FRAUD RISK 

MANAGEMENT 

2018-2022 Data Show 

Federal Government 

Loses an Estimated 

$233 Billion to $521 

Billion Annually to 

Fraud, Based on 

Various Risk 

Environments 

Report to Congressional Committees

April 2024

GAO-24-105833

United States Government Accountability Office
United States Government Accountability Office

Highlights of GAO-24-105833, a report to 

congressional committees

April 2024

FRAUD RISK MANAGEMENT

2018-2022 Data Show Federal Government Loses an 

Estimated $233 Billion to $521 Billion Annually to 

Fraud, Based on Various Risk Environments

What GAO Found

GAO estimated total direct annual financial losses to the government from fraud 

to be between $233 billion and $521 billion, based on data from fiscal years 2018 

through 2022. The range reflects the different risk environments during this 

period. Ninety percent of the estimated fraud losses fell in this range. 

GAO collected data from three key sources to develop the estimate: investigative 

data, such as the number of cases sent for prosecution and the dollar value of 

closed cases; Office of Inspector General (OIG) semiannual report information; 

and confirmed fraud data reported to the Office of Management and Budget 

(OMB) by agencies. GAO organized these data around three fraud categories—

adjudicated, detected potential, and undetected potential. Model design and 

validation were also informed by 46 fraud studies. OIG and other knowledgeable 

officials agreed with these categories and subcategories. 

Categories of Fraud-Related Data Used in GAO’s Estimate

GAO’s approach is sensitive to the assumptions made about fraud and accounts 

for data uncertainty and limitations. GAO used a well-established probabilistic 

method for estimating a range of outcomes under different assumptions and 

scenarios where there is uncertainty. The estimate does not include fraud loss 

associated with federal revenue or fraud against federal programs that occurs at 

the state, local, or tribal level unless federal authorities investigated and reported 

it. GAO’s estimate is in line with other estimates of fraud losses from the United 

Kingdom and Association of Certified Fraud Examiners, among others.

View GAO-24-105833. For more information, 

contact Rebecca Shea at (202) 512-6722 or 

shear@gao.gov or Jared Smith at (202) 512-

2700 or SmithJB@gao.gov.

Why GAO Did This Study

All federal programs and operations 

are at risk of fraud. Therefore,

agencies need robust processes in 

place to prevent, detect, and respond 

to fraud. While the government 

obligated almost $40 trillion from fiscal 

years 2018 through 2022, no reliable 

estimates of fraud losses affecting the 

federal government previously existed.

As part of GAO’s work on managing

fraud risks, this report (1) estimates the

range of total direct annual financial 

losses from fraud based on 2018-2022 

data and (2) identifies opportunities 

and challenges in fraud estimation to 

support fraud risk management.

GAO estimated the range of total direct 

annual financial losses from fraud 

based on 2018-2022 data using a 

Monte Carlo simulation model. GAO

identified opportunities and challenges 

through interviews and data collection 

focused on 12 agencies representing 

about 90 percent of federal obligations. 

What GAO Recommends

GAO is making two recommendations 

to OMB—one in collaboration with the 

Council of the Inspectors General on 

Integrity and Efficiency (CIGIE) and the 

other with agency input to improve the 

availability of fraud-related data. GAO

is also making a recommendation to

the Department of the Treasury to 

expand government-wide fraud 

estimation, in consultation with OMB.

OMB generally agreed with the 

recommendations but disagreed with 

the estimate. GAO believes the 

estimate is sound, as discussed in the 

report. CIGIE stated it would work with 

OMB to consider how OIGs might 

improve fraud-related data. Treasury 

agreed with the recommendation.
Highlights of GAO-24-105833 (Continued)

As a first of its kind government-wide estimate of federal dollars lost to fraud, there are known uncertainties associated with 

the model and underlying data important to interpreting the results. These include caveats related to

• applying the estimate to agencies or programs. GAO’s model was developed to estimate government-wide federal 

fraud. The fraud estimate’s range represents 3 to 7 percent of average federal obligations. These percentages should 

not be applied at the agency or program level. While every federal program and operation is at risk of fraud, the level of 

risk can vary substantially. Controls, growth or shrinkage of budget, and the emergence of new fraud schemes are 

some reasons the risk level can vary;

• drawing conclusions about pandemic fraud. GAO’s estimate is based on data from fiscal years 2018 through 2022. 

The data include time periods and programs with and without pandemic-related spending. Therefore, the estimate 

includes, but is not limited to, pandemic-related spending fraud. While the upper range of the estimate is associated 

with higher-risk environments, it is not possible to break out a subset of our government-wide estimate to describe 

pandemic program fraud;

• comparing with improper payment estimates. GAO’s estimate is not comparable to improper payment estimates. 

Improper payment estimates are based on a subset of federal programs, using a methodology not designed to identify

fraud. GAO has also consistently reported that the federal government does not know the full extent of improper 

payments and has long recommended that agencies improve their improper payment reporting. In contrast, GAO’s 

fraud estimate includes all federal programs and operations and is based on fraud-related data. With these differences 

in scope and data, the upper end of GAO’s estimated fraud range exceeded annual improper payment estimates; and

• assuming the estimate is predictive. GAO’s estimate is not based on a predictive model. Factors such as the 

amount of emergency spending, the effectiveness of federal fraud risk management, and the nature of new fraud 

threats could substantially impact the scale of future fraud.

GAO has previously issued Matters for Congressional Consideration and recommendations to improve agencies’ program 

integrity, including fraud risk management. Fraud estimation provides opportunities to improve fraud risk management, 

according to OIG and agency officials. For example, estimates can demonstrate the scope of the problem, improve 

oversight prioritization, and help determine the return on investment from fraud risk management activities. While it is not 

possible to eliminate fraud, with a better understanding of the costs, agencies will be better positioned to manage the risk.

How Fraud Estimates Can Improve Fraud Risk Management

OIG and agency officials noted challenges in producing fraud estimates, such as limited available fraud-related data and 

use of varying terms and definitions of fraud for recording data. These data gaps and variability result in information that 

cannot be readily compared or consolidated to determine the extent of fraud across the federal government. Guidance for 

collecting and reporting fraud-related data is currently limited to OIG semiannual reports and confirmed fraud reported by 

agencies to OMB, which are not designed to support fraud estimation. With guidance targeted to the purpose of fraud 

estimation, agencies and OIGs would be better positioned to collect and report data on potential and adjudicated fraud in 

support of estimation efforts. 

OIG and agency officials also noted the utility of agency or program-level estimates compared with government-wide

estimates. They further noted the need for expertise and data-analytics capacity to produce estimates. GAO previously 

reported that agencies identified limitations in expertise, data, and tools as a significant challenge for their fraud risk 

management efforts. These challenges could also impact agencies’ ability to develop effective fraud estimates at a 

program or agency level. The Department of the Treasury’s Office of Payment Integrity (OPI) supports agencies facing 

such challenges. OPI’s resources are dedicated to preventing and detecting improper payments through a variety of datamatching and data-analytics services. Therefore, OPI is well positioned—with the expertise, data, and analytic tools—to 

evaluate and advance methods that the federal government can take to estimate fraud in support of fraud risk 

