[House Hearing, 119 Congress]
[From the U.S. Government Publishing Office]
CURBING FEDERAL FRAUD: EXAMINING
INNOVATIVE TOOLS TO DETECT AND
PREVENT FRAUD IN FEDERAL PROGRAMS
=======================================================================
HEARING
before the
SUBCOMMITTEE ON GOVERNMENT
OPERATIONS
of the
COMMITTEE ON OVERSIGHT AND
GOVERNMENT REFORM
U.S. HOUSE OF REPRESENTATIVES
ONE HUNDRED NINETEENTH CONGRESS
SECOND SESSION
__________
JANUARY 13, 2026
__________
Serial No. 119-55
__________
Printed for the use of the Committee on Oversight and Government Reform
[GRAPHIC(S) NOT AVAILABLE IN TIFF FORMAT]
Available on: govinfo.gov, oversight.house.gov or docs.house.gov
______
U.S. GOVERNMENT PUBLISHING OFFICE
62-435 PDF WASHINGTON : 2026
COMMITTEE ON OVERSIGHT AND GOVERNMENT REFORM
JAMES COMER, Kentucky, Chairman
Jim Jordan, Ohio Robert Garcia, California, Ranking
Mike Turner, Ohio Minority Member
Paul Gosar, Arizona Eleanor Holmes Norton, District of
Virginia Foxx, North Carolina Columbia
Glenn Grothman, Wisconsin Stephen F. Lynch, Massachusetts
Michael Cloud, Texas Raja Krishnamoorthi, Illinois
Gary Palmer, Alabama Ro Khanna, California
Clay Higgins, Louisiana Kweisi Mfume, Maryland
Pete Sessions, Texas Shontel Brown, Ohio
Andy Biggs, Arizona Melanie Stansbury, New Mexico
Nancy Mace, South Carolina Maxwell Frost, Florida
Pat Fallon, Texas Summer Lee, Pennsylvania
Byron Donalds, Florida Greg Casar, Texas
Scott Perry, Pennsylvania Jasmine Crockett, Texas
William Timmons, South Carolina Emily Randall, Washington
Tim Burchett, Tennessee Suhas Subramanyam, Virginia
Lauren Boebert, Colorado Yassamin Ansari, Arizona
Anna Paulina Luna, Florida Wesley Bell, Missouri
Nick Langworthy, New York Lateefah Simon, California
Eric Burlison, Missouri Dave Min, California
Eli Crane, Arizona Ayanna Pressley, Massachusetts
Brian Jack, Georgia Rashida Tlaib, Michigan
John McGuire, Virginia James R. Walkinshaw, Virginia
Brandon Gill, Texas
Vacancy
------
Mark Marin, Staff Director
James Rust, Deputy Staff Director
Ryan Giachetti, Chief Counsel
Jenn Kamara, Director of Strategic Initiatives
Emily Allen, Professional Staff Member
Mallory Cogar, Director of Operations and Chief Clerk
Contact Number: 202-225-5074
Robert Edmonson, Minority Staff Director
Contact Number: 202-225-5051
------
Subcommittee on Government Operations
Pete Sessions, Texas, Chairman
Virginia Foxx, North Carolina Kweisi Mfume, Maryland, Ranking
Gary Palmer, Alabama Member
Tim Burchett, Tennessee Eleanor Holmes, Norton District of
Brian Jack, Georgia Columbia
Brandon Gill, Texas Maxwell Frost, Florida
Emily Randall, Washington
C O N T E N T S
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OPENING STATEMENTS
Page
Hon. Pete Sessions, U.S. Representative, Chairman................ 1
Hon. Kweisi Mfume, U.S. Representative, Ranking Member........... 3
WITNESSES
Mr. Ken Dieffenbach, Executive Director, Pandemic Response
Accountability Committee
Oral Statement................................................... 7
Ms. Renata Miskell, Deputy Assistant Secretary for Accounting
Policy & Financial Transparency, U.S. Department of the
Treasury
Oral Statement................................................... 8
Mr. Sterling Thomas, Chief Scientist, U.S. Government
Accountability Office
Oral Statement................................................... 10
Written opening statements and bios are available on the U.S.
House of Representatives Document Repository at:
docs.house.gov.
INDEX OF DOCUMENTS
* Letter, from Program Integrity Alliance; submitted by Rep.
Sessions.
* Letter, from United Council on Welfare Fraud; submitted by
Rep. Sessions.
The documents listed above are available at: docs.house.gov.
ADDITIONAL DOCUMENTS
* Questions for the Record: Mr. Kenneth Dieffenbach; submitted
by Rep. Sessions.
* Questions for the Record: Mr. Kenneth Dieffenbach; submitted
by Rep. Mfume.
* Questions for the Record: Mr. Kenneth Dieffenbach; submitted
by Rep. Walkinshaw.
* Questions for the Record: Ms. Renata Miskell; submitted by
Rep. Sessions.
* Questions for the Record: Ms. Renata Miskell; submitted by
Rep. Mfume.
* Questions for the Record: Mr. Sterling Thomas; submitted by
Rep. Sessions.
* Questions for the Record: Mr. Sterling Thomas; submitted by
Rep. Mfume.
* Questions for the Record: Mr. Sterling Thomas; submitted by
Rep. Walkinshaw.
These documents were submitted after the hearing, and may be
available upon request.
CURBING FEDERAL FRAUD: EXAMINING
INNOVATIVE TOOLS TO DETECT AND
PREVENT FRAUD IN FEDERAL PROGRAMS
----------
TUESDAY, JANUARY 13, 2026
U.S. House of Representatives
Committee on Oversight and Government Reform
Subcommittee on Government Operations
Washington, D.C.
The Subcommittee met, pursuant to notice, at 2:13 p.m., in
room HVC-210, U.S. Capitol Visitor Center, Hon. Pete Sessions
[Chairman of the Subcommittee] presiding.
Present: Representatives Sessions, Comer, Palmer, Burchett,
Jack, Mfume, Norton, and Frost.
Also present: Representative Walkinshaw.
Mr. Sessions. The Subcommittee on Government and Operations
will come to order, and I would like to welcome everybody to
the hearing today.
Without objection, the Chair may declare recess at any
time, and I represent myself for making an opening statement.
OPENING STATEMENT OF CHAIRMAN PETE SESSIONS
REPRESENTATIVE FROM TEXAS
Welcome, each of you, today to our hearing where we are
having a discussion about, ``Innovative Tools to Detect and
Prevent Fraud in Federal Programs.'' Last week, we spent time
highlighting a significant problem in state-administered
programs. We exposed extensive fraud in Minnesota that went
largely ignored until brave whistleblowers stepped forward. We
saw that fraudsters are getting smarter and richer at the
expense of the American taxpayer. These bad actors were
exploiting loopholes in oversight, leaving us all to ask why
wasn't it stopped earlier? Why did we just now catch it?
Well, today we are here to do the important work of
bringing to the American people, and each of you, a group of
people who are dedicated to this, who have longstanding answers
and who have thought through much of this, not only to prepare
to be here today, but in their career. Finding a solution is a
very important thing, and this is a persistent problem that not
only must be addressed, but must be talked about openly, and as
you see today, with Mr. Mfume and on a bipartisan basis.
Last March, this Subcommittee examined why fraud was not
stopped earlier and learned that agencies are not incented to
prevent fraud. They are incented to make quick payments and to
try and figure things out later. As we have seen countless
times, this approach needs to be looked at and changed. I think
you will hear some of that change today, and you certainly will
hear the resolve of people who believe that this is something
that is within our mandate, not just something that exists that
is good to talk about. Fraud should be detected before it
happens. That is not a new concept, but it is going to be
widely discussed today.
Government agencies must ensure that hard-earned taxpayer
dollars are going to the right person for the right person from
the very start. I have told the story many times about how I
have a 31-year-old Down syndrome son, who will find throughout
his life as he continues that he needs to have help from many
people. One of those might be the Federal Government or state
government, but that the dollars that are intended for people
who need them, who cannot take care of themselves, is part of
the responsibility that we have to make sure that it goes to
the right people. As we have seen recently in Minnesota, when
there are no guardrails, bad actors enrich themselves over the
taxpayers' dime.
The Pandemic Response Accountability Committee was created
to provide necessary oversight over pandemic problems. We are
entering a new era where this oversight has been expanded to
other agencies and other programs. Over the course of the past
few years, the PRAC, as it is called, has addressed data
analytics capacities and capabilities that can show when bad
actors are trying to hit the Federal Government in multiple
programs, they can be found, they can be stopped, and we can do
something about it before a check goes out the door. They can
determine when IP addresses are recommended and connected to
others who, too, might be far away from eligibility for a
specific program but are tied to a need for us to know more.
They can alert programs to pause and move more carefully to
review information before any payments go out the door. They
can do this for free, but not everybody knows about it.
A key collaborator in this effort is the Treasury's Bureau
of Fiscal Services, which maintains the Do Not Pay system. The
Treasury is the last barrier before payment is sent out so they
can initiate that pause before putting money into the wrong
hands, but they are not always allowed to do so. Collaboration
is key. However, there are some legislative challenges that, if
solved, could strengthen these efforts to detect and prevent
fraud. That is part of what we will also hear today. Currently,
the PRAC is housed within the Council of Inspectors General on
Integrity and Efficiency, and its operations were extended in
law until 2034. A permanent solution that maintains the
analytic capacities and capabilities that have been built over
the past six years is necessary and needed. Its database is
billions of records deep, and it has begun to pay for itself,
but only because of proper, not just management, but good
oversight that is provided whereby we all work together.
Treasury's Do Not Pay system has access to a large number
of datasets, but more are needed to ensure that the system is
comprehensive and truly innovative. As we discuss innovative
tools and collaboration, it is important that we also discuss
not only how to make best use of these tools, but what barriers
need to be removed so that they can be used. It is important
that we discuss what needs to change with program design so
that agency and programs are verifying the validity of
information, not just confirming that documentation exists.
