Curbing Federal Fraud — Hearing, House of Representatives (January 13, 2026)
Summary
Transcript of the hearing Curbing Federal Fraud: Examining Innovative Tools to Detect and Prevent Fraud in Federal Programs, before the Subcommittee on Government Operations of the Committee on Oversight and Government Reform, on January 13, 2026 (Serial No. 119–55). Opening statements are listed for Chairman Pete Sessions and Ranking Member Kweisi Mfume. Witnesses were Mr. Ken Dieffenbach of the Pandemic Response Accountability Committee; Ms. Renata Miskell, Deputy Assistant Secretary for Accounting Policy & Financial Transparency at the U.S. Department of the Treasury; and Mr. Sterling Thomas, Chief Scientist at the U.S. Government Accountability Office. Letters from the Program Integrity Alliance and the United Council on Welfare Fraud were entered.
Summary drafted by a model from the document's text below and checked by script against that text before publication. It is a navigation aid, not a reading of what the document proves. Where AI is used
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“Curbing Federal Fraud — Hearing, House of Representatives (January 13, 2026).”
Original at www.govinfo.gov. Archived at Pandemic Darlings, https://pandemicdarlings.com/source-documents/chrg-119hhrg62435-curbing-federal-fraud-examining-innovative-tools-to-845cdb8020/
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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
(
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 Minority
MIKE TURNER, Ohio 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 Member
GARY PALMER, Alabama ELEANOR HOLMES, Norton District of
TIM BURCHETT, Tennessee Columbia
BRIAN JACK, Georgia MAXWELL FROST, Florida
BRANDON GILL, Texas EMILY RANDALL, Washington
(II)
C O N T E N T S
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. Ses-
sions.
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. Ses-
sions.
* Questions for the Record: Ms. Renata Miskell; submitted by Rep. Mfume.
* Questions for the Record: Mr. Sterling Thomas; submitted by Rep. Ses-
sions.
* 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.
(III)
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 [Chair-
man 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 sig-
nificant problem in state-administered programs. We exposed ex-
tensive fraud in Minnesota that went largely ignored until brave
whistleblowers stepped forward. We saw that fraudsters are get-
ting 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 pre-
(1)
2
vent 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 con-
cept, 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 en-
rich themselves over the taxpayers’ dime.
The Pandemic Response Accountability Committee was created
to provide necessary oversight over pandemic problems. We are en-
tering 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 ca-
pacities 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 be-
fore a check goes out the door. They can determine when IP ad-
dresses 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 Fis-
cal Services, which maintains the Do Not Pay system. The Treas-
ury is the last barrier before payment is sent out so they can ini-
tiate that pause before putting money into the wrong hands, but
they are not always allowed to do so. Collaboration is key. How-
ever, 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 Effi-
ciency, and its operations were extended in law until 2034. A per-
manent solution that maintains the analytic capacities and capa-
bilities 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 manage-
ment, but good oversight that is provided whereby we all work to-
gether.
Treasury’s Do Not Pay system has access to a large number of
datasets, but more are needed to ensure that the system is com-
prehensive and truly innovative. As we discuss innovative tools and
collaboration, it is important that we also discuss not only how to
3
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 pre-
venting 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. Yester-
day, 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 collabo-
rating 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 gen-
tleman, 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 atten-
tion 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 administra-
tions on both sides of the aisle, and it has cost taxpayers hundreds
of billions of dollars. In fact, a 2024 Government Accountability Of-
fice analysis estimates that the Federal Government loses a stag-
gering $233 to $521 billion in fraud every year. That is just abso-
4
lutely amazing, and it is beyond comprehension. So, Chairman Ses-
sions 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 des-
perately needed programs of assistance that the Federal Govern-
ment and its programs provide.
The government’s assistance programs play, I believe, an irre-
placeable role in helping small businesses to grow, and keeping
children fed, and ensuring access to affordable healthcare, and pro-
tecting 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 dis-
cusses 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 termi-
nology.