management.
Page i GAO-24-105833 Federal Fraud Estimate

Letter 1

Background 5

Annual Federal Losses Due to Fraud Are Estimated to Be 

between $233 Billion and $521 Billion Based on Data from 

Fiscal Years 2018 through 2022, Reflecting Various Risk 

Environments 18

Opportunities and Challenges in Estimating Fraud to Support 

Fraud Risk Management 25

Conclusions 38

Recommendations for Executive Action 38

Agency Comments and Our Evaluation 39

Appendix I Objectives, Scope, and Methodology 48

Appendix II Matters for Congressional Consideration 68

Appendix III Comments from the Office of Management and Budget 70

Appendix IV GAO Contacts and Staff Acknowledgments 73

Figures

Figure 1: Illustrative Examples of Adjudicated Fraud Perpetrated 

Against the Federal Government 7

Figure 2: Confirmed Fraud, as Reported by the Office of 

Management and Budget for Fiscal Years 2018 through 

2022 9

Figure 3: Examples of Analytic Studies of Potential Fraud in Select 

Federal Programs 11

Figure 4: Select International or Nongovernmental Fraud 

Estimation Studies 13

Figure 5: Components of the Fraud Risk Framework 14

Figure 6: Estimate of Direct Annual Financial Losses from Fraud 

Affecting the Federal Government, Based on Our 

Simulation 18

Contents
Page ii GAO-24-105833 Federal Fraud Estimate

Figure 7: Fraud Categories and the Data Collected and Used in 

the Simulation, by Category 24

Figure 8: How Fraud Estimates Can Improve Fraud Risk 

Management 26

Figure 9: Examples of Variation in How Fraud-Related Data Are 

Reported in Office of Inspectors General Semiannual 

Reports to Congress 32

Figure 10: Fraud Categories: Adjudicated, Detected Potential, and 

Undetected Potential 51

Figure 11: Information and Data Sources for the Simulation, by 

Fraud Category 53
Page iii GAO-24-105833 Federal Fraud Estimate

Abbreviations

ACFE Association of Certified Fraud Examiners

CFO Chief Financial Officer

CIGIE Council of the Inspectors General on

 Integrity and Efficiency

DOD Department of Defense

Federal internal control Standards for Internal Control in the Federal

 standards Government

Fraud Risk Framework A Framework for Managing Fraud Risks in

 Federal Programs 

FRDAA Fraud Reduction and Data Analytics Act of 

2015 

HHS Department of Health and Human Services

MAX Office of Management and Budget’s MAX 

A-11 Data Entry Information System 

OIG Office of Inspector General

OMB Office of Management and Budget

PIIA Payment Integrity Information Act of 2019

This is a work of the U.S. government and is not subject to copyright protection in the 

United States. The published product may be reproduced and distributed in its entirety 

without further permission from GAO. However, because this work may contain 

copyrighted images or other material, permission from the copyright holder may be 

necessary if you wish to reproduce this material separately.
Page 1 GAO-24-105833 Federal Fraud Estimate

441 G St. N.W.

Washington, DC 20548

April 16, 2024

Congressional Committees

All federal programs and operations are at risk of fraud. Some individuals 

or groups will seek to gain through fraud when and wherever there is 

opportunity. As a result, agencies need robust processes in place to 

prevent, detect, and respond to fraud.1 Prior GAO work has found 

evidence of substantial losses due to fraud in some government 

programs. While the federal government obligated almost $40 trillion from 

fiscal years 2018 through 2022, no reliable estimate has existed on the

amount of federal dollars lost to fraud.2

One of the many challenges in determining the full extent of fraud is its 

deceptive nature. Programs can incur losses related to fraud that are 

never identified, and such losses are difficult to reliably estimate. 

However, without usable information on the scope of fraud, actions to 

address it might not be directed at the highest-risk areas or designed to 

maximize their effectiveness. Reliably determining the extent of fraud in 

federal programs could help Congress, oversight entities, and agency 

officials better prioritize prevention, detection, and response resources. 

By identifying and addressing fraud, such as through targeted actions 

based on estimations of risk, potential fraudsters may be deterred.

1Fraud involves obtaining a thing of value through willful misrepresentation. Willful 

misrepresentation can be characterized by making material false statements of fact based 

on actual knowledge, deliberate ignorance, or reckless disregard of falsity. Program 

officials are responsible for managing the risk of fraud through activities to prevent, detect, 

and respond to potential fraud. 

2Obligations as reported on Office of Management and Budget’s MAX A-11 Data Entry 

System (MAX), a government-wide system used to share information and services among 

government agencies and to collect and process most of the information required for 

preparing the President’s Budget of the federal government. An obligation is a definite 

commitment that creates a legal liability on the part of the federal government for the 

payment of goods and services ordered or received, or a legal duty on the part of the 

United States that could mature into a legal liability by virtue of actions on the part of the 

other party beyond the control of the United States. Payment may be made immediately or 

in the future. An agency incurs an obligation, for example, when it places an order, signs a 

contract, awards a grant, purchases a service, or takes other actions that require the 

government to make payments to the public or from one government account to another. 

GAO, A Glossary of Terms Used in the Federal Budget Process, GAO-05-734SP

(Washington, D.C.: Sept. 1, 2005).

Letter
Page 2 GAO-24-105833 Federal Fraud Estimate

We have previously reported that the federal government faces an 

unsustainable long-term fiscal future.3 Our projections, as well as those 

from the Office of Management and Budget (OMB), the Department of the 

Treasury, and the Congressional Budget Office, all show that current 

fiscal policy is unsustainable over the long term. Improved efforts to 

combat fraud, with an emphasis on prevention, can reduce the loss of 

federal dollars and help improve the federal government’s fiscal outlook.

We performed our work under the Comptroller General’s authority to 

conduct evaluations to address issues of broad interest to Congress, 

such as the extent of fraud affecting the federal government. Our 

objectives were to (1) estimate the range of total direct annual financial 

losses from fraud affecting federal programs and operations and (2) 

identify opportunities and challenges in fraud estimation to support fraud 

risk management.

For both objectives, we interviewed officials from 12 selected agencies 

and their respective Offices of Inspector General (OIG).4 These 12 

agencies were selected based on obligation levels for fiscal years 2018 

through 2022 and include those with the top 10 obligations for one of the 

fiscal years. Combined, they represent approximately 90 percent of all 

government obligations during this time.

We also identified and reviewed 46 fraud measurement and estimation 

studies developed by U.S. government, and international, academic, and 

others with subject-matter expertise.5 We generally focused on the 

studies published between fiscal years 2013 and 2022. We used these 

studies to assess estimation and measurement methodologies, the 

amount of fraud estimated or measured, and challenges in estimating and 

measuring fraud. As appropriate, we also used the studies to assess the 

reasonableness of our fraud estimate. Our estimate was in line with fraud 

3GAO, The Nation’s Fiscal Health: Road Map Needed to Address Projected 

Unsustainable Debt Levels, GAO-24-106987 (Washington, DC: Feb. 15, 2024). 

4The 12 selected agencies and OIGs are the Department of Defense, Department of 

Education, Department of Homeland Security, Department of Labor, Department of the 

Treasury, Department of Health and Human Services, Office of Personnel Management, 

Social Security Administration, Department of Agriculture, Department of Veterans Affairs, 

Department of Transportation, and the Small Business Administration. 

5For the purposes of this report, we define fraud “measure” as a count of detected fraud or 

fraud-related activities. We define fraud “estimate” as a projection or inference based on 

fraud or fraud-related measures, assumptions, or analytical techniques, where direct 

measures are incomplete or unreliable. 
Page 3 GAO-24-105833 Federal Fraud Estimate

estimates and analysis developed by other governments, as well as 

relevant nongovernmental organizations with fraud expertise.

We also met with subject-matter experts from the Association of Certified 

Fraud Examiners (ACFE); the Centre for Cybercrime and Economic 

Crime at the School of Criminology and Criminal Justice at the University 

of Portsmouth in the United Kingdom; and the creator of the Crime, and 

Compliance blog, among others.6 We selected these experts based on 

our review of fraud measurement and estimation studies and through 

interviews.

To estimate the range of total direct annual financial losses from fraud, 

we assessed different methods that could be used to estimate fraud. 

Given available data and our cross-government scope, we selected a 

Monte Carlo simulation to develop our estimate. A Monte Carlo simulation 

is a method that can be used to estimate ranges for events where there is 

a high degree of uncertainty or for which there are limited data.7 We 

chose this approach based on our review of fraud studies, available 

agency fraud-related reporting data and information, interviews with 

agency and OIG officials and fraud experts, and knowledge gained 

through our past work on fraud.

To inform the simulation, we identified and collected relevant fraudrelated data and information from fiscal years 2018 through 2022 for the 

12 selected agencies. These sources include

• OIG investigative data relating to past and ongoing investigations,

including adjudicated cases;

• OIG semiannual reports;8 and

6During our review, the Centre for Cybercrime and Economic Crime was founded and 

integrated the Centre for Counter Fraud Studies.

7Determining the extent of fraud is challenging due to multiple factors. We have previously 

reported on several challenges in measuring fraud, which are discussed later in this 

report. 

8Under the Inspector General Act of 1978, as amended, federal inspectors general are 

required to submit semiannual reports to Congress describing the offices’ activities and 

accomplishments during that reporting period. 5 U.S.C. App. § 5.
Page 4 GAO-24-105833 Federal Fraud Estimate

• confirmed fraud reported by agencies to the OMB’s 

Paymentaccuracy.gov dashboard.9

To collect OIG investigative data, we developed a data collection 

instrument, which enabled us to consistently request and gather detailed, 

fraud-related data and information from the 12 selected OIGs. We 

pretested this data collection instrument with three OIGs prior to collecting 

these data from all 12 selected OIGs.