Today, we will hear from distinguished subject matter
experts in the field who can help us to understand what tools
are available and what needs to be done to make us better at
detecting and preventing fraud payments before they happen. In
the coming weeks, I will be introducing legislation to address
some of these issues, more specifically, the permanent solution
for the PRAC, and I am eager to hear from our witnesses today
about the opportunities that exist to promote financial
transparency and integrity. Yesterday, I sat down with each of
these witnesses, and we discussed in some bit of detail about
the need for them to do more than just present information, but
to tell a story because the work that is done by each of these
of people who will speak today goes into depth of
understanding, not just the problem and the solution, but the
things that we need to do to be better prepared for fraudsters
who always seem like they are just a step ahead of us.
So, I look forward today to a thoughtful discussion and
collaborating together on a bipartisan basis, and that is
exactly why the man of the hour, Mr. Mfume, is here. I believe
that Mr. Mfume and I work well together. I think we get a
better answer when we both ask questions, perhaps from each
other's own perspective, but it is done for the benefit of the
taxpayer and for the right reason. I am very proud of my
relationship with Mr. Mfume, and I told him as we were walking
up today, I hope I do not cause him any problem in any election
that he has. So, I will say to him today, thank you for being a
part of this effort. Thank you for the opportunity for us to
approach getting to difficult subjects with the same expertise,
the same knowledge, and close to the same answer.
I will yield my time and now yield to the distinguished
gentleman, the Ranking Member. The gentleman is recognized.
OPENING STATEMENT OF RANKING MEMBER KWEISI MFUME
REPRESENTATIVE FROM MARYLAND
Mr. Mfume. Thank you very, very much, Chairman Sessions. I
do not know that you are going to cost me any votes. There used
to be an old practice around here by some of the old timers who
would walk up to you before an election and say, ``I can be for
you or a'gin you. Which one do you want?'' So, you continue to
be for me. I appreciate that, I really do, and I want to thank
you for working together to hold this hearing today. Obviously,
I want to thank the witnesses that are here before us, the
Members of this Committee who have joined, and will be joining.
Although it is captured in the news cycle with all sorts of
attention in recent weeks, the problem of fraud and improper
payments in the Federal Government programs has existed for
many, many years, as we all know. It has bedeviled Presidential
administrations on both sides of the aisle, and it has cost
taxpayers hundreds of billions of dollars. In fact, a 2024
Government Accountability Office analysis estimates that the
Federal Government loses a staggering $233 to $521 billion in
fraud every year. That is just absolutely amazing, and it is
beyond comprehension. So, Chairman Sessions and I have worked
on a bipartisan basis, as you have heard, to address this
issue, and we have done it for the past few years. We remain
keenly focused on the task because every dollar lost to fraud,
as we all know, is prevented from going into those desperately
needed programs of assistance that the Federal Government and
its programs provide.
The government's assistance programs play, I believe, an
irreplaceable role in helping small businesses to grow, and
keeping children fed, and ensuring access to affordable
healthcare, and protecting so many other sectors of our
society. The fight to combat fraud must not malign these
programs and rip away support from people that need it, but,
instead, we have got to find and make sure that our laws are
faithfully executed and that taxpayer dollars go to the people
they were meant to serve. As our Committee discusses the fight
to detect and to prevent fraud before it happens, it is
extremely important to reflect on the work that has been done
and much of the work that has not worked, if I can use that
terminology.
At the beginning of the year, the so-called Department of
Government Efficiency sought to go it alone, ignoring prior
recommendations for cost savings, for fraud prevention, and for
efficiency from our Federal oversight bodies. Firing scores of
Federal employees and unilaterally destroying government
programs that Mr. Musk did not personally like, really led to
much of the damage, I believe, that we can look back on today
and pinpoint. When DOGE employees failed to identify actual
instances of rampant waste, fraud, and abuse, they made up, in
their own way, their own numbers and logged widely inaccurate
claims of their so-called ``Wall of Receipts'' that we have all
read about. Now here we are a year later and government
spending has actually increased. Let me repeat that again: one
year after that, government spending has actually increased.
And so, the fact is that our government already had in place
fraud-fighting expertise. Instead of illegally defunding the
Council for Inspector Generals on Integrity and Efficiency and
firing Federal workers trained in fraud prevention, the focus
went on funding these other offices and doing away with career
civil servants and inspector generals that truly, truly
understand the systems that we hold them accountable for.
So, as we move beyond the chaos and move beyond some of the
destruction of the last year, we have got to look at the tools
of the future that prevent fraud before it happens. I am
greatly encouraged by the progress of the Pandemic Response
Accountability Committee in creating analytic systems to
prevent and detect fraud, progress, by the way, that has
already recovered over $500 million in taxpayer funds.
PRAC's model, as the Chairman has said, demonstrates the
belief and the reality that the combination of data sharing
between siloed Federal agencies and responsibly implemented
human supervised artificial intelligence systems can stop fraud
before it happens. AI systems working with large databases and
datasets can detect patterns and connections with fraudulent
actors that humans often cannot flag, certain applications that
cannot be processed correctly and that are paused and reviewed
by a human before payments happen. Of course, as we all know,
and I think as we all agree, the use of any artificial
intelligence systems with private data of Americans requires
absolute caution, and fraud-fighting officials must receive
proper training to ensure that these models are not trained on
high-quality data that could be used in the wrong way. An
unreliable AI system or an AI system without a skilled
workforce to train and use it would be worse than no system at
all.
So, before I conclude, I just want to address the harmful
politicization of fraud-fighting efforts that we have seen
taking place. Legitimate efforts to combat fraud in federally
funded programs must never result in cuts to programs that
Americans rely on. We can do better than that. Just last week,
the Administration froze $10 billion in social services for the
states of New York, California, Colorado, Illinois, and
Minnesota without any real clear reasoning, impacting hundreds
of families and hurting the poorest among us, but not, as I
have been able to detect, preventing any sort of fraud. So,
politically-motivated attacks accompanied by announcements like
that that the Assistant Attorney General for Fraud Prevention
made the other day apparently lead to the kind of unfortunate
reporting that we have. I think that we have got to be able to
do what we do in a real clear, concise way, that we have got to
be able to share that information, that this cannot be a
partisan fight. The people who are hurting as a result of
fraud, waste, and abuse come from all parts of this country.
There are all backgrounds and religions. They can help
themselves and others cannot, but we have the power in this
Committee and, indeed, this Congress, to put in place the sort
of things that work.
So, I want to commend the work of PRAC. I want to thank the
Chairman for being and having a long vision on this and working
together in a bipartisan way so that Members of this body and
Members of the House of Representatives recognize that there is
a good, clear, and workable way of out of this if we can put
aside those things that separate us long enough to be able to
deal with the fact that $233 billion to $531 billion are going
out the door every day while we engage in some of those
battles.
So, I want to yield back. Mr. Chairman, I want to thank you
again for this. I think it is fair to say that everybody on the
Committee has a clear desire to really get their arms around
this. It has been a lonely couple of years working on this,
quite frankly, but I think people now are starting to recognize
that once we do something, and it is verifiable, and we do it
again and again and again, we can make a real and lasting
difference. So, I thank you, and I yield back any time I might
have.
Mr. Sessions. The gentleman yields back his time, and I
want to concur with him that perhaps the greatest thing we can
do in this is to get ideas from the people who are closest to
it, who studied it, who understand it, who understand the
characteristics and the relationships inside government of
maybe not just the limitations, but the frailties that keep us
from getting closer. I want to thank the gentleman for his
opening statement today.
Today, I would like to note that we have the young Chairman
of the entire Committee, Mr. Comer, who is with us today.
Normally, he is on this hot seat over here, but, Jim [sic], I
want to welcome you to this Subcommittee and thank you very
much. I would also, without objection, like to waive on
Congressman Timmons of South Carolina and Walkinshaw of
Virginia, who are waived on the Subcommittee for the purpose of
questioning the witnesses at today's Subcommittee. I am hearing
discussion from the gentleman from Tennessee who wanted me to
mention his name also, so I am delighted that each of the
Members are here today.
We are going to move directly to our witnesses, and so,
today, I am pleased to introduce the witnesses who really,
today, will be the star of the show. And I have instructed
them, as you know I always do, to please come and tell us not
just the information that we need, but the story behind it
because I think the compelling evidence that they bring needs
some clarification about really what a difference it makes.
And so, today, I am pleased to introduce Ken Dieffenbach.
He is Executive Director of the Pandemic Responsible
Accountability Committee, known as the PRAC, and he really gets
the gold star. He is a star witness, has been with us before,
and as I told him last time, he did well enough to get invited
back, and he has taken us up on today. So, he leads the
Committee's efforts to support and coordinate oversight of the
trillions of dollars that were spent on pandemic response and
help to understand the detection of fraud, waste, and abuse and
management of related funds. Ken, thank you for being here
today.
Second, we have Renata Miskell. She is the Director/
Assistant Secretary for Accounting Policy and Financial
Transparency at the U.S. Department of Treasury. I think this
is a new role for her, and we are delighted. I think that,
because of the great work that she has done, she also is a
valuable asset to this interest. There, she leads work to
safeguard taxpayer dollars by modernizing payments, preventing
fraud, reducing improper payments, and promoting fiscal
responsibility. And she told me yesterday that this goes all
the way to the top, to the Secretary, that the Secretary is
interested and sees this as being part of President Trump's
management agenda.
Last, Sterling Thomas is the Chief Scientist at the GAO,
the Government Accounting [sic] Office, where he leads the
Agency's science and technology work and assesses and evaluates
emerging technologies for application in government, including
AI, artificial intelligence. And I enjoyed being with him
yesterday. I had to leave a little bit early, but I really
appreciate him. Doctor, thank you so much for taking time to
join us.
So, pursuant to Committee Rule 9(g), I will ask that the
witnesses rise and raise their right hand to be sworn.
Do you solemnly swear or affirm that the testimony you are
about to give is the truth, the whole truth, and nothing but
the truth, so help you God?
Mr. Dieffenbach. I do.
Ms. Miskell. I do.
Mr. Thomas. I do.
Mr. Sessions. Thank you. Let the record reflect these
witnesses answered in the affirmative. Thank you. You may take
your seat. The opportunity for us to have you to give testimony
today to the U.S. Congress, this Subcommittee, is important to
us. We will count on that that you have.