At the beginning of the year, the so-called Department of Gov-
ernment Efficiency sought to go it alone, ignoring prior rec-
ommendations for cost savings, for fraud prevention, and for effi-
ciency 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 al-
ready had in place fraud-fighting expertise. Instead of illegally
defunding the Council for Inspector Generals on Integrity and Effi-
ciency and firing Federal workers trained in fraud prevention, the
focus went on funding these other offices and doing away with ca-
reer civil servants and inspector generals that truly, truly under-
stand 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 encour-
aged 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 be-
lief and the reality that the combination of data sharing between
siloed Federal agencies and responsibly implemented human super-
vised artificial intelligence systems can stop fraud before it hap-
pens. AI systems working with large databases and datasets can
detect patterns and connections with fraudulent actors that hu-
mans often cannot flag, certain applications that cannot be proc-
essed correctly and that are paused and reviewed by a human be-
5
fore payments happen. Of course, as we all know, and I think as
we all agree, the use of any artificial intelligence systems with pri-
vate data of Americans requires absolute caution, and fraud-fight-
ing officials must receive proper training to ensure that these mod-
els 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 sys-
tem 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 pro-
grams 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, Cali-
fornia, Colorado, Illinois, and Minnesota without any real clear rea-
soning, 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 an-
nouncements 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 par-
tisan fight. The people who are hurting as a result of fraud, waste,
and abuse come from all parts of this country. There are all back-
grounds and religions. They can help themselves and others can-
not, 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 Com-
mittee 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 stud-
ied it, who understand it, who understand the characteristics and
the relationships inside government of maybe not just the limita-
tions, 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
6
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 Sub-
committee 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 de-
lighted 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 evi-
dence 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 fis-
cal 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 wit-
nesses 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
7
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 remem-
ber 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 Account-
ability 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 capa-
bilities 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 effec-
tiveness of our fraud prevention work. Operating on an annual
budget of $18.5 million a year, to date, the PRAC has helped re-
cover 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 identi-
fied over $79 billion in potential fraud that could have been pre-
vented with pre-award vetting and cross-agency collaboration. To
address these issues, the PRAC is developing an artificial intel-
ligence-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 ap-
plications before payments are made. Had our fraud prevention en-
gine 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 Gen-
eral 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 tradi-
tional 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
8
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, applica-
tions, 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 appli-
cations 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 pros-
ecution, 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 recipi-
ents the Congress intended to help the small businesses, the unem-
ployed, 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 preven-
tion. Thank you again for your continued strong support of the
PRAC, the IG community, and independent oversight. This con-
cludes 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
9
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, dis-
burses 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 proc-
esses to flag risky payments. I will provide an overview of both ini-
tiatives, 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 pre-
vent 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, respon-
sible 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, name-
ly 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 solu-
tions. The Tiger Team identified promising new datasets and ma-
chine 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, Pro-
tecting 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 addi-
tion, 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 pay-
ments 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 con-
sistent with Do Not Pay’s rigorous access controls. These data
10
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 sci-
entist 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 en-
sure 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 esti-
mates 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 Administra-
tion’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 implemen-
tation, and rapid deployment without thoughtful design has al-
ready led to unintended outcomes. Before pouring data science on
11
the problem, we need solid, reliable, ground-truth data and a
human in the loop to ensure that data reliability and the applica-
tion 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 Protec-
tion Program, loans made before December 2020 with a rules-based
tool just looking for indicators and not using AI, and they still iden-
tified $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 cen-
ter of excellent, like the PRAC we have been talking about, to dis-
tribute tools to the community that more efficiently and effectively
identify and prevent fraud and improper payments across the gov-
ernment.
My final point is that harnessing innovation also requires a Fed-
eral 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 in-
novative big-picture ideas, like establishing a digital services acad-
emy. Rapid advances in AI capabilities hold great promise to re-
duce fraud in Federal programs. We, at GAO, believe that the Fed-
eral 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 Sub-
committee 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 nec-
essary 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
12
who are truly in need. This Committee, hopefully, is serious about
this. I know the Majority is on the Committee, and we plan on con-
tinuing to identify waste, fraud, and abuse, and hold people ac-
countable.
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 for-
ward. 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 num-
ber 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 as-
sistance? 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 Treas-
ury to provide Do Not Pay services to states that administer Fed-
eral 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 in-
vestigators to followup on?
Mr. THOMAS. So, all data science algorithms, inclusive of ma-
chine learning and AI, are going to produce indicators of fraud. It
13
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 be-
havior that do not fit the expected patterns of behavior of someone
who is using the money for the intended purpose or for the in-
tended 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 de-
velop 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 behav-
iors 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. Chair-
man, 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 dif-
ferent 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 identi-
fied. 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, mod-
ernizing 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 ana-
lytic 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 meth-
14
ods 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 in-
frastructure, 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 inti-
mately 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 inves-
tigations, 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 pro-
vide 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 Gov-
ernment.
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 be-
haviors that sort of go outside of the norm that should call the at-
tention 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 col-
leagues, 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—eligi-
bility, identity verification, their financials, their claims—and once
15
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 man-
datory requirement that we have recommended, and we rec-
ommend 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 rec-
ommendations 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 Ten-
nessee. 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 every-
where, and that poor folks cannot get what they need because we
16
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 ac-
cess 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 sci-
entists, and we can provide data more centrally so not every little
program has to create data connections to the important informa-
tion 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, be-
fore—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 abil-
ity at comprehension is very limiting. When I was the county
mayor, I found out that a lot of our different departments had dif-
ferent 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
17
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 envi-
ronment 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 Med-
icaid. Last year, through that data matching, we prevented $1.3
billion in duplicate payments. We are enhancing that with addi-
tional 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 Fed-
eral 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 vali-
dating 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, figu-
ratively, 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.