We also collected and analyzed information reported in semiannual 

reports for the 12 selected agencies for fiscal years 2018 through 2022. 

For example, we collected information on cases referred for prosecution, 

investigative reports issued, and hotline reporting statistics.

We spoke with OIG officials knowledgeable about their investigative data 

and semiannual reports and reviewed any relevant documentation they 

provided to identify limitations with the data.

We collected and analyzed confirmed fraud data and asked 

knowledgeable agency and OIG officials about the data. We assessed 

the reliability of all data used in our analysis and determined they were 

sufficiently reliable for our purposes.

Using these data, we developed an estimate of fraud affecting the federal 

government. Based on the data available and known uncertainties in 

estimating fraud, we developed our estimate as a range.

We assessed the reasonableness of our estimate by comparing it with 

applicable fraud estimates identified in our review of 46 fraud 

measurement and estimation studies. While these studies utilized 

different methodologies or estimated fraud in a variety of environments, 

our estimate was in line with those estimates.

To identify ways that enhanced fraud estimation could potentially improve 

fraud risk management, we evaluated these data and information using 

relevant leading practices in GAO’s A Framework for Managing Fraud 

Risks in Federal Programs (Fraud Risk Framework), specifically, leading 

9OMB requires agencies to provide certain information about improper payments and 

confirmed fraud. OMB publishes this information in a dashboard on 

Paymentaccuracy.gov.
Page 5 GAO-24-105833 Federal Fraud Estimate

practices related to assessing fraud.10 We also evaluated existing fraudrelated data and information collection against the principles in Standards 

for Internal Control in the Federal Government (federal internal control 

standards).11 Specifically, we determined that the information and 

communication component of internal control was significant to the 

objective, along with the underlying principles that management should 

use quality information to achieve the entity’s objectives.

For additional information on our methodology, including a detailed 

discussion of the steps taken to develop our fraud estimate and 

associated caveats, see appendix I.

We conducted this performance audit from February 2022 to April 2024 in 

accordance with generally accepted government auditing standards. 

Those standards require that we plan and perform the audit to obtain 

sufficient, appropriate evidence to provide a reasonable basis for our 

findings based on our audit objectives. We believe that the evidence 

obtained provides a reasonable basis for our findings based on our audit 

objectives.

Our work has shown that fraud can result in financial and nonfinancial 

losses to federal programs and operations. In 2022, we issued the 

Antifraud Resource, a web-based tool that provides users with tools to 

help learn more about fraud schemes that affect the federal government, 

their underlying concepts, and how to combat such fraud.12 The Antifraud 

Resource provides a detailed discussion of the characteristics of fraud 

that affect the federal government and notes that fraud can impact many 

different federal programs and operations.

The Antifraud Resource also provides numerous case examples of fraud 

that impacts federal programs and operations. These examples include 

cases that resulted in a financial loss to the federal government, as well 

10GAO, A Framework for Managing Fraud Risks in Federal Programs, GAO-15-593SP

(Washington, D.C.: July 28, 2015).

11GAO, Standards for Internal Control in the Federal Government, GAO-14-704G

(Washington, D.C.: Sept. 10, 2014).

12GAO, “The GAO Antifraud Resource” (Washington, D.C.: Jan. 10, 2022), 

https://gaoinnovations.gov/antifraud_resource/.

Background

Fraud Against the Federal 

Government
Page 6 GAO-24-105833 Federal Fraud Estimate

as those that resulted in nonfinancial losses. Nonfinancial losses can 

include things such as negative impacts to the affected program’s ability 

to provide beneficiary services, or reputational loss. For illustrative 

examples of federal fraud cases, see figure 1. As shown in the figure, it 

can take months, and even years, from the date fraud-related charges are 

filed against a suspected fraudster until the case is adjudicated.
Page 7 GAO-24-105833 Federal Fraud Estimate

Figure 1: Illustrative Examples of Adjudicated Fraud Perpetrated Against the Federal Government 

Note: Restitution is ordered to reimburse victims for financial losses, making it indicative of at least a 

portion of the federal financial loss. Restitution does not, however, include or reflect agency resources 

spent investigating and prosecuting fraud. 
Page 8 GAO-24-105833 Federal Fraud Estimate

Various federal entities report data that provide insights into the extent of 

federal fraud. For example:

• The Council of the Inspectors General on Integrity and Efficiency 

(CIGIE): annually reports its accomplishments to the President and 

Congress.13 For fiscal years 2018 through 2022, CIGIE reported 

between $6.6 billion and $19.7 billion in potential savings from 

investigative recoveries and receivables. This amount includes 

ordered restitution, fines, and settlements from resolved criminal and 

civil cases. However, this amount also includes potential crimes 

beyond fraud, such as theft and the mismanagement of government 

funds. This amount also excludes undetected fraud, as well as 

potential fraud detected by the agency that has not resulted in 

investigative action.

• The Office of Management and Budget: annually reports federal 

government confirmed fraud data on its website, 

Paymentaccuracy.gov. According to OMB, confirmed fraud is the 

amount determined to be fraudulent through the judicial or 

adjudication process.14 It represents only those fraud cases that have 

been confirmed by a court or other adjudicative forum and does not 

represent anything settled out of court with or without admission of 

guilt.15 For fiscal years 2018 through 2022, OMB reported between 

$4.41 billion and $7.31 billion annually in confirmed fraud, as shown in 

figure 2.

13CIGIE was established as an independent entity within the executive branch by The 

Inspector General Reform Act of 2008, Pub. L. No. 110-409. Among other things, CIGIE is 

to increase the professionalism and effectiveness of personnel by developing policies, 

standards, and approaches to aid in the establishment of a well-trained and highly skilled 

workforce in the Offices of the Inspectors General.

14Per OMB guidance, confirmed fraud does not include transactions determined by 

management to be anomalous or indicative of potential fraud that were referred to the 

agency’s OIG or the Department of Justice, unless the appropriate judicial or adjudicative 

process has made the determination. 

15Paymentaccuracy.gov reporting on confirmed fraud states that it only includes fraud 

confirmed by a court. 

Existing Information on the 

Extent of Fraud and 

Potential Fraud in the 

Federal Government
Page 9 GAO-24-105833 Federal Fraud Estimate

Figure 2: Confirmed Fraud, as Reported by the Office of Management and Budget 

for Fiscal Years 2018 through 2022

According to OMB’s definition used on PaymentAccuracy.gov, confirmed 

fraud does not include those cases resolved through settlement or an 

administrative process. Therefore, for example, it would not reflect a 

contractor that was debarred from obtaining government contracts for a 

certain period based on determinations made by an agency 

administrative body. Further, it does not include settlements, with or 

without an admission of wrongdoing, that may be significant. In addition, it 

does not count amounts recouped under the False Claims Act. According 

to the Department of Justice, in fiscal year 2022 the government was 

party to 351 False Claims Act settlements and judgments, in an amount 

that exceeded $2 billion.16

In addition to these issues, limitations with the reported confirmed fraud 

numbers have also been identified. For example, the Department of 

1631 U.S.C. §§ 3729 – 3733. The False Claims Act provides that any person who 

knowingly submits, or causes to submit, false claims to the government is liable for three 

times the government’s damages, plus a penalty. 
Page 10 GAO-24-105833 Federal Fraud Estimate

Defense (DOD) OIG reported at least $1.1 billion in confirmed fraud to 

CIGIE in fiscal year 2021, but DOD reported $0 in confirmed fraud to 

OMB for the same period.17 Because of these limitations on the 

completeness and quality of OMB’s confirmed fraud data, it is not 

sufficient information for fraud estimation.

• The Department of Justice: issues press releases for some federal 

fraud cases and may include information such as the charges and 

outcome of a trial. For fiscal years 2018 through 2022, the 

Department of Justice annually issued around 1,300 press releases. 

However, these press releases include cases unrelated to federal 

fraud, and the Department of Justice does not issue a press release 

for every case.

Some agencies and OIGs have also issued studies that examine the 

extent of identified potential fraud in a limited number of programs, 

including studies in response to concerns about fraud impacting 

pandemic spending (see fig. 3).