Let me remind the witnesses that we have read your written
statement--we talked about it yesterday--and it will appear in
the hearing record in full. So, while we do talk about the 5
minutes, I instructed each of you yesterday to tell your story
and to remember that I have a slow gavel, and Mr. Mfume agrees
with that. We want to hear from you, this is important, but I
would ask that you also pay attention to the green light,
yellow light, and red light, and I would now go to Mr.
Dieffenbach for his opening statement. The gentleman is
recognized.
STATEMENT OF MR. KEN DIEFFENBACH
EXECUTIVE DIRECTOR
PANDEMIC RESPONSE ACCOUNTABILITY COMMITTEE
Mr. Dieffenbach. Thank you. Chair Comer, Chair Sessions,
Ranking Member Mfume, Members of the Subcommittee, it is an
honor to be here today to discuss the Pandemic Response
Accountability Committee, or PRAC, and our work to investigate
fraud and improve fraud prevention across Federal programs.
Thanks to Congress and to this Subcommittee, the One Big
Beautiful Bill Act extended the PRAC and its data analytics
capabilities until 2034, provided $88 million in funding, and
expanded our jurisdiction to programs funded in the law. The
PRAC team is thankful for this opportunity to demonstrate the
value and effectiveness of our fraud prevention work. Operating
on an annual budget of $18.5 million a year, to date, the PRAC
has helped recover over $500 million for the taxpayer. As
Executive Director of the PRAC, an entity Congress created to
oversee over $5 trillion in relief funding, I work with a
phenomenal team that is leveraging artificial intelligence to
collect, organize, analyze data to rapidly provide insights
into fraud risks. This proactive approach is clearly needed so
that fraud is prevented before funds are disbursed.
This past summer, the PRAC issued two fraud alerts that
identified over $79 billion in potential fraud that could have
been prevented with pre-award vetting and cross-agency
collaboration. To address these issues, the PRAC is developing
an artificial intelligence-enabled fraud prevention engine.
Trained on five million pandemic applications and other data,
the tool can quickly identify anomalies, trends, patterns, and
hidden connections in future applications before payments are
made. Had our fraud prevention engine been in existence in
March 2020, pre-award vetting would have flagged at least tens
of billions of dollars in fraudulent claims for further
scrutiny, allowing agencies to prevent fraud.
The PRAC is also actively engaging with partner Inspectors
General to identify opportunities to prevent fraud in programs
funded in the One Big Beautiful Bill Act and to address fraud
risks beyond identity theft and eligibility fraud issues, or
issues such as traditional procurement fraud. Of particular
focus will be cross-program risks as fraudsters rarely target
just one government program. They exploit vulnerabilities
wherever they exist. The PRAC also works with Inspector General
(IG) offices to develop analytics tools that provide new
insights and improve their effectiveness in their oversight
mission. For example, we developed a risk dashboard for the
Pension Benefit Guarantee Corporation OIG that, to date, has
contributed to their recovery of over $260 million. And we just
launched a dashboard for the Federal Communications Commission
OIG that is focused on four programs that disbursed $11 billion
to 14,000 different entities.
The PRAC also provides investigative support to more than
50 law enforcement partners related to over 1,200
investigations, with over 24,000 subjects, with potential fraud
losses of over $2.5 billion. We also partner closely with GAO
and the Treasury Department, and it is important to note that
the PRAC and the Treasury's Do Not Pay program are
complementary platforms, that both work to protect taxpayer
dollars from different angles. The PRAC focuses on a broad set
of risks, patterns, trends, anomalies, and hidden connections
amongst data from a wide array of programs, applications, and
transactions. Our access to law-enforcement-sensitive and other
unique data, our 119 million pandemic aid applications, and
over 127,000 known pandemic fraud cases or suspected cases can
reveal powerful new insights and serve as an early warning
system of organized, often transnational criminal conspiracies
and other emerging threats.
In one of the tens of thousands of pandemic fraud cases, a
PRAC investigation identified one scheme involving more than
450 applications from over 100 different applicants across 24
states. This is but one example where the proactive use of data
and technology could have prevented or aided in the early
detection of a scheme, mitigated the need for a resource-
intensive investigation and prosecution, and helped ensure
taxpayer dollars went to the intended recipients and not the
fraudsters. As many of you already pointed out, every dollar
that goes to a fraudster does not go to the recipients the
Congress intended to help the small businesses, the unemployed,
individuals, veterans, just to name a few.
With the support of Congress and the PRAC's talented staff,
we will continue our work on behalf of the taxpayers to
investigate fraud and demonstrate the value and effectiveness
of fraud prevention. Thank you again for your continued strong
support of the PRAC, the IG community, and independent
oversight. This concludes my prepared remarks, and I look
forward to your questions. Thank you.
Mr. Sessions. Thank you very much. Yesterday, you spoke
about the pride of authorship that you have for your job and
how other people that may be associated with it saw that. I
hope they are having a chance to at least get a tape of you
today.
Ms. Miskell. Thank you, Chairman.
Mr. Sessions. Perhaps they are. Well, the gentlewoman is
now recognized for an opening statement.
STATEMENT OF MS. RENATA MISKELL
DEPUTY ASSISTANT SECRETARY FOR ACCOUNTING POLICY &
FINANCIAL TRANSPARENCY
U.S. DEPARTMENT OF THE TREASURY
Ms. Miskell. Thank you. Chair Comer, Chair Sessions,
Ranking Member Mfume, Members of the Subcommittee, thank you
for the opportunity to share how Treasury is supporting Federal
programs in preventing fraud and improper payments. Treasury is
firmly committed to safeguarding taxpayer dollars and advancing
data-driven solutions to prevent fraud and improper payments
before they occur. Each year, Treasury, on behalf of Federal
agencies, disburses trillions of dollars in payments. Ensuring
those payments go to the right people, in the right amounts,
and at the right time is both a matter of fiscal responsibility
and a matter of public trust. Under Secretary Bessent's
leadership, Treasury is modernizing stewardship of taxpayer
dollars. At the core of Treasury's efforts are two initiatives:
first, expanding the use and utility of Do Not Pay, and second,
enhancing Treasury's payment verification processes to flag
risky payments. I will provide an overview of both initiatives,
followed by the challenges Treasury faces to implement them.
Do Not Pay is a government-wide tool provided by Treasury
for agencies and states operating Federal programs to detect
and prevent the leading causes of improper payments. As an
analogy, think of Treasury as America's bank. Across the
country, certifying officers, grant managers, caseworkers are
like bank tellers, responsible for ensuring payments go to
eligible individuals and entities. Do Not Pay is a tool that
helps these frontline workers detect risk when making awards
and certifying payments. Despite its promise, Do Not Pay has
faced two challenges. First, the program has been
underutilized. In Fiscal Year 2024, only four percent of
Federal programs could access all available data. Second, Do
Not Pay has not had sufficient authority to access key Federal
databases that could detect the most common driver of improper
payments, namely verifying identity, financial status, and
death.
To address these challenges, in January 2024, Treasury
created a Tiger Team to study the issue and prototyped
innovative solutions. The Tiger Team identified promising new
datasets and machine learning and AI techniques that could have
prevented about $28 billion in improper payments if it was used
the prior year. In March 2025, the President issued Executive
Order 14249, Protecting America's Bank Account Against Fraud,
Waste, and Abuse. The Executive order embedded many of the
lessons learned from the Tiger Team, and since then, Treasury,
in partnership with the Office of Management and Budget, has
made substantial progress in expanding and improving Do Not
Pay. By the end of this fiscal year, all Federal programs are
on track to fully utilize Do Not Pay.
We are also working to add new high-value datasets and are
overhauling our technology to deliver more useful results. In
addition, Treasury is enhancing its payment verification
screening to identify anomalies prior to agency certification.
The screening helps ensure that payments have valid accounting
codes, that the bank account provided is open and belongs to
the payee, and that payments are not going to deceased
individuals. While this progress is significant, additional
statutory authority would help Treasury fully achieve its
objectives. I am grateful for Congress' support in granting
Treasury permanent access to the full Death Master File through
passage of S. 269 last evening. Treasury is also seeking
limited access for Do Not Pay to validate taxpayer
identification numbers and income in a privacy-preserving
manner that is consistent with Do Not Pay's rigorous access
controls. These data sources would dramatically improve
eligibility determination and fraud prevention.
In closing, I look forward to working with this Committee
to help ensure Federal dollars reach the people and entities
that they are meant to serve. Thank you, and I look forward to
your questions.
Mr. Sessions. Thank you very much. Dr. Thomas, you are now
recognized.
STATEMENT OF MR. STERLING THOMAS, CHIEF SCIENTIST
U.S. GOVERNMENT ACCOUNTABILITY OFFICE
Mr. Thomas. Chair Comer, Chairman Sessions, Ranking Member
Mfume, and Members of the Committee, thank you for inviting me
to participate in today's hearing to discuss how innovative
tools can be used to detect fraud in Federal programs. As GAO's
chief scientist and throughout my career in industry, academia,
and in the intelligence community, I have seen great
advancement in data science. These innovations offer exciting
opportunities to improve government efficiency.
As you know, GAO, we are a nonpartisan watchdog for
Congress. We have expanded our science and technology team in
recent years, and it includes a group of data scientists. We
know firsthand from their work that AI holds great promise in
furthering GAO's mission and your goals of safeguarding the
taxpayers' dollars. My aim today is to offer three important
actions that will help us reach that goal. First, we must
continue and augment our traditional anti-fraud efforts.
Second, we must apply AI thoughtfully and ensure that we use
quality data to mitigate its well-known and well-described
risks. And third, we must ensure the Federal workforce has the
skills they need to apply new innovations like AI.
