responsible actors in this space
are continuing to innovate, but they are not leaving their
values--and they are concerned about making trustworthy AI--
behind.
Smart businesses in this sector, including a lot of the
leaders, recognize that having trustworthy technology is not
just good; it is good for business because customers,
government, other stakeholders have more trust and faith in the
technology that we are building--that they are building.
There is a lot to be said for government setting certain
standards around transparency and asking for folks to show how
does the technology work at a high level, and how are some of
those specifications made.
Mr. Nunn. I would like to continue to see this level of
collaberation, in the same way we have done with cyber defense
and bringing an AI consortium together of business leaders like
yourself, of innovators like yourself, to be able to do this. I
think that starts with the AI PLAN Act.
With that, I yield my time.
Mr. Downing. The gentleman yields.
The gentlewoman from Colorado, Ms. Pettersen, is now
recognized for 5 minutes.
Ms. Pettersen. Thank you, Mr. Chairman.
Thank you all for being here today and I am grateful that
you all are hosting this opportunity to talk about such an
important issue.
I want to address--Mr. Reisman, you talk about how we need
to have--it is helpful if we have a regulatory framework at the
Federal level, and we could have some complementary State laws
as well.
I am really worried because we worked in a bipartisan way
to provide a framework to try to continue to move forward here
in Congress. We are not known for being the most efficient body
in government and our inability to come together to produce a
regulatory framework on AI is concerning, when I look at the
risks that are involved.
There is great opportunity for efficiency and a lot of
promise in all of the different sectors, but I am especially
worried in the financial sector and the vulnerabilities that we
have.
We know that the risks that already--that we are facing are
going to get exponentially worse with AI. Already, 50 percent
of fraud has been identified as using AI. Ninety-two percent of
the financial institutions surveyed indicate that fraudsters
are using generative AI and 44 percent of financial professors
report that deepfakes are used in fraudulent schemes.
So, when I think about the impact to our financial system
and the gaps that we are leaving here, what are the most
important steps? This is opened up for all of you. How do we
provide to ensure that we do not have the gaps in supporting
our smaller financial systems, so our smaller banks, making
sure that they have access to the technology and what would you
recommend on providing that framework and those guardrails at
the Federal level?
That is a lot to ask, so do not feel the pressure but if
anyone wants to add on just some of the key things that we need
to be thinking about right now.
Ms. Turner Lee. I will start.
Ms. Pettersen. Okay.
Ms. Turner Lee. I definitely think the conversation has
centered around really advancing national data privacy
standards and I think that conversation, as someone has already
mentioned, will sort of ring in the beast of data that is
actually fueling these systems. If we can come to some
bipartisan support on that, I think that would be helpful.
I also think that approaching, Congresswoman, as you said,
some of these very nuanced areas, like deepfakes--data is out
there that seniors are thinking that their grandchildren are
calling them for money. When a senior's economic viability is
compromised, that is worse for the whole family. I think going
into those verticals where there is bipartisan agreement is
another area--we have seen that with the Tools to Address Known
Exploitation by Immobilizing Technological Deepfakes on
Websites and Networks (TAKE IT DOWN) Act--in terms of anything
that has to do with any manipulated content, I think is a step
forward.
But I do also think that we have to reimagine what it looks
like in terms of regulatory framework that has some guidance
and guardrails, which I would be happy to share with your
office in more detail.
Ms. Pettersen. That is great. I look forward to working
with you on this and we have a bill, as well, that I have
worked with Representative Flood on; it is the Preventing Deep
Fake Scams Act.
This is important because our agencies are only able to
take on what we are directing them to do and so, when we look
at trying to change with the times to make sure that they have
the support they need to address the challenges that we are
facing now, can you speak to the greater coordination needed
between the regulators, the industry, and the subject-matter
experts to develop strategies to protect financial institutions
and consumers from fraud using AI?
Ms. Turner Lee. I sure can, if no one else will.
I think on the deepfake side, I think the challenge that we
have is that, because we have multiple agencies with
jurisdiction over this particular area, we have not come up
with some shared values and goals on, one, how are we defining
this.
We have had a lot of conversation, to your point, where we
have not necessarily agreed on, on the copyright area, this
digital provenance. The same thing, I think, is actually going
to go into the deepfake space, where we are looking at
extracted video, text, and audio and trying to determine, where
is this coming from? That is very hard for us to track at this
moment but also coming up with some shared values and goals
across the various agencies that are responsible for
enforcement over that.
I think if we are able to actually get away from some of
that fragmentation, we could actually push forward with really
good legislation that protects people from clones.
Ms. Pettersen. Great.
Mr. Reisman?
Mr. Reisman. Yes. I just wanted to say--to praise, in the
bill you have called for specifically bringing together
industry and expert stakeholders to talk with regulators and
government to try and figure out how to solve these problems
together. It has never been more important, as the technology
moves quickly, to have all of these voices in the room to
problem-solve together.
Ms. Pettersen. It seems like this should just--we should
bring bills to do this for every agency across--sorry. Thank
you. I yield my time.