17Department of Defense, Office of Inspector General, Audit of the Department of 

Defense’s FY 2021 Compliance With Payment Integrity Information Act Requirements, 

DODIG-2022-108 (Alexandria, VA: June 28, 2022). In the report, DOD personnel noted 

that the difference was a result of a change in reporting requirements and certain data 

limitations that had since been resolved.
Page 11 GAO-24-105833 Federal Fraud Estimate

Figure 3: Examples of Analytic Studies of Potential Fraud in Select Federal Programs

Note: Reports cited: Department of Labor, Office of Inspector General, Alert Memorandum: 

Potentially Fraudulent Unemployment Insurance Payments in High-Risk Areas Increased to $45.6 

Billion, Report No. 19-22-005-03-315 (Washington, D.C.: Sept. 21, 2022); Small Business 

Administration, Protecting the Integrity of the Pandemic Relief Programs: SBA’s Actions to Prevent, 

Detect and Tackle Fraud (Washington, D.C.: June 2023); Small Business Administration, Office of 

Inspector General, COVID-19 Pandemic EIDL and PPP Loan Fraud Landscape, White Paper Report 

23-09 (Washington, D.C.: June 27, 2023); and U.S. Department of Agriculture, The Extent of 

Trafficking in the Supplemental Nutrition Assistance Program: 2015-2017 (Alexandria, VA.: 

September 2021). 
Page 12 GAO-24-105833 Federal Fraud Estimate

The studies noted in figure 3 quantify the extent of fraud based on 

instances of identified potential fraud. However, some portion of fraud is 

never detected. See sidebar for example of now detected, potential fraud 

that is alleged to have taken place, and gone undetected for about two 

years, per news media. Fraud estimates can provide information on the 

extent of undetected fraud. For example, we developed an estimate of 

unemployment fraud in response to congressional interest in the extent of 

pandemic-related spending fraud.18 Specifically, we estimated that 

between $100 billion and $135 billion (between 11 and 15 percent of total 

spending) in fraudulent unemployment insurance payments were made 

between April 2020 and May 2023. This included an estimate of

undetected fraud.

In addition to fraud estimation or analysis efforts performed by the federal 

government, non-U.S. governments and relevant nongovernmental 

organizations with fraud expertise have also developed estimates (see 

fig. 4).

18GAO, Unemployment Insurance: Estimated Amount of Fraud during Pandemic Likely 

Between $100 Billion and $135 Billion, GAO-23-106696 (Washington, D.C.: Sept. 12, 

2023). 

Undetected Fraud May Be Significant 

In March 2024, the Department of Health and 

Human Services Office of Inspector General 

(HHS OIG) issued a consumer alert related to 

scams that involve obtaining a Medicare 

enrollee’s personal information and then billing 

for unnecessary, low-cost medical equipment. 

Related media reported that an alleged fraud 

ring may have used the scheme to overbill 

Medicare for more than $2 billion since 2022. It 

is alleged the fraud ring employed multiple 

small charges to many victims to help avoid 

detection. 

Source: HHS OIG Consumer Alert: Urinary Catheter Scams. │ 

GAO-24-105833 
Page 13 GAO-24-105833 Federal Fraud Estimate

Figure 4: Select International or Nongovernmental Fraud Estimation Studies 

Note: Reports cited: United Kingdom Public Sector Fraud Authority, Cross-Government Fraud 

Landscape, Annual Report 2022; Occupational Fraud 2022: A Report to the Nations (Association of 

Certified Fraud Examiners, Inc.:2022). ACFE’s study also explored characteristics of occupational 

fraud, such as the methods used and the characteristics of the perpetrators and impacted 

organizations. Jim Gee and Mark Button, The Financial Cost of Fraud (University of Portsmouth and 

Crowe, United Kingdom: 2021). 

The Fraud Risk Framework provides a comprehensive set of key 

components and leading practices that serve as a guide for agency 

managers to use when developing efforts to combat fraud in a strategic, 

Fraud Risk Management 

and Standards for Internal 

Control
Page 14 GAO-24-105833 Federal Fraud Estimate

risk-based manner.19 The objective of fraud risk management is to ensure 

program integrity by continuously and strategically mitigating the 

likelihood and impact of fraud.

As discussed in the Fraud Risk Framework, strategic fraud risk 

management involves more than having controls to prevent, detect, and 

respond to fraud. Rather, it also encompasses structures and 

environmental factors that influence or help managers achieve their 

objective to mitigate fraud risks. The Fraud Risk Framework describes 

leading practices in four components: commit, assess, design and 

implement, and evaluate and adapt, as depicted in figure 5.

Figure 5: Components of the Fraud Risk Framework

19GAO-15-593SP. In June 2016, Congress enacted the Fraud Reduction and Data 

Analytics Act of 2015. This act required OMB to establish guidelines for federal agencies 

to create controls to identify and assess fraud risks and to design and implement antifraud 

control activities. The act further required OMB to incorporate the leading practices from 

GAO’s Fraud Risk Framework in these guidelines. In its 2016 Circular No. A-123 

guidelines, OMB directed agencies to adhere to the Fraud Risk Framework’s leading 

practices as part of their efforts to effectively design, implement, and operate an internal 

control system that addresses fraud risks. The act was repealed and replaced in March 

2020 by the Payment Integrity Information Act of 2019, which required these guidelines to 

remain in effect. In its 2021 update to Appendix C to Circular No. A-123 guidelines, OMB 

provided guidance to agencies to improve their controls for identifying, assessing, 

mitigating, and monitoring payment integrity risks, including fraud.
Page 15 GAO-24-105833 Federal Fraud Estimate

The Fraud Risk Framework includes several leading practices that 

highlight the importance of understanding the scope of fraud affecting a 

program to manage fraud risk. It also includes leading practices 

associated with data analytics, such as

• assessing the likelihood and impact of inherent fraud risk, which may 

include involving qualified specialists, such as statisticians and 

subject-matter experts, to contribute expertise and guidance when 

employing techniques like analyzing statistically valid samples to 

estimate fraud losses and frequency;

• identifying specific tools, methods, and sources for gathering 

information about fraud risks, including data on fraud schemes and 

trends from monitoring and detection activities;

• considering known or previously encountered fraud schemes to 

design data analytics; and

• collecting and analyzing data, including data from reporting 

mechanisms and instances of detected fraud, for real-time monitoring 

of fraud trends and identification of potential control deficiencies.

The Fraud Risk Framework also includes leading practices related to 

assessing risk and using this information to inform a response, such 

as by

• considering the financial and nonfinancial impacts of fraud risks, and

• using the programs fraud risk profile (see sidebar) to help decide how 

to allocate resources to respond to residual fraud risks. 
Page 16 GAO-24-105833 Federal Fraud Estimate

Federal internal control standards provide managers with criteria for 

designing, implementing, and operating an effective internal control 

system, which is key to preventing and reducing fraud.20 Among other 

things, the standards state that program managers are to use quality 

information to achieve their objectives. Further, program managers are to 

identify and obtain relevant and reliable data and process the data into 

quality information. The standards also state that management should 

consider the potential for fraud when identifying, analyzing, and 

responding to risks.

OMB oversees implementation of programs and operations across the 

executive branch. Among other things, OMB issues guidance to agencies 

on their responsibilities. OMB Circular A-123 defines management’s 

responsibility for internal control in federal agencies.21 Among other 

actions, the circular directs agencies to follow the leading practices 

outlined in the Fraud Risk Framework. Moreover, in October 2022, OMB 

issued a Controller Alert reminding agencies that they must establish 

financial and administrative controls to identify and assess fraud risks.22

In our March 2022 testimony before the Senate Committee on Homeland 

Security and Governmental Affairs, we identified 10 actions that Congress 

could take to strengthen internal controls and financial and fraud risk 

management practices across the government.23 For example, we 

suggested Congress (1) establish a permanent analytics center of 

excellence to aid the oversight community in identifying improper 

payments and fraud; (2) amend the Social Security Act to make 

permanent the sharing of full death data with the Department of the 

20GAO-14-704G. 

21Office of Management and Budget, OMB Circular No. A-123, Management’s 

Responsibility for Enterprise Risk Management and Internal Control, M-16-17 

(Washington, D.C.: July 15, 2016). 

22Office of Management and Budget, Establishing Financial and Administrative Controls to 

Identify and Assess Fraud Risk, CA-23-03 (Washington, D.C.: Oct. 17, 2022). Enterprise 

risk management is a decision-making tool that can assist federal leaders to anticipate 

and manage risks across their portfolios. Prior to implementing enterprise risk 

management, risk management focused on traditional internal control concepts for 

managing risk exposures. Beyond traditional internal controls, enterprise risk management 

promotes risk management by considering its effect across the entire organization and 

how it may interact with other identified risks.