Regarding my first point, GAO has a large body of work on
fraud and improper payments in the Federal Government. We found
the Federal Government reported an estimated $162 billion in
payment errors or improper payments during Fiscal Year 2024,
and that is almost certainly an underestimate because it does
not include estimates for some Federal programs. We have
outlined numerous ways that Congress and Federal agencies can
tackle this problem with existing capabilities, such as
reducing data silos. For example, Congress could make permanent
the Social Security Administration's authority to share its
death list with Treasury's Do Not Pay system. According to
Treasury, just one year of access to this data resulted in
total net benefits of $109 million, but Treasury's access to
this data was set to expire in December, although I learned
that it just passed for permanent expansion, which is great
news to us. These traditional methods routinely prevent and
detect fraud. By enhancing them, we can save taxpayer dollars
today without new technology.
On my second point, like I said, I am optimistic about
innovation using AI, but we must be thoughtful about it. In
data science, we often say garbage in, garbage out. Nowhere is
that more true than with AI and machine learning. If we start
trying to identify fraud and improper payments with flawed
data, we are going to get poor results. AI is still in its
early stages of development and implementation, and rapid
deployment without thoughtful design has already led to
unintended outcomes. Before pouring data science on the
problem, we need solid, reliable, ground-truth data and a human
in the loop to ensure that data reliability and the application
of the technology. GAO has an AI accountability framework which
lays out these and other principles. One piece of advice that
emerges from such principles is to find a solution that
produces the desired result with the least complexity.
For example, in response to our recommendations, the Small
Business Administration screened all the PPP, or Paycheck
Protection Program, loans made before December 2020 with a
rules-based tool just looking for indicators and not using AI,
and they still identified $4.7 billion in loans that went to
ineligible recipients or were used for unauthorized purposes.
To build on that foundation, we need more innovation in
government. We recommend one way to do this is that Congress
could establish a permanent analytics center of excellent, like
the PRAC we have been talking about, to distribute tools to the
community that more efficiently and effectively identify and
prevent fraud and improper payments across the government.
My final point is that harnessing innovation also requires
a Federal workforce that has the right skills, but agencies
continue to face barriers in hiring, managing, and retaining
staff with these advanced technical and data science skills.
This is another area where GAO has made recommendations, and we
have explored innovative big-picture ideas, like establishing a
digital services academy. Rapid advances in AI capabilities
hold great promise to reduce fraud in Federal programs. We, at
GAO, believe that the Federal Government can and must realize
those opportunities and do so upon a firm foundation of
reliable data and digitally skilled workforce.
Chairman Comer, Chairman Sessions, Ranking Member Mfume,
and Members of the Committee, thank you, and I would be happy
to respond to your questions.
Mr. Sessions. Dr. Thomas, perfect. Five minutes. That is a
10.0, so congratulations. I would now like to go to questions.
We would go to the distinguished gentleman from Kentucky. The
gentleman is now recognized. Mr. Chairman.
Chairman Comer. Thank you, Chairman Sessions and Ranking
Member Mfume, for always delivering on good, quality
Subcommittee hearings.
Last week, the full Committee held a hearing to examine the
massive fraud in Minnesota social services program that
resulted in at least $9 billion in taxpayer funds being wasted.
Much of this fraud occurred because there were no proper
safeguards put in place to prevent fraud before the funds were
paid. There was also too little oversight on how the money was
used once it went out the door to recipients. During the Biden
Administration, Democrats rushed out Federal funds with
virtually no safeguards, resulting in massive theft of taxpayer
dollars. Republicans repeatedly warned that the absence of
guardrails would invite waste, fraud, and abuse. Identifying
fraud before money goes out the door is necessary to prevent
any further repeat of these failures and to protect taxpayer
dollars. We need to get ahead of the criminals, continue to
help the Department of Justice in arresting, prosecuting, and
jailing those responsible, and ensure Federal programs serve
those who are truly in need. This Committee, hopefully, is
serious about this. I know the Majority is on the Committee,
and we plan on continuing to identify waste, fraud, and abuse,
and hold people accountable.
My first question for Mr. Dieffenbach, how could PRAC tools
and analysis have been used to stop the large identity and
eligibility schemes that defrauded programs in Minnesota?
Mr. Dieffenbach. Thank you, Chair Comer, and, again, thank
you for your support. The hallmark of most fraud schemes is
that people hide information, so leveraging data analytics
allows us to see patterns, trends, anomalies, hidden
connections to shine a bright light on what is actually
happening. That is the path forward. So, we have to assemble
the right data, the right team, the right tools, which we
already have at the PRAC, thanks to your support. We just need
to think more about the jurisdiction of how we are employing
those tools, but data is the solution, absolutely.
Chairman Comer. Okay. Ms. Miskell, given the Treasury is
the last stop before payments from Federal programs get
executed, what authorities would help the Department identify
and stop high-risk payments for additional agency review?
Ms. Miskell. Thank you, Chair Comer. We are, as I
mentioned, implementing a number of payment verification
processes. So, we are applying the technique of trust, but
verify, doing some basic checks before agencies can certify a
payment. One of the pieces that we are missing is the ability
to ping authoritative Federal databases to confirm a payee ID,
such as a tax identification number or a Social Security
number.
Chairman Comer. Okay.
Ms. Miskell. So, we already received the data. We just
cannot verify it, so there are a number of, you know----
Chairman Comer. Okay.
Ms. Miskell [continuing]. Databases that would help.
Chairman Comer. Well, how does Treasury partner with states
to prevent fraud in state-administered Federal benefits
programs? Are there ways that Treasury could increase or
enhance their assistance? And I am sure Minnesota wants no
assistance based on what I have determined thus far in our
investigation, but how do you partner with states that want to
work to prevent fraud?
Ms. Miskell. Thank you, Chair Comer. Do Not Pay, thanks to
the Payment Integrity Improvement Act of 2019, authorizes
Treasury to provide Do Not Pay services to states that
administer Federal funds. However, it has been underutilized.
We think that it can be part of a multilayered approach, so
things like Do Not Pay before a state issues payments to sub-
recipients will be very useful. We can also work to address
some of their common challenges by adding additional data.
Chairman Comer. Okay.
Ms. Miskell. We know this works.
Chairman Comer. And last question for Mr. Thomas. How can
AI and machine learning be used to detect and prevent large-
scale fraud schemes? What type of anomalies do these tools flag
for investigators to followup on?
Mr. Thomas. So, all data science algorithms, inclusive of
machine learning and AI, are going to produce indicators of
fraud. It is critically important, and each of the programs we
have talked about today does this, has a fraud investigator, an
analyst who is an expert in the tools, techniques, and
technologies that fraudsters use to look at the data coming
out. So, the types of things you are looking for are just as
what were mentioned earlier: patterns of behavior that do not
fit the expected patterns of behavior of someone who is using
the money for the intended purpose or for the intended program
design.
We talk about this at GAO, and we publish this, and we
support the Federal Government and states and local governments
in using our Fraud Risk Framework, which is designed to help
them develop these indicators for a fraud risk management plan,
which would then feed into algorithms, machine learning, AI,
other data science methods, all acceptable, that could then be
used to track and monitor potential fraud while the program is
in execution. That is the purpose of it, is you design the tool
to find the behaviors that you want to get rid of.
Chairman Comer. Very good. Well, we look forward to working
with you all as we proceed with this fraud investigation that
is starting in Minnesota, and I have a pretty good feeling that
it is going to expand to several more states. So, with that,
Mr. Chairman, I yield back.
Mr. Sessions. Thank you very much. The gentleman yields
back his time. Mr. Mfume, you are recognized.
Mr. Mfume. Thank you very much, Mr. Chairman. Mr. Thomas, I
want to start with you because I just find some of what you
said to be absolutely fascinating, almost unbelievable. And the
main thing is the inability, and maybe, Ms. Miskell, you can
touch on this also, to be able to get tax IDs, Social Security
numbers, or other identifying information that we have got in a
number of different silos that you cannot seem to get access
to. The Chairman and I both think that there is a way to break
through this. We just need to know where the log jams are.
Could you talk through that for a minute?
Mr. Thomas. Yes. So, yes, data silos are a problem. I
appreciate that. I talked about it. You talked about it. It is
very clearly identified. You know, an example, the Social
Security Administration with the death file, is that they feel
like the Privacy Act is making it difficult for them to share
that with everybody. Now, privacy is an important concept,
particularly in the age of AI, but there are opportunities that
Congress could look into of modifying potentially the Privacy
Act or putting in exceptions that allow the data to be shared
specifically for the purpose of fraud investigations. That
seems to be a fairly, you know, important component of some of
the silos. Some of the other silos you have seen in our studies
at GAO also had to do with incompatible or old networks, you
know, modernizing the IT infrastructure of some agencies so
that the data can be accessible to tools like Do Not Pay, as
well as some of the analytic tools that the PRAC uses.
You know, keep in mind that there are opportunities to
improve the data and make it more available, but the technology
is old and does not have the ability to share information back
and forth through Application Programming Interface (API)s or
other methods that makes it very difficult. So, those are some
of the things we have talked about and made recommendations
about.
Mr. Mfume. And what was the dollar amount saved as a result
of the Social Security model? Was it $4 billion?
Mr. Thomas. I think it was $4.7.
Mr. Mfume. Mm-hmm.
Mr. Thomas. You know, I mean, there is tremendous
opportunity there to use this data together, you know, but it
is a matter of these are organizations that operate separately,
have their own infrastructure, and, you know, need funds to
modernize, and then make the sharing more available, in
addition to the statutory changes that we have talked about.
Mr. Mfume. And can you talk a little more about this
digital services academy? It is a concept, I assume----
Mr. Thomas. Mm-hmm.
Mr. Mfume [continuing]. But for those of us who are not
intimately aware of it or familiar with it, help me to
understand. I think this is about creating the people who will
have the proper training to be able to do what is necessary.
Mr. Thomas. Yes. So, I like the Chairman's idea of
storytelling, you know. What does the ideal AI-enabled analyst
look like? Well, it is somebody who is trained both in anti-
fraud skills and investigations, but also knows a lot about
data science, computer science, as well as artificial
intelligence. And so, in order to train those people, you know,
we need to set up an opportunity for them to get the training
and, you know, leverage their already interest in being a
public servant, so a digital services academy could provide
that. You know, think about upskilling analysts that already
have the interest, you know, the passion to work in this anti-
fraud domain, but also just want some additional analytics,
data science, and AI skills. That is the concept behind it, you
know, help people develop this training. One of the challenges
of this field, though, that we see in other technology fields,
is that there is quite a bit of competition with private
sector.