Mr. Downing. The gentlewoman yields.
The gentleman from South Carolina, Mr. Timmons, is now
recognized for 5 minutes.
Mr. Timmons. Thank you, Mr. Chairman.
Thank you to the witnesses for being here today.
I am pleased that the subcommittee is turning its attention
to the use of artificial intelligence in financial markets.
Like stablecoins and market structure, this is a technology
that will have a significant impact on how American companies
and consumers engage with our evolving financial systems.
In order for the United States to remain a global leader in
innovation, we must establish clear rules of the road that
allow the free market to thrive, and we must do so thoughtfully
and effectively.
As I continue to meet with industry leaders, I am
increasingly concerned about the growing number of conflicting
State laws related to AI. These laws often vary in scope,
definition, and enforcement, creating a complex regulatory
environment for businesses to operate across multiple
jurisdictions.
This patchwork of regulation makes it more difficult for
companies to scale, innovate, and remain compliant, while also
increasing the risk of inconsistent protections and outcomes
for consumers. Without a unified approach, we risk slowing
progress and creating barriers that disadvantage both American
businesses and the people they serve.
Mr. Gorfine, States have introduced more than a thousand
laws related to the use of artificial intelligence. From your
perspective, what are the most pressing risks of a State-by-
State regulatory patchwork for both consumers and innovators?
Mr. Gorfine. It is a good question.
I want to start by being very clear that when I look at
this issue I am talking about it within the context of the
regulated financial services sector. There may be issues
outside of financial services where States are engaging on AI,
but I think that what is really important is to recognize that
there are existing Federal and State laws, there is existing
regulation, and there is existing guidance that financial
institutions adhere to where a patchwork of State laws can
absolutely interfere, conflict, or create ambiguity for
financial services firms operating on a national level.
I think that is especially clear in the data context. We
have existing Gramm-Leach-Bliley Act (GLBA) data privacy laws
in place for financial services firms. If you start introducing
a patchwork of State approaches, that can be really
problematic.
The same goes for when it comes to risk management. There
is a very careful risk management framework in place for
financial services, and, again, I do worry about interference
of State laws there.
So that is how I am thinking about the interplay of State
and Federal, especially in the context of the financial
services space.
Mr. Timmons. I am going to turn that a little bit around
and say, while this is going to be difficult for financial
services companies to comply and to work across State lines,
let us talk about the AI companies that are trying to create
these products.
I mean, the Chinese are ahead of us or close to being ahead
of us, and the businesses that they have that are focused on
this do not have this burden. Would you say that is an equally
difficult challenge?
Mr. Gorfine. Yes. I mean, the broader question for AI is
that I do think having a proper Federal framework that allows
us to operate on a national scale makes really good policy,
market, and competitiveness sense. That is something that I
would absolutely encourage because, as we are describing, it is
not even just a national competition; it is global, right?
These activities transcend borders and having a coherent, kind
of, Federal framework there makes good sense in the AI context.
Mr. Timmons. Thank you for that.
While there are many challenges involved in legislating and
establishing clear rules of the road for artificial
intelligence, there are also powerful opportunities. Artificial
intelligence has the potential to equip financial institutions
with advanced tools to better serve their clients, improve
efficiency, and reduce risk.
As I mentioned earlier, one of the most common concerns
that I hear from both large financial institutions and smaller
community banks and credit unions is the burden that regulatory
compliance places on their operations. These requirements
strain both their financial resources and staffing capacity.
Artificial intelligence offers a promising solution to help
automate and streamline compliance processes, allowing firms to
redirect time and resources toward innovation and customer
service.
Dr. Lau, given the significant burden that regulatory
compliance places on financial institutions of all sizes, how
is artificial intelligence helping firms streamline these
processes and manage risk more effectively?
Mr. Lau. Thank you for the question.
Definitely, we see applying AI to compliance workflows as
one of the highest return on investment (ROI) activities that
we are seeing banks implement today, including smaller
community banks and regional banks, right? The reason is not
just so they are able to automate more workflows where you have
low staffing, but also they open up new opportunities for
greater compliance. Think about continuous monitoring, 24-hour
audits, et cetera, right?
On the flip side, you also need to be able to look at, as
you are introducing AI to these regulated workflows, what type
of guardrails or controls are put around those AIs so that they
are able to follow the right policies and procedures when
executing those workflows. That, actually, in many cases, is
the more challenging problem, is, how do you rein in this
autonomous AI to actually carry out the tasks effectively and
in compliance?
Mr. Timmons. Thank you for that.
Emerging technology has the ability to make sure that the
U.S. is the center of the global economy for decades to come,
and we have to get this right.
With that, Mr. Chairman, I yield back.
Mr. Downing. The gentleman yields.
The gentleman from New York, Mr. Torres, is now recognized
for 5 minutes.
Mr. Torres. Thank you, Mr. Chair.
AI is the most transformative technology of our time. The
rise of generative AI could prove to be as revolutionary as the
advent of writing or the advent of the printing press. Whether
AI will create a better world or a worse world, no one knows
for sure. What we do know for sure is that the world will be
radically different from anything we have seen before.