23GAO, Emergency Relief Funds: Significant Improvements Are Needed to Ensure 

Transparency and Accountability for COVID-19 and Beyond, GAO-22-105715

(Washington, D.C.: Mar. 17, 2022).

Fraud Risk Profile 

The fraud risk profile forms the basis of a 

program’s antifraud strategy and informs the 

specific control activities to be designed and 

implemented. 

A Framework for Managing Fraud Risks in 

Federal Programs discusses information that 

might be in a fraud risk profile such as 

• the identified fraud risk, 

• fraud risk factors, 

• inherent risk likelihood and impact, 

• inherent risk significance, and 

• the fraud risk response. 

Source: GAO analysis of GAO, A Framework 

for Managing Fraud Risks in Federal 

Programs, GAO-15-593SP (Washington, 

D.C.: July 28, 2015). │ GAO-24-105833 
Page 17 GAO-24-105833 Federal Fraud Estimate

Treasury’s Do Not Pay working system; and (3) reinstate the requirement 

that agencies report on their antifraud controls and fraud risk 

management efforts in their annual financial reports, among other actions. 

As of March 2024, these Matters for Congressional Consideration remain 

open. We continue to believe that such actions will increase 

accountability and transparency in federal spending in both normal 

operations and emergencies. See appendix II for a summary of the 

Matters for Congressional Consideration.

The federal government has established capacity to provide dataanalytics resources to combat fraud. For example, the Department of the 

Treasury’s Office of Payment Integrity includes resources and expertise 

for preventing and detecting improper payments, including those due to 

fraud. The Office of Payment Integrity’s Do Not Pay Business Center 

operates a resource dedicated to preventing and detecting improper 

payments through a variety of data-matching and data-analytics services 

to support agency programs.24 The Office of Payment Integrity’s Payment 

Integrity Center of Excellence is a community of experts with a mission to 

provide government-wide partnership, guidance, and customer-centric 

solutions that aid in the prevention and recovery of improper payments 

due to fraud, waste, and abuse. The Payment Integrity Center of 

Excellence uses capacities such as data and analytics to help address 

agency payment integrity challenges.

Additionally, in March 2020, Congress enacted the CARES Act, which 

created the Pandemic Response Accountability Committee within CIGIE. 

The mission of the Pandemic Response Accountability Committee is to 

promote transparency and conduct and support oversight of covered 

funds and the coronavirus response to (1) prevent and detect fraud, 

waste, abuse, and mismanagement; and (2) mitigate major risks that cut 

across program and agency boundaries. In March 2021, the American 

Rescue Plan Act of 2021 appropriated $40 million to the Pandemic 

Response Accountability Committee, which subsequently established the 

Pandemic Analytics Center of Excellence. The role of the Pandemic 

Analytics Center of Excellence is to help oversee the trillions of dollars in 

federal pandemic-related emergency spending.

24According to its website, the Do Not Pay Business Center uses a variety of data 

sources, such as those to verify individual personal records, as well as data sources to 

determine if companies have been debarred or received other sanctions against them. 

Data Analytics to Aid 

Program Integrity
Page 18 GAO-24-105833 Federal Fraud Estimate

We estimated direct annual financial losses to the federal government 

from fraud to be between approximately $233 billion and $521 billion, as 

shown in figure 6. This range reflects the middle 90 percent of values, 

based on our model. The width of the range is a reflection of both the 

uncertainty associated with estimating fraud and the diversity in the risk 

environments that were present in fiscal years 2018 through 2022.

The estimate reflects fraud losses associated with direct federal spending 

on programs and operations. Accordingly, fraud loss associated with 

revenues, such as tax credits or other fees collected by the federal 

government, are not included. This estimate does not capture losses that 

occur at the state, local, tribal, or other government level unless those 

losses included a federal investigative, administrative, or related action. 

Further, the estimate does not include the nonfinancial losses due to 

fraud or the value of nonfinancial benefits obtained fraudulently.25

Figure 6: Estimate of Direct Annual Financial Losses from Fraud Affecting the 

Federal Government, Based on Our Simulation 

The estimated losses represent about 3 to 7 percent of average federal 

obligations for fiscal years 2018 through 2022. These percentages are 

generally in line with fraud estimates and analysis developed by other 

governments, as well as relevant nongovernmental organizations with 

fraud expertise such as the ACFE and academia. For example, studies 

from the United Kingdom’s Public Sector Fraud Authority, ACFE, and 

Portsmouth Centre cite fraud losses of 0.5 to 5, 5, and 6.4 percent, 

respectively. Although these estimates vary in their methodology, risk 

25Nonfinancial losses due to fraud may not pose a direct financial cost but they lead to 

other potentially harmful outcomes. For example, fraud can impact government outcomes 

or program reputation. Further, government activities such as passport and Social 

Security number issuance, or small business certification may result in nonfinancial fraud 

but not a direct financial loss to the government. 

Annual Federal 

Losses Due to Fraud 

Are Estimated to Be 

between $233 Billion 

and $521 Billion 

Based on Data from 

Fiscal Years 2018 

through 2022, 

Reflecting Various 

Risk Environments
Page 19 GAO-24-105833 Federal Fraud Estimate

environment, and entities affected, the results are in line with our 

estimate.26

Our estimate is also in line with studies of domestic federal program 

fraud. For example, we and others conducted estimation work related to 

pandemic spending, which was at higher risk of fraud. We estimated that 

between $100 billion and $135 billion (between 11 and 15 percent of total 

spending) in fraudulent unemployment insurance payments were made 

between April 2020 and May 2023.27 This analysis supported even higher 

fraud rates for the Pandemic Unemployment Assistance payments, which 

made up a subset of the unemployment insurance payments that were 

included in our review. The Small Business Administration OIG reported 

that it estimated $200 billion in potentially fraudulent pandemic related 

business loans as of May 2023.28 

Our estimate of direct annual financial losses due to fraud reflects 

significant financial impacts to the federal government. For comparative 

context, the lower range of the estimate—$233 billion—is greater than 

fiscal year 2022 obligation levels for all but the eight largest agencies. 

There are five agencies with total annual obligations greater than the 

upper range of $521 billion, based on fiscal year 2022.

This estimate is based on a Monte Carlo simulation using data from fiscal 

years 2018 through 2022 (see sidebar). For the Monte Carlo simulation 

that generated our estimate, we used investigative, OIG semiannual, and 

confirmed fraud data from 12 selected agencies and other fraud estimate 

and measurement information from fiscal years 2018 through 2022. The 

resulting range represents our best estimate of the extent of fraud, given 

the data available, which have limitations. While we designed our 

simulation approach and underlying assumptions to account for the 

inherent uncertainties associated with fraud estimation and data 

limitations, we cannot eliminate the possibility that the actual amount of 

fraud could be outside of the range of our estimate. See appendix I for 

26See figure 4 for more information on these studies. 

27GAO-23-106696. 

28Small Business Administration, Office of Inspector General, COVID-19 Pandemic EIDL 

and PPP Loan Fraud Landscape, White Paper Report 23-09 (Washington, D.C.: June 27, 

2023). Using a different approach, the Small Business Administration estimated $36 billion 

of pandemic relief emergency program funds that were likely obtained fraudulently from 

2020 to 2022. Small Business Administration, Protecting the Integrity of the Pandemic 

Relief Programs: SBA’s Actions to Prevent, Detect and Tackle Fraud (Washington, D.C.: 

June 2023). 

Monte Carlo Simulation

A Monte Carlo simulation attempts to capture 

the uncertainty in a process by randomly 

generating a range of values consistent with 

that process. Monte Carlo simulations can be 

useful for understanding the range of potential 

outcomes that can exist under different 

assumptions and scenarios. They are useful 

where there is a high degree of uncertainty, or 

for where there are limited data. 

Source: GAO analysis. │ GAO-24-105833
Page 20 GAO-24-105833 Federal Fraud Estimate

additional details on our simulation, data sources, assumptions, and 

limitations.

Our results reflect a first-of-its kind, government-wide estimate of federal 

dollars lost to fraud. Our estimate includes known uncertainties 

associated with the model and underlying data that are important to 

understand in interpreting and applying the results. Improvements to the 

availability of fraud-related data could improve future estimation efforts. 