Mr. Mfume. Mm-hmm.
Mr. Thomas. You know, they are obviously very interested in
people with these skills as well for different purposes often,
but, you know, that is a challenge for hiring people in the
Federal Government.
Mr. Mfume. You talked about, while we get to where we want
to go, that while we are where we are, that there are certain
behaviors that sort of go outside of the norm that should call
the attention to potential fraud that is occurring. I just need
you to take a few moments to talk about what those behaviors
are and why people who are administrating programs do not
understand that.
Mr. Thomas. That is probably a better question for my
colleagues, if that is okay.
Mr. Mfume. Ken, go right ahead.
Mr. Dieffenbach. Happy to take that. So, what we are
looking for again is patterns, trends, things that are unusual,
and it all starts with a risk assessment, similar to what Dr.
Thomas said about the GAO Fraud Risk Framework. When we talk
about fraud, that is a broad term. So, what specific concerns
you have--eligibility, identity verification, their financials,
their claims--and once we nail down what exactly we are most
concerned about, we can determine what is expected, what is
normal, what pattern, and then we can automatically, and
machine learning can do this much better than a human being,
can say that the frequency of claims, the frequency of
applications is abnormal. That is three times what we would
expect on a Saturday night. The frequency of claims from this
geographic region, from this ZIP code, from this IP address is
not anything we would ever expect.
And again, a lot of this is based on statistics and data
modeling, so it is not a human being sitting there saying this
looks unusual. It is literally a machine who pops it up and
says this is five times what is expected. And again, once you
start with the risks and then use the model, we can be much
more efficient in addressing this issue.
Mr. Mfume. My time is up. Mr. Chairman, if you would allow
me one other question here.
Mr. Sessions. The gentleman can certainly ask.
Mr. Mfume. Are agency heads being told this? I mean, is
there any training? If I am the secretary of this or the
administrator of that, I know I am responsible for all this
money, and if there is fraud, waste, and abuse, I am going to
have to account for it. But do they know what to look for in
terms of abnormal behaviors, whether it is ZIP code or anything
else that you mentioned?
Mr. Dieffenbach. Sure. I cannot speak to the entire Federal
Government, but I can tell you I spent the last 29 years as a
fraud investigator, mainly in grant fraud and public
corruption, and there are agency officials at all levels that
are intimately interested and aggressive about wanting to know
from OIGs. Every single OIG does an outreach program where they
teach those willing to listen that these are the red flags,
these are the concerns. But the biggest single challenge, sir,
is that this is an agency responsibility, and so they have to
have commitment from the top to the bottom, tone at the top, to
address the risks, identify the risks, address the risks, and
seek out that, and we do see that in some places in the
government, but it is not across the board.
Mr. Mfume. So, a mandatory requirement would help us get to
where we want to be.
Mr. Thomas. Yes, and if I can just add, because at GAO, we
do look across the government, and you are absolutely right.
The mandatory requirement that we have recommended, and we
recommend to all agencies, is they build an analytics-based
fraud risk analysis team so they do the work that my colleagues
are talking about. And we make these recommendations, we track
these recommendations to make sure that where they do respond
to us, they implement them.
Mr. Mfume. Thank you. Mr. Chairman, I yield back.
Mr. Sessions. Thank you very much. The gentleman yields
back. We have now moved to the distinguished gentleman from
Tennessee. The gentleman is recognized.
Mr. Burchett. Thank you, Mr. Chairman, Ranking Member. My
mama used to have a saying, and I always hear her saying that
in the back of my head, ``I hate to make my living off the
suffering of others,'' and it seems that these people are
ripping us off everywhere, and that poor folks cannot get what
they need because we have got limited money and unlimited
needs, so I thank you all for what you do. I have always
thought that those GAO reports, and I have talked about those,
always reminds me of that last scene in the Raiders of the Lost
Ark where Harrison Ford is telling them, he said, ``Well, where
is the Ark of the Covenant?'' ``Oh, our top people are looking
at it.'' ``Who is?'' He said, ``Our top,'' and then you show it
going into this vast warehouse. And I always think those GAO
reports are there because, brother, I have been here for eight
dadgum years, and I have not seen a dadgum report yet, but I am
fixing to, and I will talk about that a little later, but not
right now.
What obstacles do you all have, the Federal agencies face
in this fraud prevention? Either one, and both of you all--sir,
ma'am--your names. My name is Burchett, so nobody ever gets my
name right, so I am not even going to attempt you all's, so you
all.
Mr. Dieffenbach. Thanks for the question. Obstacles include
access to data, but risk assessments, agency buy-in, resources,
and I think the biggest obstacle is where to start sometimes.
But I think, especially at the PRAC, we have built some tools
and have some great examples of where to start and what some of
the key lessons learned are from the pandemic. So, I think the
best starting point is, what do we know about the risks that
are out there and how can we address this?
Mr. Burchett. Ma'am?
Ms. Miskell. Thank you for the question. So, I agree with
Mr. Dieffenbach. Treasury can play a unique role, and to help
scale that prevention, we have a dedicated team of analysts,
data scientists, and we can provide data more centrally so not
every little program has to create data connections to the
important information that helps to safeguard the dollars in
their programs. So, we can scale that prevention, and we can
also help them understand best practices and, then again, that
last line of defense before the payment is made, being able to
flag that risk.
Mr. Burchett. Sir?
Mr. Thomas. And I will get you those reports, so.
Mr. Burchett. You are fixing to, I can guarantee it,
brother.
Mr. Thomas. Yes. Yes.
Mr. Burchett. Me and you are going to be best friends.
Mr. Thomas. I love it. So, yes, I mean, just building on
what they have already said, we recommend a systemic risk
management plan for all of these programs. And again, these
tools are great for leveraging by small programs, but the large
programs as well should design the risk management framework we
talk about into their risk management plan, and then build the
indicators they need so the analytics will actually find the
fraudsters. Again, before--we have talked about the pay-and-
chase method--before the money goes out the door.
Mr. Burchett. You all may have said this, but my limited
ability at comprehension is very limiting. When I was the
county mayor, I found out that a lot of our different
departments had different procurement avenues, and we
consolidated those and it saved us a heck of a lot of money. Of
course, it cost some people their jobs, but it was just a
duplication, and we needed to move that on. Do you all think
that this enhanced data sharing is really going to help
increase the detection and prevention of fraud or, what, just
be window dressing?
Mr. Dieffenbach. Absolutely. I will make one point that the
Federal Government disburses a trillion dollars a year in
Federal grants. Eighty-five percent of that, just over $900
billion, goes to state governments to disburse, and they are
absolutely a rich environment where we can leverage data to
give them insights that they have not had before.
Mr. Burchett. You all are itching. Go hit the button. We
are good. Go ahead, ma'am.
Ms. Miskell. Thank you. Yes, we have lots of examples. So,
Do Not Pay also operates the back end of the PARIS system,
which helps states identify duplicate beneficiaries for
programs like Medicaid. Last year, through that data matching,
we prevented $1.3 billion in duplicate payments. We are
enhancing that with additional death data. In November, 19
states subscribed to that, and we identified $156 million in
just that one month.
Mr. Burchett. Wow.
Ms. Miskell. So, yes, these things matter.
Mr. Burchett. Sir?
Mr. Thomas. Yes. So, that over a trillion dollars you talk
about that goes across all of the agencies and goes out to the
states and localities, they are audited, and it goes into what
is called the Federal Audit Clearinghouse, right? We built a
tool that evaluates that and has identified indicators of risk,
patterns of potential fraud using machine learning AI tools at
the GAO. It goes across all of the different agencies of
government. So, yes, all of these things matter, but, and you
will see this in one of our reports, we talk about the quality
of those audits are part of the problem, and validating that
the auditors are actually doing their job and looking into the
programs the way they are supposed to so that that data does
actually exist, not only for that particular program, but
across the rest of the government so we can look at large
patterns of fraud that do go across agencies.
Mr. Burchett. Thank you. I am out of time, but, Mr.
Chairman, I think these good folks here do great work, and I
think they are getting used just a little bit by the fact that
we have too many staffers, that, really, these Committees are
just so large, there is no way that can be monitored. And I
think that they are, figuratively, in bed with some of the
lobbyists, and I believe they use these folks to say we are
going to do a report on this, and it takes you a year to get it
done. And then by the time you get it done, we have moved on to
another bright, shiny object. And both parties are guilty of
that, and I think we need to get to the bottom of it, and I
think we need to utilize them more in a quicker fashion. So,
thank you, Mr. Chairman, thank you, Ranking Member, for you
all's indulgence.
Mr. Sessions. Chairman Burchett, thank you very much. And
by the way, Dr. Thomas, it would be well worth your time to
look at some of these important GAO reports and others that
Inspectors General because there are disconnects, as you allude
to, and I want to thank the distinguished gentleman----
Mr. Burchett. Yes.
Mr. Sessions [continuing]. For his time.
Mr. Burchett. I am on it like a cheap suit. Please contact
my office. Mr. Thomas, both of you all, all three of you all.
Thank you all.
Mr. Sessions. The gentleman yields back his time. Ms.
Norton, you are now recognized.
Ms. Norton. Thank you, Mr. Chairman. Thank you to our
witnesses for being here today to discuss substantive reforms
to address this important issue. As my colleagues noted, we, on
the Government Operations Subcommittee, have been focused on
fraud and improper payments not as a partisan issue, but as one
that must be addressed for the benefit of all Americans. We
have heard in today's testimony about the importance of
catching fraud before program benefits are paid rather than
tracking down fraudsters to try to recover funds after the
fact. I support efforts to remove [sic] agencies toward this
more effective strategy, which will also leave a greater
portion of funds Congress appropriates available to provide
services and support to intended recipients. It is also
critical that agencies conduct their fraud prevention
activities efficiently so that funding and services can reach
the people without unnecessary delays.
Ms. Miskell, what are some ways the Federal Treasury
Department is trying to help Agencies conduct fraud prevention
checks today more quickly?