18
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 wit-
nesses for being here today to discuss substantive reforms to ad-
dress this important issue. As my colleagues noted, we, on the Gov-
ernment 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 to-
day’s testimony about the importance of catching fraud before pro-
gram 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 pro-
vide 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 unneces-
sary delays.
Ms. Miskell, what are some ways the Federal Treasury Depart-
ment 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 au-
thority, 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 determina-
tions. 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 contin-
uous monitoring can flag that risk, you know, as payments are
going out the door. And then finally, Treasury, as that central dis-
bursement 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 agen-
cies need to be able to effectively prevent fraud without unneces-
sarily delays in payments?
Mr. THOMAS. So, they need both the fraud indicators for the pro-
grams 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.
19
Mr. SESSIONS. Thank you. I appreciate the gentlewoman. The
gentlewoman yields back her time. Now I would recognize distin-
guished 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 Com-
mittee 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 some-
where 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 dol-
lars 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 num-
bers 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 for-
givable 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 legiti-
mate 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 per-
cent of the problem was administrative error, failure to verify eligi-
bility, which I think is a massive problem right now, and anti-
quated 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 to-
ward the agencies, that we say that whatever you save, we will
take part of that to bring your data systems into the 21st century
20
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 Af-
fordable 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 tril-
lion 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 distin-
guished gentleman. His avenues of working in this area for his ca-
reer 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 gen-
tleman 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 in-
telligence systems leverage massive amounts of data, and specifi-
cally 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 appre-
ciate 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, mean-
ing 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 contin-
21
uous 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 informa-
tion 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 every-
thing 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 de-
signed 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 dif-
ferent 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 be-
cause 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 in-
volved and DOGE was going on, they conducted attacks on our
Federal data systems and cybersecurity, right, and this is public in-
formation, 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’ sen-
sitive information into a master database that would give the Ad-
ministration 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 mis-
used 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 conversa-
22
tion around AI, we have successfully used machine learning to de-
tect 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 col-
laboration 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 appre-
ciate 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, pro-
gram-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 high-
light 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 impor-
tant.
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
23
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 tril-
lion 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 rep-
licated 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 real-
ly 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
24
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 ma-
chine 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 deci-
sionmakers 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 con-
versation 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 conversa-
tion 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 indi-
vidually, 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 defi-
nite, 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.
25
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 accom-
plishments that he had thus far in this session, among many oth-
ers 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 suc-
cessful 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 con-
stituents know, my constituents and the constituents of this Com-
mittee, 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, Congress-
man Jack, and for your support. The PRAC has assembled a phe-
nomenally 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 tremen-
dous 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 sys-
tem. 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 recommenda-
tions 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
26
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 num-
bers, 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 num-
ber, and again, we are not looking to get full access to these data-
bases, but just a simple yes/no to identify identity theft. There are
a number of commercial databases that we have found to be ex-
tremely 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 Treas-
ury alongside you. He is a wonderful guy. So, I appreciate your tes-
timony 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 rep-
resent typical behavior of a payee in this program, and now some-
one 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 my-
self if I could.
Unanimous consent. Agreed to.
Dr. Thomas, which agency manages the Federal Audit Clearing-
house?
Mr. THOMAS. So, the Federal Audit Clearinghouse is actually the
responsibility of the Office of Management and Budget. They dele-
gate that to the General Services Administration, and that is actu-
ally who is operating it, and when we access that, we work with
27
them to work through their APIs. Now, they have had some chal-
lenges 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 rec-
ognizing, as you have alluded to, the delay and perhaps the mis-
representation 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 re-
cently 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 im-
portant 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 rep-
resent.
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 busi-
ness. 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 unfortu-
nately, 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 respon-
sibility, the digital service academy to train. And even though there
is a great deal of competition on the private side, behavior identi-
fication, I just think that PRAC, OMB, the Treasury, GAO rep-
resents 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
28
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 wit-
nesses 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 re-
quest. 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 pre-
vent 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 con-
versation that was had today in relationship to staff, the staff of
the Subcommittee, the staff of the Committee. And I want to com-
mend our staff members on what I believe are professional prod-
ucts 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 profes-
sional 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.
29
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 peo-
ple. 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 municipali-
ties, 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 crimi-
nals 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 com-
pel, 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 ad-
dress the three of you. You have been impressive, you are sin-
gularly impressive, and your ability to know each other and work
together on a common issue is important to the country. It is al-
ways 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.
30
With that said, without objection, all Members have five legisla-
tive days which to submit materials and additional written ques-
tions for the witnesses, which will be forwarded to the witnesses.
Mr. SESSIONS. And if there is no further business, without objec-
tion, the Subcommittee stands adjourned.
[Whereupon, at 3:52 p.m., the Subcommittee was adjourned.]
Æ
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