Current caveats to our estimate include those related to extrapolating a 

government-wide estimate to (a) programs or agencies, (b) pandemic 

spending, (c) improper payment estimates, and (d) future years.

Applying the estimate to agencies or programs. Our model was 

developed to estimate government-wide federal fraud losses and the 

model’s dollar range and percent should not be applied to the agency, 

program, or operation level. While every federal program and 

operation is at risk of fraud, the level of risk can vary substantially. 

These variations affect the rate of fraud, both detected and 

undetected, in each agency, program, or operation. Our model did not 

account for such variation. 

Different factors may impact the fraud risk environment (see sidebar). 

Factors could include growth or shrinkage in the budget or scope of 

operations, changes to controls, emergence of new fraud schemes, 

and changes to investigative and prosecutorial priorities that affect the 

detection and adjudication of fraud. For example, if two agency 

programs undertook significant contracting activity but one program 

relied extensively on sole source acquisition (which can be at higher 

risk of fraud due to the lack of competition), while the other used 

competitive bidding, the extent of fraud might be different between the 

two programs. Each of these fraud risk factors, and potentially others, 

could increase or decrease the likelihood of fraud against an agency, 

program, or operation.

Drawing conclusions about the extent of pandemic fraud from 

our estimate. The pandemic is a recent example of how factors such 

as the effectiveness of federal fraud risk management, and the nature 

of new fraud threats, can substantially impact the scale of fraud. We 

have previously reported on the heightened fraud risk environment 

Risk Factors for Fraud

Factors associated with heightened risk of 

fraud include 

• reliance on self-certification; 

• programs that are new to the agency; 

• expansions or major changes in program 

funding, authorities, practices, or 

procedures; 

• a large volume of payments being made; 

• payment or eligibility decisions made 

outside of the agency, such as those by 

state governments; 

• limitations in the experience or training of 

those making eligibility determinations or 

payment certifications; and 

• challenges related to eligibility and 

identity, such as lack of information or 

data systems to confirm eligibility. 

Source: GAO, COVID-19: Insights and Actions for Fraud 

Prevention, GAO-24-107157 (Washington, D.C.: Nov. 14, 

2023). │ GAO-24-105833
Page 21 GAO-24-105833 Federal Fraud Estimate

associated with some pandemic programs.29 These prior reviews 

provided observations about varying fraud risk environments by 

program, and across delivery years, and included a standalone 

estimate of unemployment insurance fraud during the pandemic.

Unlike this prior work, our current model was designed to estimate 

government-wide fraud and included agencies and programs with and 

without pandemic spending. In addition, the fraud and obligation data 

used in our model reflected pandemic and prepandemic time periods. 

As such, our estimate includes but is not limited to fraud against 

federal pandemic spending. Given the scope of our work and the 

nature of the available investigations data, subsetting the estimate to 

describe pandemic programs is not possible. Further, our model 

estimates annual loss within a range, whereas a pandemic estimate 

would reflect loss across multiple spending years.

Comparing estimated fraud to improper payment estimates.

Fraud and improper payments are two distinct concepts that are not 

interchangeable but are related.30 While all fraudulent payments are 

considered improper, not all improper payments are due to fraud. For 

example, payments can be determined to be improper due to error or 

lack of documentation. Given the broader definition, it may seem that 

fraud estimates are a subset of improper payment estimates. 

However, there are two key distinctions that lead to different and not 

comparable estimates. These include differences in

• the scope of programs included in the estimate. Our estimate 

reflects fraud loss associated with direct federal spending in 

29GAO, COVID Relief: Fraud Schemes and Indicators in SBA Pandemic Programs,

GAO-23-105331 (Washington, D.C.: May 18, 2023); GAO-22-105715; and 

GAO-23-106696. Further, in November 2023, we reported that there were also federal 

fraud-related charges pending against at least 599 other individuals or entities involving 

federal COVID-19 relief programs as of June 30, 2023. GAO, COVID-19: Insights from 

Fraud Schemes and Federal Response Efforts, GAO-24-106353 (Washington, D.C.: Nov. 

14, 2023). A charge is merely an allegation, and all defendants are presumed innocent 

until proven guilty beyond a reasonable doubt in a court of law.

30An improper payment is defined by law as any payment that should not have been made 

or that was made in an incorrect amount (including overpayments and underpayments) 

under statutory, contractual, administrative, or other legally applicable requirements. It 

includes any payment to an ineligible recipient, any payment for an ineligible good or 

service, any duplicate payment, any payment for a good or service not received (except 

for such payments where authorized by law), and any payment that does not account for 

credit for applicable discounts. 31 U.S.C. § 3351(4). When an executive agency’s review 

is unable to discern whether a payment was proper because of insufficient or lack of 

documentation, this payment must also be included in the improper payment estimate. 31 

U.S.C. §3352(c)(2). Since fiscal year 2003, executive agencies have reported cumulative 

improper payment estimates of about $2.7 trillion, including $247 billion for fiscal year 

2022 and $236 billion for fiscal year 2023.
Page 22 GAO-24-105833 Federal Fraud Estimate

programs and operations government-wide. In contrast, improper 

payment reporting is required for programs and activities that 

agencies have determined are susceptible to significant improper 

payments. For fiscal year 2022, 18 agencies reported improper 

payment estimates across 82 programs and activities that totaled 

about $247 billion.31 For fiscal year 2023, 14 agencies reported 

estimates for 71 programs and activities that totaled about $236 

billion.32 Total reported improper payment estimates for a given 

fiscal year may not include estimates for certain risk-susceptible 

programs.33 For example, the total reported in estimated improper 

payments for fiscal year 2023 did not include the Department of 

Health and Human Service’s Temporary Assistance for Needy 

Families. This programs and all others across the federal 

government are reflected in our estimate of fraud losses.

• the estimation methodology used. Our estimate is based on 

adjudicated and potential fraud, which is then extrapolated to 

determine the probable range of undetected fraud. This approach 

was developed to help account for the uncertainties associated 

with fraud determination. Conversely, improper payment estimates 

are based on reviews of documentation associated with a 

statistically valid sample of payments. While the reviews can be 

rigorous, they are not designed to identify fraud, particularly 

schemes that cannot be easily detected. For example, improper 

payment reviews include checking whether certain documentation 

is present, but they are not designed to identify falsified 

documentation. Building our estimate around known and potential 

fraud eliminates one element of uncertainty associated with fraud 

determination.

Assuming the estimate is predictive of future federal fraud. Our

estimate is not based on a predictive model. Future federal operations 

and budgets will present different fraud risk environments and 

31GAO, Improper Payments: Fiscal Year 2022 Estimates and Opportunities for 

Improvement, GAO-23-106285, (Washington, D.C.: March 29, 2023). Our 2023 priority 

recommendation letters included 59 priority recommendations related to improper 

payments.

32GAO, Improper Payments: Information on Agencies’ Fiscal Year 2023 Estimates, 

GAO-24-106927, (Washington, D.C.: Mar. 26, 2024).

33GAO, Financial Audit: FY 2023 and FY 2022 Consolidated Financial Statements of the 

U.S. Government, GAO-24-106660 (Washington, D.C.: February 15, 2024). In our audit 

reports on the U.S. government’s consolidated financial statements, we note that the 

federal government is unable to determine the full extent to which improper payments 

occur. Our most recent report was issued in February 2024.
Page 23 GAO-24-105833 Federal Fraud Estimate

associated fraud losses. Factors such as the amount of emergency 

spending, the effectiveness of federal fraud risk management, and the 

nature of new fraud threats could substantially impact the scale of 

future fraud losses.

On the basis of our understanding of fraud, investigations, available data, 

and the findings of other fraud measurement and estimation studies, we 

identified three fraud categories—adjudicated fraud, detected potential 

fraud, and undetected potential fraud. Within detected potential fraud, we 

developed three subcategories. These categories reflect different degrees 

of certainty about the possibility of fraud. For example, an instance of 

adjudicated fraud is certainly fraudulent. In contrast, the certainty of fraud 

may be substantially lower for an instance of potential fraud that has been 

detected and not accepted for investigation.

Using these fraud categories, we collected and analyzed data from three 

key sources. Individually, these data do not provide a comprehensive 

understanding of the extent of fraud but, when analyzed collectively,

provide support for a government-wide estimate. These data include

• investigative case data from 12 selected OIGs,

• OIG semiannual reports, and

• confirmed fraud reported to the OMB’s Paymentaccuracy.gov 

dashboard.