Ms. Miskell. Thank you, Chairwoman. So, I mentioned Do Not
Pay. We are making it more useful by adding additional
databases. So, focusing right now on commercial databases and
with more authority, those really high-value Federal databases.
So, we want every program, and there are thousands of Federal
programs, to use Do Not Pay before making award and eligibility
determinations. We also have a concept called continuous
monitoring. So, you may say that an organization looks like it
passes all of the checks, but something may happen, like, found
in an audit. This continuous monitoring can flag that risk, you
know, as payments are going out the door. And then finally,
Treasury, as that central disbursement organization, can flag
risk. We have a unique role where we can see cross-government
payments. So, when my friend here, Mr. Dieffenbach, tells me
that a specific entity or individual is at risk, we can scan
the cross-government payments and identify other programs at
risk.
Ms. Norton. Mr. Thompson, what tools and resources will
agencies need to be able to effectively prevent fraud without
unnecessarily delays in payments?
Mr. Thomas. So, they need both the fraud indicators for the
programs that they have set up, but also the data science tools
that we have been talking about that the program, PRAC, has
been using, as well as access to tools, like Do Not Pay. All of
these are examples of tools that can be implemented in a fast
way so that you can prevent potential fraud before the money
leaves. Now, we have to set them up so they move quickly and
have access to the appropriate data for each of the programs,
but that is part of what the Fraud Risk Framework we provide is
all about.
Ms. Norton. I hope this Subcommittee will continue to focus
on substantive reforms rather than partisan attacks, and I
yield back.
Mr. Sessions. Thank you. I appreciate the gentlewoman. The
gentlewoman yields back her time. Now I would recognize
distinguished young Chairman from Alabama, Chairman Palmer.
Mr. Palmer. Thank you, Mr. Chairman. I have been working on
reducing our improper payments for 11 years since I have been
in Congress. One of the first things I did, I was on the Budget
Committee and insisted that we take reducing improper payments
into account in our budget process. And I am sad to say that
when I came into Congress, our improper payments were running
somewhere around $130, $150 billion a year. It has since
surpassed that considerably. During COVID, it got even worse,
particularly with payroll subsidies that we sent out, the
additional unemployment insurance. The fraud was massive on
that end. I think one state, in particular, in the first four
months sent out about a billion dollars in fraudulent payments.
I know that we are working on that, but I want to know what
progress has been made in that area to address the fraud from
the pandemic funding.
Mr. Dieffenbach. Thank you, Congressman Palmer. We have
done a tremendous body of work on identifying what went wrong
during the pandemic. We have issued lessons learned reports and
a blueprint for program integrity about how to ensure this
never happens again, but let me answer your question quickly
with a story that Chair Sessions, I know, will appreciate.
One of the many projects we did, was we examined recipients
of HUD low-income housing benefits with the Social Security
numbers that were also used to obtain Small Business
Administration PPP loans. So, folks that were claiming a low
income to get the housing benefit and a high income to get the
PPP loans, often forgivable loans. We found 40,000 instances in
which the disparity in income between those two programs was
ten times or greater, and that impacted $860 million in PPP
loans.
Mr. Palmer. Mm-hmm.
Mr. Dieffenbach. So, I think telling those stories is
important. That is one of many, many risks in programs, and the
end to that story is that ID theft or lies occurred in the HUD
program or the Small Business Administration (SBA) program, or
both, and legitimate victims that needed housing or PPP loans
did not get them.
Mr. Palmer. It has been mentioned a couple of times about
one of the problems with all of this is the failure to have
data systems that interface.
Mr. Dieffenbach. Correct.
Mr. Palmer. I was astonished at the fact that we could not
even get agreement within Congress on access to the Social
Security Master Death File so that we could check that against
some of the things that were submitted by the states. I also
found--the GAO was tremendously helpful, Mr. Thomas, in this--
but about 55 percent of the problem was administrative error,
failure to verify eligibility, which I think is a massive
problem right now, and antiquated data systems. And I had
suggested that one of the ways to approach this to make major
improvements in agencies' ability to eliminate improper
payments is that, rather than be punitive toward the agencies,
that we say that whatever you save, we will take part of that
to bring your data systems into the 21st century so that we
have the ability to interface across the entire Federal
Government. Would you like to comment on that, Mr. Thomas?
Mr. Thomas. Yes. I mean, certainly, changes in incentives
are something that we have recommended. I do just want to add a
note of, not caution, but just thought is that improper
payments are not always recoverable payments. They could also
just be----
Mr. Palmer. I understand.
Mr. Thomas. Yes, they could just be errors in the dataset,
yes.
Mr. Palmer. Yes, but when you got over $200 billion, if
you----
Mr. Thomas. Yes.
Mr. Palmer. I mean if you recovered a tenth of it----
Mr. Thomas. Yes.
Mr. Palmer [continuing]. That would pay for a lot of what
we need to do in terms of improving our data systems.
Mr. Thomas. You are right, yes.
Mr. Palmer. The other thing is, and I know some of this is
a sore subject with some of my colleagues, but some of the
bills that have been passed left some pretty big doors open. In
the Inflation Reduction Act, with some of the green energy
subsidies. The Affordable Care Act (ACA) premiums, I think the
GAO report was billions of dollars in fraud on the tax subsidy
for the ACA. Same thing was true in the Infrastructure
Improvement and Jobs Act. I think that Congress has got to do a
better job of writing legislation that helps that, but we are
really going to be dependent on Federal agencies to do the
proper oversight here, and then when you need help, you have
got to come to us because this is a massive, massive problem.
When you are talking $200 billion, you are talking $2 trillion
over ten years, plus interest. We are borrowing that money to
send it out improperly. Thank you, Mr. Chairman. I yield back.
Mr. Sessions. Thank you very much. I appreciate the
distinguished gentleman. His avenues of working in this area
for his career have been not just stellar, but they have really
illuminated many people, including conversations that you have
had with our team about the need to make sure that they see the
bigger picture also, and I want to thank the distinguished
gentleman. The gentleman yields back his time. Mr. Frost, you
are now recognized.
Mr. Frost. Thank you so much, Mr. Chairman. I am glad we
are holding this hearing today. I support innovation to tackle
fraud that is executed safely, and we have heard testimony
today about the promise of artificial intelligence and other
new technological tools that may be used to improve fraud
detection across the entire Federal Government. That said,
machine learning and artificial intelligence systems leverage
massive amounts of data, and specifically personal data. Ms.
Miskell, how does Treasury protect data, and how can I address
concerns from my constituents on their data privacy?
Ms. Miskell. Thank you, Congressman Frost. I sincerely
appreciate that concern. We take privacy security very
seriously at the Treasury Department. Specifically, within Do
Not Pay, privacy is built in by design. We follow the principle
of least privilege, meaning that a person can only receive a
response back on information that they provided. We operate in
a Federal Information Security Modernization Act (FISMA)-high
environment, which means that it is the highest standard in
terms of Federal cyber. We apply continuous monitoring, and we
are also transparent to the public. We provide transparent
systems of record notices, communicating what data we have on
individuals and how it can be used.
Mr. Frost. Thank you. Mr. Thomas, what are the concerns
about having unknown quantities of private data and sensitive
information in a master database?
Mr. Thomas. So, you know, there are technologies that, if
you have them in separate data systems, that you can control
the way they query back and forth to keep the data private and
controlled. But you are right, once you put it in a master
system where everything is all together, which is the design of
AI--AI needs that to train--all of a sudden you are now
dependent upon the actual AI prompt to control your privacy.
The term that the AI industry uses for that is ``alignment.''
There have actually been excellent studies recently in how you
can actually break alignment in an AI system so you can get it
to return information back to you that is not designed to
return to you, and even safeguards were put in. So, this is an
area of concern, you know, and that is why we talk about it is
important to use, potentially, the least complex solution to
trying to get to where we are using machine learning, where you
do not have to train on everything, but you can still maintain
the controls that Treasury is currently doing with their Do Not
Pay system.
Mr. Frost. Yes, thank you. I know there is, you know, many
different things that are being done to make sure data is kept
safe when people query information or when information goes
from agency to agency. But part of the reason I have this
concern is because it is also dependent on the conduct of the
Administration and different things that are going on. So, for
instance, you know, under President Trump and back when Elon
Musk was more involved and DOGE was going on, they conducted
attacks on our Federal data systems and cybersecurity, right,
and this is public information, right? They brought unsecured
servers into Federal agencies. They reportedly exported unknown
quantities of private data that we still are not really sure
how much it was or where it was from. And then the one that is
most concerning for me is they contracted with private
companies to merge Americans' sensitive information into a
master database that would give the Administration surveillance
powers and put data at greater risk of hacks for outside
people. And so, it is just important to me that we ensure that
any new innovative tools are not politicized and misused by
this Administration and any administration, and that they are
not abused by private corporations that have been brought in to
help us with different projects.
We also have to recognize and expand successful existing
tools and not toss them aside either. There are tools that some
agencies are not using enough that I think needs to be
explored. So, Ms. Miskell, the Treasury's Do Not Pay system,
which you brought up earlier, is one such effective program.
What do you hope other agencies can learn from this model?
Ms. Miskell. Thank you, Congressman. Agencies can learn a
lot from using Do Not Pay. We are helping to integrate the data
for them. So, we talked a lot about technical debt,
longstanding issues with resources. We can provide that
information securely to the people that need it. And I just
wanted to mention in the conversation around AI, we have
successfully used machine learning to detect check fraud. It
has been an outstanding use case. In 2024 and 2025, because of
this technology, we prevented and recovered $1.9 billion, so,
again, used responsibly, this technology can be really
transformational, but as Mr. Thomas mentioned, it relies on the
data. So, we had really good data about the checks we issue and
we could compare that, and that is what is important about
using AI.
Mr. Frost. Yes, and obviously, more and more interagency
collaboration is important, too, and is free to use. Last
question, real quick. Mr. Thomas, what basic fraud protections
could agencies use that they are not already?
Mr. Thomas. Well, as we talk about, you know, implementing
our Fraud Risk Framework, and developing models as programs are
being developed is one of the most important things they can
do, in addition to actually implementing data-science-based
fraud programs. So, they are bringing in data scientists,
implementing the algorithms that the PRAC has been able to
implement, proven tools that can identify fraud that, just like
you said, exist today.