There is nothing new about the use of AI in finance. What
is new is the use of generative AI, large language models, in
finance.
So my first question: What are the capabilities in finance
that a large language model can perform that legacy AI has been
historically unable to perform?
Anyone who can answer that question is free to do so.
Mr. Lau. I can go ahead and jump in.
I think Dr. Cox actually mentioned at the beginning, the
reason why there is so much investment in this space is because
it is general-purpose AI, meaning that it could actually do
many different things. You can prompt it in infinitely
different ways to carry out certain types of workflows that are
very specific to your day-to-day, all the way to doing things
like continuous monitoring, et cetera, right?
So that is the power of the technology, are these general-
purpose AI models----
Mr. Torres. What is the best new use--what is the best new
use case in finance?
Mr. Lau. Yes. I would say--I mentioned compliance as one of
the very high ROI activities because of the manual effort
involved in that but I would say, the most mature one that is
delivering the highest ROI that we see is around developer
productivity, the ability for these AI agents to actually go
and build applications from scratch, empowering even, we are
seeing, small community banks to leverage these to actually
really accelerate their own software development and
integrating their infrastructure stack.
Mr. Torres. Can an AI banker outperform a human banker?
Mr. Lau. I think one of the biggest challenges to actually
having the AI outperform a human banker is to make sure that it
complies with the common sense of a human banker, right? That
has actually been challenging when you look at AI agents, to
give them that type of common sense out of the box, even given
the vast amounts of data that they have been trained on.
So building guardrails that a human is trained to follow is
something that is still an open challenge but something we are
helping a lot of these banks with today.
Mr. Torres. Because I know Members of Congress are
irreplaceable, but I am wondering if bankers and traders are
replaceable by AI.
What is the impact of AI on concrete applications like
credit scoring, loan approvals, fraud prevention and detection?
Like, does AI lead to more loan approvals or fewer? Does it
lead to more inclusive credit scoring or less inclusive credit
scoring? What is the outcome so far.
Ms. Turner Lee. I can--oh, do you want--well, I can point
to that.
I mean, I think we thought, because of the objective nature
of AI, that it would be easier to see more loan approvals, more
credit applications actually confirmed----
Mr. Torres. Where did we get this notion that AI is
objective?
Ms. Turner Lee. Well, we thought----
Mr. Torres. The data comes from the internet.
Ms. Turner Lee. Well, that is what I was going to say. We
thought----
Mr. Torres. It is a reflection of human nature.
Ms. Turner Lee. That is right and because the data that is
fueling the AI systems basically comes from us--and
particularly in generative AI, it is curated data. It is not
necessarily predictive data. It is data that exists on people
on the public internet--we are actually in some cases the same
results and in other instances worse.
We have seen that also on the housing appraisal side when
it comes to homeownership, that even if you scrub your entire
home of any type of remnant or artifact of you, the AI will
still generate the same results, because AI uses other
proxies--your address, your net worth in your community, et
cetera--Congressman.
So I think, when we say that AI is better than humans or a
banker can outperform a human, probably in time, but
expeditious calculation of how we make these decisions comes at
foreclosing on opportunities economically for various
consumers.
Mr. Torres. Can AI--I guess I was going to ask, can AI be
harnessed to expand access to capital, to expand access to
credit? One of my frustrations--I have real frustrations with
traditional credit scoring methodology. Can AI detect new
patterns of evaluating creditworthiness?
Mr. Gorfine. I think that is right. I mean, traditional
credit scores are highly correlated with protected class
characteristics.
One thing I would suggest is to consider second-look
applications of AI. What that means is, if you take an existing
decline pool and you run the decline pool through, kind of,
cutting-edge, gen-AI-related underwriting models, you will at
worst case result in another decline, but you may actually
start pulling some approvals through that process, and you
mitigate some of your initial concerns around the impact of
such models.
So I think there are ways to smartly start testing these
models in a way that upholds fairness.
Mr. Torres. The question for me--because, inevitably, AI is
going to have some measure of algorithmic bias----
Ms. Turner Lee. That is right.
Mr. Torres [continuing]. right? The question for me is not
whether it is completely free of bias but whether we can make
it less biased than the human alternative.
Is that an achievable mission?
Ms. Turner Lee. Well, I think it is achievable if there is
transparency, first and foremost, that an AI model is making
the decision on behalf of the financial--on a financial
application. Many people do not know that AI is actually being
used to make those credit decisions, so you have to start with
public disclosure.
I think the second thing, to your point, is, we do need a
stat that is able to actually evaluate what the
disproportionate impact--disparate impact is.
Mr. Torres. I am about to be replaced by AI but thank you.
Mr. Downing. The gentleman's time has expired.
I would like to thank all the witnesses for your 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 October 23, 2025.
[The information referred to can be found in the appendix.]
Mr. Downing. With that, this hearing is adjourned.
[Whereupon, at 3:47 p.m., the subcommittee was adjourned.]
[all]
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