Each of these data sources has strengths and limitations, which impact 

our estimate. The data sources were generally available from all selected 

agencies and included information on detected potential and adjudicated 

fraud. However, there was variance in terms and definitions used across 

the data sources, and not all sources included information on potential 

fraud. Finally, data for undetected fraud, by definition, do not exist. We 

considered these strengths and limitations in determining the data to 

collect and our estimation approach.

Our review of 46 fraud measurement or estimation studies also informed 

the development of these categories and use of data within them. Studies 

include those developed by agencies, such as the OIGs for the Small 

Business Administration and the Department of Labor; academics;

professional organizations; and international entities, such as the United 

Kingdom’s Public Sector Fraud Authority.

Fraud Categories and 

Data Sources
Page 24 GAO-24-105833 Federal Fraud Estimate

Figure 7 provides additional information on the fraud categories and the 

data used in our simulation, by category.

Figure 7: Fraud Categories and the Data Collected and Used in the Simulation, by Category

a

For the purposes of this review, we defined “adjudication” as the legal or administrative process of 

resolving a dispute and that includes a formal, fact-finding process; due process; and a formal 

determination of the facts. 

b

For the purposes of this review, we defined “full investigation” as a thorough inquiry into alleged facts 

to find out the truth about a potential crime or violation and for which a determination can be made on 

whether there is substantial evidence to refer the case for a judicial or administrative remedy. 

Our estimate was based on our Monte Carlo simulation, which relied 

upon certain key assumptions that reflect how we collected and used 

Key Assumptions
Page 25 GAO-24-105833 Federal Fraud Estimate

agency and OIG data for our estimate. One such assumption relates to 

agencies that were not selected as part of our sample of 12. Specifically, 

we assumed that the relationship between the number of fraud 

occurrences and the financial loss associated with an individual 

occurrence of fraud in these agencies was similar to the relationships 

observed at the 12 agencies in our sample. We took this approach, given 

that our agency selection included about 90 percent of agency obligations 

and also reflected a variety of different programs and operations.

We designed our approach so that the financial loss amount of each 

instance of fraud or potential fraud was lower for the less certain 

categories. For example, we assumed that instances of potential fraud 

that were detected by the federal government but not investigated tended 

to have lower associated financial loss than instances of fraud that were 

investigated and ultimately adjudicated by U.S. courts and other bodies.

We simulated values for undetected fraud by relying on the information 

that we had available about detected potential fraud. We considered 

several potential models of the relationship between detected and 

undetected fraud to help account for the substantial uncertainty 

associated with the undetected fraud category.

Across all categories, we did not assume a single value, but rather a

range of values was included in our simulation to capture the degree of 

uncertainty associated with the subject area.

Understanding the extent of fraud supports effective fraud risk 

management. The Fraud Risk Framework includes several leading 

practices that highlight the importance of understanding the scope of 

fraud to better manage the risks, such as for managers to conduct 

quantitative or qualitative assessments of the likelihood and impact of 

inherent risks on the program’s objectives. This helps agencies allocate 

resources to respond to their more significant fraud risks. The Fraud Risk

Opportunities and 

Challenges in 

Estimating Fraud to 

Support Fraud Risk 

Management

Developing and Using 

Fraud Estimates Supports 

Fraud Risk Management
Page 26 GAO-24-105833 Federal Fraud Estimate

Framework also notes the importance of including qualified specialists to 

estimate fraud loss and frequency, among other data-analytics activities.

While our estimate focused on government-wide fraud, multiple agency 

and OIG officials told us that more granular estimates, such as those at 

the program level, are particularly helpful. Officials also stated that the 

likelihood of fraud can vary significantly by program. For example, 

multiple OIG officials stated that programs that accept self-certification by 

applicants to obtain government benefits tend to have much higher fraud 

risks and fraud rates than programs that require additional verification. In 

addition, Association of Certified Fraud Examiners officials stated that 

granular estimates provided more actionable information to guide 

oversight.

Agency and OIG officials and fraud experts identified additional ways that 

program-level fraud estimates can improve fraud risk management. 

These include the ability to demonstrate the scope of the problem,

improve oversight prioritization, and demonstrate return on investment for 

oversight investments (see fig. 8).

Figure 8: How Fraud Estimates Can Improve Fraud Risk Management
Page 27 GAO-24-105833 Federal Fraud Estimate

Demonstrate scope of problem: As noted in the Fraud Risk Framework, 

fraud estimates can be used to help assess program fraud risks.34 It also 

states that capturing information on fraud schemes, trends, and outcomes 

from fraud measurement activities can aid fraud risk management efforts. 

These risks can be both financial and nonfinancial in nature. Better 

estimates of the scope of fraud at program, agency, and governmentwide levels can help inform decisions about the level of resources to 

commit to fraud risk management activities. In addition, continued 

refinement of estimates can help agencies further strengthen their fraud 

risk assessments.

In November 2023, we reported on factors that are important to agencies 

when managing fraud risks.35 In survey responses, 18 officials from the 

24 Chief Financial Officers (CFO) Act agencies noted that the amount lost 

to fraud was an extremely or very important factor when managing fraud 

risks.36

Fraud experts have also noted how fraud estimation can support fraud 

risk management. For example, the Director of the University of 

Portsmouth Centre for Cybercrime and Economic Crime told us that fraud 

estimation can be used to help raise awareness of the risks of fraud in 

particular areas and show the importance of making investments to 

counter fraud. The Director noted that without estimation, some may 

assume that a problem does not exist and, thus, not direct resources to 

stop fraud from occurring. The Director also noted that most organizations 

underestimate their fraud levels because they confuse detected levels of 

fraud with the real level of fraud. The Director noted that detected levels 

34GAO-15-593SP.

35GAO, Fraud Risk Management: Agencies Should Continue Efforts to Implement Leading 

Practices, GAO-24-106565 (Washington, D.C.: Nov. 1, 2023).

36The 24 agencies surveyed are those listed in the CFO Act of 1990, as amended. These 

agencies include the Departments of Agriculture, Commerce, Defense, Education, 

Energy, Health and Human Services, Homeland Security, Housing and Urban 

Development, the Interior, Justice, Labor, State, Transportation, the Treasury, and 

Veterans Affairs, as well as the Agency for International Development, Environmental 

Protection Agency, General Services Administration, National Aeronautics and Space 

Administration, National Science Foundation, Nuclear Regulatory Commission, Office of 

Personnel Management, Small Business Administration, and Social Security 

Administration. Pub. L. No. 101-576, § 205, 104 Stat. 2838 (1990), codified, as amended, 

at 31 U.S.C. § 901(b).
Page 28 GAO-24-105833 Federal Fraud Estimate

of fraud represent the small tip of the “fraud iceberg” and that substantial 

levels of undetected fraud likely remain below the surface.37

Officials with an OIG we spoke with stated that fraud rates were likely 

larger than some assumed, and that estimation could help show the 

scope of the problem. Through a better understanding of the problem, 

actions such as additional oversight could be taken. We have previously 

reported that estimation practices could produce different results, 

depending on the key assumptions used or how the analysis was 

conducted.38 In conducting estimates, multiple agency and OIG officials 

told us that program-level estimates are particularly helpful because the 

likelihood of fraud can vary significantly by program. Officials also stated 

that it is important to estimate fraud in such a way that it would effectively 

capture the risks to the program and that the estimates were correctly 

interpreted. In one example, officials said that benefits programs that 

include a self-certification component tend to have much higher fraud 

risks and fraud rates than programs requiring additional verification. In 

another example, an agency official stated that agencies and states use 

varying definitions of fraud that may vary by the program and relevant 

legal statutes. Likewise, another official emphasized that there was not a 

clear definition of potential fraud. By defining fraud or potential fraud 

differently, fraud rates could differ substantially.

Officials also noted that organizations needed to develop estimates that 

reflected the true extent of fraud and, thus, did not misinform decisionmaking.

Improved oversight prioritization: The Fraud Risk Framework notes 

that effective managers of fraud risk use the program’s fraud risk profile to 

help decide how to allocate resources to respond to residual fraud risks. 

As federal fraud estimates mature, additional information on program 

risks could help refine fraud risk assessments that are documented in the 

profile.