Mr. Frost. Thank you, and thank you for letting me go over,
Mr. Chair. I yield back.
Mr. Sessions. Thank you very much, Mr. Frost, and we
appreciate you being at this hearing today. Your support of
this is very important, and you have been to all of our
hearings, and not only showed up to ask important, leading-edge
questions, but I think worked as part of the team, and I
appreciate and respect that very much.
Okay. We have got several Members that asked to be waived
on and others who have indicated they might be here, here. I
have chosen to put myself as last, but pending them arriving, I
am going to go ahead and use my time.
All three of you have been very good, I think, rather
exceptional at taking me up on this view about telling a story.
And I think Mr. Mfume really began some of this, for sure, with
Mr. Dieffenbach, but I want to go to Dr. Thomas, if I can
first, and then have either of you then join in. This telling
the story, really, that is the power of AI, not just machine
learning, but across the avenues that each of you have. And I
would like for you, Dr. Thomas, to not recreate what we talked
about yesterday in my office, but pretty close to that, about
how important these exercises are on a, really, program-by-
program basis, that you have found characteristics that you
spoke about, about how certain things that might be a request
under one program found themselves across, if you used AI to
highlight where there were inconsistencies, to look at
inconsistencies that would draw you to those things. Do you
mind taking a minute? Mr. Mfume, I think, would learn a lot
from this. Perhaps he knows it, but hearing from you, Dr.
Thomas, would be important.
Mr. Thomas. Yes, I appreciate that. So, we have a
demonstration program that I talked about earlier that we call
FACET, that is really built for, just as Chairman Sessions
talked about, identifying indicators of potential bad behavior,
really just datapoints in a large data collection, and this is
the Federal Audit Clearinghouse that, you know, can be used
across programs. So, I will give you a couple of thoughts
there.
The Federal Audit Clearinghouse, you know, houses over a
trillion dollars of spending every year, and these are audits
that are done not only on Federal programs, but these are state
programs that are using Federal money and local programs are
using Federal money. We use a combination of AI, natural
language processing, and machine learning to identify, you
know, one pattern here, say that, you know, is in a food
support program, that then is replicated in another program in
a completely different locality. These indicators of fraud do
not mean fraud is happening, but it means someone should take a
look at it. And that is really the message we try to put to
people, is that not only should they look at it, they should
understand is it actually a problem with the program, is it a
problem with the way the audit was done, or was it a problem
where the design of the program is not actually collecting the
right piece of data? And then you can help that program say,
okay, all new grantees should produce this piece of data that
will then go into your fraud risk model.
You know, we have been doing it now for several years, and
it has produced lots of wonderful examples. We actually
published several of these examples on a website that is,
basically, a pattern storage site of the patterns of fraud that
you can go to at the GAO's website and look at and see these
different patterns that have been identified through this tool
and other tools as well as some of my colleagues' tools.
Mr. Mfume. Could you make that available to the Committee,
this publication?
Mr. Thomas. Yes.
Mr. Mfume. It is a publication. I would really like to see
it----
Mr. Thomas. Yes.
Mr. Mfume [continuing]. Because I would like to see what
are the examples that----
Mr. Thomas. Mm-hmm.
Mr. Mfume [continuing]. Are so clear that people are
missing.
Mr. Thomas. Yes. We will provide the reports, and it is a
live website you can go to, yes.
Mr. Mfume. Thank you.
Mr. Sessions. Anyone else? Once again, this is telling the
story about how important it is, and it might be Social
Security. It may be a death file. It may be something else, and
I know that we could say, well, there is a hesitancy by the
agency, Congress, you need to address that. I get that, but I
am more into the power that really exists of the tools that you
have if we allow you to fully utilize them, if we look at them
and say to you, yes, please go do this. So, anybody want to add
to that? Otherwise, I want to go to my last question. Mr. Jack
is here, and he is taking time, and I want to get to him.
Mr. Dieffenbach. Thank you, Chair Sessions. Great question.
Under the umbrella of artificial intelligence, we are
leveraging it. We have built the fraud prevention engine, as I
talked about, that uses a variety of techniques. And the best
analogy I can give you is if you put the best government
analysts in a room and give them an application for Federal
funding and access to databases, they can check, and they can
review, and they can look at our reports, and they can look at
prior history and all kinds of things. This machine can do that
very rapidly, almost instantaneously. The system we built can
do 20,000 applications per second, and it is using all kinds of
those technologies to give us an edge on this information war,
if you will, to shine light on what is actually occurring.
That, to me, is the single biggest challenge, is fraudsters are
hiding what they are doing, but using these technologies in a
responsible way with the right datasets, addressing the right
risk, can give decisionmakers instantaneous visibility into
what is actually occurring.
Mr. Sessions. And they can go across all the entities that
you allow them because you develop----
Mr. Dieffenbach. Proper jurisdiction, absolutely, yes.
Mr. Sessions. All right. One last question because I know I
am at my timeframe, too. I want to ask you, there was a great
conversation last week and also in the media across the
country, and that that deals with the states. And we have
already heard--Ms. Miskell talked about how this is a huge
amount of money. Tell me about your recommendation to Mr.
Mfume, myself, Mr. Jack, and this Subcommittee about how we
ought to be looking at conversation with states. For instance,
we know that some of the states want to do business with us.
Some do not. We know that, however, we have Federal money that
is at risk, and I think that they would, through our efforts,
want to do things. Do we give them money? Do we ask that they
update their databases? Do we share information? There may be
ten or fifteen things that I could think of, but you are closer
to that than I am. What would be your advice to this
Subcommittee about doing business with states on data and
information related to Federal programs?
Mr. Dieffenbach. Sure. I have done fraud investigations
over my career in, I think, 35 different states, and I can tell
you that they vary dramatically in their interests, their
resources, their level of responsibility. So, I think it starts
with conversations with those states that are interested in
starting the conversation. As a pilot, as we discussed in your
office yesterday, I think our tools we built at the PRAC, along
with the GEO Risk Framework and other tools from Treasury, we
can add tremendous value to what they are doing. Again, I would
submit it as a pilot project to prove its worth, but with
additional insights to those states in particular programs
looking at particular types of risks that they cannot wrap
their arms around, they do not have the data for, and build off
from there, and prove that that concept indeed can work, which
I think it can.
Mr. Sessions. Members of the panel here, we are meeting
individually, separately, and so they did not have the
advantage, and I asked, where would you start and ask states,
and then I had my ideas. And I really think that it is a
conversation that we want to have Mr. Mfume on, and each of
you, and perhaps some Inspectors General, and perhaps some in
your agency. So, I see that as a definite, walking out of here,
first thing we need to do, in addition to the legislation that
I think Mr. Mfume and his Members and mine would agree to. I
want to thank you very much. I am sorry. I am well over my
time.
We will now move to the distinguished gentleman, Mr. Jack.
The gentleman is recognized.
Mr. Jack. Thank you very much, Mr. Chairman. I want to
thank our witnesses for testifying today, and at the outset, I
always like to frame this for constituents and everyone
watching back home. But one of the things I want to focus on
today, and the Chairman is probably too humble to acknowledge
it, but one of the accomplishments that he had thus far in this
session, among many others over his career, was the inclusion
of HR. 2277, the FACT Act, in the One Big Beautiful Bill Act,
which, of course, as Mr. Dieffenbach no doubt knows, extends
PRAC's authority through 2034.
And I think it is important to focus that on the outset,
because when we talk about the many accomplishments of the One
Big Beautiful Bill Act, this is one of them. And it is because
the bill was so big and had so many great things within it that
sometimes some of these accomplishments could be overlooked by
the media and others, but in this case, this hearing amplifies
just how successful that was. So, I want to commend you,
Chairman Sessions, for your leadership in introducing that
legislation and getting it into the One Big Beautiful Bill Act
for passage and enactment.
So, my first question to you, Mr. Dieffenbach, is, to help
our constituents know, my constituents and the constituents of
this Committee, could you walk us through what the world would
look like if PRAC had expired? If we no longer had access to
this, you know, great tool that is meant to root out waste,
fraud, and abuse, walk us through what life would look like if
this had expired and we had not been able to extend it last
year.
Mr. Dieffenbach. So, thanks for that great question,
Congressman Jack, and for your support. The PRAC has assembled
a phenomenally unique set of data about pandemic fraud, about
program fraud in general, about the patterns, the trends, the
anomalies. We have issued a number of alerts. So, had we
expired, the ability to provide the insights I just spoke to a
minute ago would be gone. There would be another disaster. The
Congress would fund an emergency data analyst capability, and
we would have to spend a year or two to rebuild that. So, we
have been able to continue to keep pace. The fraudsters do not
take naps or take breaks, so we have been able to continue to
build upon everything we have learned over the last six years,
and I think it has been a tremendous asset to the Congress and
to the taxpayer.
Mr. Jack. Well, thank you very much for your answer, and I
will next move to Ms. Miskell. I would first like to address,
if I could, I am very interested in Department of Treasury's Do
Not Pay system. Frankly, I have got a couple companies in my
district that work on this, so I am uniquely interested in the
topic. And I am just curious if you could share with us some of
the recommendations you may have, legislative action you would
like to see that would help make the system more comprehensive
and even more effective than it already is. I welcome your
thoughts in that regard.
Ms. Miskell. Thank you, Congressman, and to just put
simply, how do we best equip states? How do we best equip
agencies in preventing and detecting fraud? It is making it
easy to do the right thing, and we can do that with data. So,
Do Not Pay can be that simple tool, but it is only as effective
as the data within it. So, there are a couple of key data
sources that are Federal data sources that we have had a
tremendously difficult time accessing that we know would be
very valuable from GAO reports, from PRAC recommendations.
Those datasets include the ability to verify financial status.
And I am not saying, you know, exact numbers, I am saying a
threshold, like does Mr., you know, So and So make over a
million dollars last year.
Other data sources would help to verify identity
attributes, again, the taxpayer identification number, the
Social Security number, and again, we are not looking to get
full access to these databases, but just a simple yes/no to
identify identity theft. There are a number of commercial
databases that we have found to be extremely valuable as well,
and we are working to rapidly onboard those, so it is not just
Federal. It is also commercial.
Mr. Jack. Wonderful. Well, I hope you have had a chance to
get to know my former colleague, Francis Brooke, who serves in
Treasury alongside you. He is a wonderful guy. So, I appreciate
your testimony here today. I will close with Mr. Thomas, if I
could. Right as I was listening a few minutes ago, you were
talking about AI and the development of it. I am fascinated by
the utility that could offer in this space. I welcome any
closing thoughts from you on how best AI could continue to
strengthen our mission to eliminate waste, fraud, and abuse.
Mr. Thomas. Yes. Like, I said, there is tremendous
opportunity in AI. I think the foundational components that,
actually, Ms. Miskell was talking about are critically
important, and that is build a solid data collection of what is
AI or what is fraud so that AI can learn that. The challenge
with AI is that it does not know the difference if you do not
tell it.
And so, if we properly label all of these examples that
both of my colleagues are finding, collect the data that
represents this is fraud, we can then start to train a tool to
do this broadly. And then all of a sudden, you can leverage
without having to have the analyst read all of these audits and
go through the data by themselves that can be empowered with
this tool, that can pull out these are the indicators of fraud.
These are the patterns that just do not represent typical
behavior of a payee in this program, and now someone can go
look at it. That is only possible if we have this foundational
kind of gold standard of this is fraud database.
Mr. Jack. Well, thank you all for your testimony. I learned
a lot from today's hearing, and I yield back to our Chairman.
Mr. Sessions. The gentleman yields back. Mr. Jack, thank
you for taking time. I know that you were stressed in your
duties today, and I appreciate you taking time for this
important event. I have been asked to ask this question, so I
am going to extend myself if I could.
Unanimous consent. Agreed to.
Dr. Thomas, which agency manages the Federal Audit
Clearinghouse?
Mr. Thomas. So, the Federal Audit Clearinghouse is actually
the responsibility of the Office of Management and Budget. They
delegate that to the General Services Administration, and that
is actually who is operating it, and when we access that, we
work with them to work through their APIs. Now, they have had
some challenges with staffing recently, which has delayed our
ability to make some updates. I will give you a specific
example. Several of their newer APIs were mislabeled on what
versions they were, so it made the system not work
appropriately, but that is, you know, something that could be
improved, and so that is where it lives.
Mr. Sessions. So, what would you say is the current status
recognizing, as you have alluded to, the delay and perhaps the
misrepresentation of the data? Where does that stand today, and
who has the responsibility on your side at GAO and within the
Agency?
Mr. Thomas. So, the agency doing the work is the GSA.
Again, the responsibility is at Office of Management and Budget
(OMB). Within our group, it is our innovation lab that is
working with them to identify these errors and improve them.
Now, we have recently been working, getting better response
from them, but I think it gets back to my earlier statement of,
you know, just enabling them with a workforce that has the
technology skills to run an important program like this. And
there is over a trillion dollars a year in spending in the
Federal Audit Clearinghouse. It is a rich dataset if we work to
improve it and extract value out of it.
Mr. Sessions. So, that is what is at risk. I am not trying
to give the whole example of a trillion, but that is how big of
a problem this would help solve.
Mr. Thomas. That is how much is obligated within the
Federal Audit Clearinghouse database. Yes, that is what the
audits represent.
Mr. Sessions. Well, I respect that, and we will take that
up also. I think that this Subcommittee is very capable of
looking at things, and if a trillion dollar is not big enough,
we are in the wrong business. Mr. Mfume, do you have any
additional questions that you would like to engage this panel
on?
Mr. Mfume. I do not know that I have any questions. I just
have a couple of observations. You know, you and I have been at
this for some time now, and, fortunately----
Mr. Sessions. You have not gotten any gray hair from it.
Mr. Mfume. You just cannot see it. Fortunately and
unfortunately, this has gained a new set of energy and a whole
new set of interests because of recent events in Minnesota and
elsewhere around the country. I just think that we are on the
precipice of something real big here if we do it the right way.
And, you know, to the extent that we can get some interest
going in the other body, meaning the Senate, and find a way to
take advantage of all the information we have got to be able to
convey it to other Members of the House of Representatives so
that they get a burning desire to recognize that, whether we
are talking about the Do Not Pay mechanisms, or cross sharing
of information with these large datasets, or mandating agency
heads in a particular type of responsibility, the digital
service academy to train. And even though there is a great deal
of competition on the private side, behavior identification, I
just think that PRAC, OMB, the Treasury, GAO represents a great
deal of information that we should have the ability to try to
coalesce and put into one great big effort.
And I would think, Mr. Chairman, just as we look back over
the history of this Congress, things like Sarbanes-Oxley, which
changed the landscape in many respects, there is an opportunity
here. I am just having a little difficulty identifying. I feel
it, but I think the more we work through this, we will be able
to identify it. And so, I would ask the witnesses, please, to
commend to our attention, and particularly mine, any journals,
any writings that are current and recent, your own thoughts,
white papers, anything that has in it a set or subset of
information that you think any of us who are looking at this
could benefit from. And in our own way, Mr. Chairman, we are
going to have to find some sort of way to kind of absorb it
all. It is a lot, but I just see that it is like begging for
some sort of national response that would be so strong that it
would set the course over the next five or ten years, and at
the rate AI is moving, we are already behind, in my opinion.
So, I think this is a good hearing, clearly. I am glad the
witnesses have come out. I thank you for your support of this,
and I would yield back.
Mr. Sessions. The distinguished gentleman yields back. I
would like to go back to a conversation that Mr. Jack really
had with us talking about some of those things that you talk
about, successes, and the things that were making sure that
PRAC did live on, and Congressman Jack had it right, but we
also did it together.
Mr. Mfume. Mm-hmm.
Mr. Sessions. We did that together, and I think that if we
view the assets and resources, like of the organizations that
are here today, and really take them at their word and work
with them, you and I work together, this Subcommittee, I think,
can really make a difference. And perhaps we need to involve
the other body a little bit more, but I think that we have been
successful in putting things in must-pass pieces of legislation
that would make things better.
Two things. Number one, I now recognize myself for a UC
request. I would like to enter into the record two letters, one
from the Program Integrity Alliance and the other from the
United Council on Welfare Fraud, and these are on reforms
needed to prevent fraud. They are their observations.
Without objection, I would enter those into the record.
The second thing I would like to carefully address is some
conversation that was had today in relationship to staff, the
staff of the Subcommittee, the staff of the Committee. And I
want to commend our staff members on what I believe are
professional products for the right reason, in the right way,
with the right outcomes. And I do not have to engage in a
yelling match on this, but I want to say to each of my staff
members, thank you. Thank you to their service. Thank you for
making sure that they stand up in a professional manner. Many
of these people work long hours and do things that might take a
lot of time, but I want to reiterate that my opinion, and I
only have 27 years at this, few times have I seen a staff that
I felt like would have been engaged in anything that would be
unbecoming to professional conduct.
Second, I want to acknowledge, and we do this often,
because Mr. Mfume and I have oversight over Federal Government
operations--all Federal Government operations we have original
jurisdiction to--I think the Federal Government has great
people also. I think the Federal Government has people who are
devoted and dedicated. They do their job. They are public
servants. They get up. They do things that match or try to
match the needs of the American people. In every organization
as large as the government is, I am sure there will be some
that are through their time, and they are ready to leave or
they are ready to do other things. That is fine, that is fair,
but I think that I have seen a devotion and a dedication across
government. And I think Mr. Mfume and I, from our vantage
point, would say that we have confidence that we can move to a
brighter, better world and work with AI, and it will help us in
so many ways. Does the gentleman wish to make any closing
statement?
Mr. Mfume. Well, just what I hope and pray is an obvious
sort of reality. I keep going back to that dollar amount of
$533 billion as a max and $233 as a floor. That is a lot of
money. If we were able to just half that, I mean, think of all
the things that we could do in this country. We would not have
had a situation where SNAP benefits were being held from
children all across this country. We would not be in the
situation where we could not figure out how we are going to
provide ACA credits for people who are about to see gigantic
increases in their healthcare benefits. There are so many more
dollars we could be sending to states and municipalities, so it
is like we are almost beggars sitting on bags of gold. And that
is why, again, finding a way to get our arm around this issue,
and to take that money that is going out the door that the
criminals and the bad guys have been taking for years and to
keep it in-house so that we can do some good with it, if that
does not compel, Mr. Chairman, our colleagues to want to be a
part of this, I do not know what will. So, I am going to be
optimistic and hopeful at this point that that is such a
compelling argument that you and I will not have to sit here
for another three or four years trying to make this case,
waiting for others to catch up. I yield back.
Mr. Sessions. Mr. Mfume, thank you very much. So, let me
address the three of you. You have been impressive, you are
singularly impressive, and your ability to know each other and
work together on a common issue is important to the country. It
is always hard to know as you are working on something where it
is going to end up, but I think that by us working together, we
will speed that effort to get where we want to go. One thing
you can get from Mr. Mfume, as we promised last year and the
year before, we were going to followup. We were going to do the
things that would be necessary to empower the good outcome. We
were going to make sure that we ask questions that you consider
to be thoughtful and something that would lead to a better
outcome.
I want each of you to know how much we appreciate you, how
much we recognize that if we will take your nuggets of data and
information and good ideas about a better outcome, that we can
get there. And so, Mr. Mfume and I, in just a second, are going
to come down and shake your hand and thank you for being here.
I hope you are able to go home tonight and say to those that
you love the most, perhaps that love you the most, that you did
a good job today, that you presented yourself in a professional
way, that you defended the work that you do, and that you are
asking for more of it, not less of it.
With that said, without objection, all Members have five
legislative days which to submit materials and additional
written questions for the witnesses, which will be forwarded to
the witnesses.
Mr. Sessions. And if there is no further business, without
objection, the Subcommittee stands adjourned.
[Whereupon, at 3:52 p.m., the Subcommittee was adjourned.]
[all]