Officials with several OIGs noted that an estimate of fraud could 

potentially help prioritize oversight resources. For example, officials at 

37We recently reported on the stages of fraud detection, including the known and 

unknown aspects of fraud. See GAO-24-106353. 

38GAO, Unemployment Insurance: Data Indicate Substantial Levels of Fraud during the 

Pandemic, DOL Should Implement an Antifraud Strategy, GAO-23-105523 (Washington: 

D.C.: Dec. 22, 2022). 
Page 29 GAO-24-105833 Federal Fraud Estimate

one OIG stated that additional fraud estimates could help guide the OIG 

to vulnerable programs across the agency. Officials also stated that fraud 

estimation could help enhance regulatory change to improve oversight for 

those programs that appeared to have a higher rate of fraud. Officials with 

another OIG noted that their agency had many different types of 

programs and operations, all with different potential fraud risks. Officials 

noted that having more information on the rate of fraud could help better 

target scarce oversight resources.

Association of Certified Fraud Examiners officials stated that detailed 

information about the likely extent of fraud can help organizations conduct 

risk analysis to better target fraud. OIG officials also cautioned that 

estimation needed to be performed and interpreted correctly, or the 

results of the estimation could misdirect oversight resources. If a program 

was determined to have a low rate of fraud when, in fact, it had a high 

rate, it may receive less oversight than needed.

Help demonstrate return on investment: OIG officials told us that fraud 

estimation could help demonstrate the return on investment of fraud risk 

management activities. In addition, the CFO Council notes that while it 

might be difficult to measure outcomes as a result of fraud prevention 

tactics, it is a vital step to an effective and robust antifraud program and 

can lead to a significant return on investment.39 Program Integrity: The 

Antifraud Playbook states that repeated monitoring and periodic 

evaluations provide insight into the effectiveness of fraud risk 

management activities. Knowing the extent of fraud affecting a program 

through estimates can be a useful data point in determining the 

effectiveness of fraud risk management activities. This is consistent with 

what we heard from an OIG official, who stated that more fraud 

measurement or estimation could help justify investment in fraud 

prevention and detection techniques.

The Director for the Centre for Cybercrime and Economic Crime at the 

School of Criminology and Criminal Justice at the University of 

Portsmouth in the United Kingdom stated that without clear measures of 

performance, it is difficult for entities involved with combating fraud to 

demonstrate their contribution to an organization and to see how well they 

39Chief Financial Officers Council, Program Integrity: The Antifraud Playbook (Oct. 17, 

2018), accessed Nov. 27, 2023, https://www.cfo.gov/assets/files/Interactive-TreasuryPlaybook.pdf. The playbook provides a four-phased approach, with 16 plays drawn from 

successful practices from the federal government and private sector to help combat the 

risk of fraud at an agency. 
Page 30 GAO-24-105833 Federal Fraud Estimate

are doing. The Director noted that without clear metrics, it can be 

challenging for increasingly financially focused organizations in both the 

private and public sector to answer questions about their performance.

Estimates of fraud in federal programs are limited, with recent estimation 

being completed in response to the unprecedented fraud against federal 

pandemic programs.40 The Fraud Risk Framework and other prior work 

have identified challenges related to determining the extent of fraud.41 In 

developing our government-wide fraud estimate, we faced several of 

these previously identified challenges, among others.

As part of our work to estimate the extent of federal fraud, we considered 

a variety of data sources. On the basis of discussions with 12 selected 

agencies, we identified varying amounts of data. While some agencies 

have data that could be informative for fraud estimation, many do not, or 

the data they do have require extensive analysis to support fraud 

estimation.

Some agencies have collected significant program data. These data have 

been used to detect potential fraud, such as through data matching, data 

mining, and network analysis.42 These data could be informative for future 

program fraud estimates. For example:

• The Centers for Medicare & Medicaid Services Fraud Prevention 

System uses predictive models and other algorithms to identify 

medical providers and suppliers exhibiting a pattern of behavior 

indicative of potential fraud.

• In 2016, the Social Security Administration began implementing a 

multiphase project to integrate data from multiple sources and use 

predictive analytics to identify high-risk transactions for review to 

prevent fraudulent actions from advancing.

40For example, see GAO-23-106696; Department of Labor, Office of Inspector General, 

Report No. 19-22-005-03-315); and Small Business Administration, Office of Inspector 

General, COVID-19 Pandemic EIDL and PPP Loan Fraud Landscape, White Paper 

Report 23-09. (Washington, D.C.: June 27, 2023).

41GAO-15-593SP; GAO, GAOverview: Fraud in the Federal Government – Challenges 

Determining the Extent of Federal Fraud, GAO-23-106110 (Washington, D.C., Jan. 23, 

2023).

42Network analysis is a quantitative approach to identifying and graphically representing 

potentially unknown relationships among individuals or organizations.

Challenges in Estimating 

Fraud

Data to Support Further Fraud 

Estimation Are Not Readily 

Available and Usable
Page 31 GAO-24-105833 Federal Fraud Estimate

In addition, the results of data analytics undertaken for program integrity 

or OIG audits and evaluations could be useful for understanding the 

extent of potential fraud facing a program. For example, in 2022, the 

Department of Labor’s OIG used data analytics to identify potentially 

fraudulent unemployment insurance claims.43

We found that OIG case management data and OIG semiannual reports 

to Congress contained data to support fraud estimates, but these data did 

not provide all the information needed. For example, OIG reporting 

excluded information on potential fraud captured by the agency that was 

not reported to the OIG for investigation. Further, in reviewing OIG 

semiannual reports to Congress, we identified variability in how data were 

reported that affects their usefulness for fraud estimation. For example, 

we reviewed information related to investigative recoveries. In some data, 

recoveries were reported as the only data point. In other data, recovered 

amounts were combined with fines, or limited to only administrative 

recoveries, and excluded court-ordered recoveries. We found similar 

variability in reporting of data regarding forfeitures, recoveries, and 

restitution, as shown in figure 9. This variability results in data that cannot 

be readily compared or consolidated to understand the financial impacts 

of fraud across the federal government.

43Department of Labor, Office of Inspector General, 19-22-005-03-315. 
Page 32 GAO-24-105833 Federal Fraud Estimate

Figure 9: Examples of Variation in How Fraud-Related Data Are Reported in Office of Inspectors General Semiannual Reports 

to Congress

We also found that reliable information on the potential effect of fraud in 

OIG data is often not captured. This can make it difficult to determine the 

extent of the impact from fraud that agencies and programs might face. 

For example, multiple OIG officials told us that they did not capture 

information on the financial impact of a fraud case unless it was formally 

adjudicated. Further, officials also said that initial information on the total 

loss to fraud gathered during an investigation can be inaccurate and 
Page 33 GAO-24-105833 Federal Fraud Estimate

might vary significantly from the outcome of the case. For example, the 

initial information provided by a whistleblower tip might be exaggerated. 

Conversely, an investigation might uncover additional information that 

would indicate that the fraud was much larger than previously believed.

Our past work has also noted that legal limitations can inhibit sharing of 

key data with some agencies that could help identify fraud-related 

payments. For example, in 2023 we reported that the Small Business 

Administration faced statutory obstacles that prevent the Social Security 

Administration from sharing key data, including Social Security numbers, 

with the Small Business Administration.44

A program official told us that different entities, such as federal and state 

programs, might use varying definitions of fraud, which can limit the ability 

to compare and aggregate data. Multiple program and OIG officials told 

us that there are different definitions of fraud used across the 

government. For example, a Department of Labor official told us that 

many state workforce agencies use different definitions of fraud, which 

complicates attempts to estimate fraud associated with unemployment 

insurance programs. Department of Health and Human Services’ OIG 

officials also noted that there are differing definitions of fraud that can 

vary across programs and statutes that can complicate attempts to 

determine the extent of fraud. We have previously reported that varying 

definitions of fraud can impact reporting and that some entities use 

broader definitions of fraud.45

Direct measures of undetected fraud, by definition, do not exist. 

Identifying previously undetected fraud, assessing related proxies, or 

estimating undetected fraud can be resource intensive and require 

significant analysis and expertise. Despite these challenges, information 

on the amount of undetected fraud is necessary to understand the full 

scope of likely fraud. To help determine the extent of undetected fraud, 

agencies and OIGs have employed methods, such as statistical sampling, 

44GAO, COVID Relief: Fraud Schemes and Indicators in SBA Pandemic Programs,

GAO-23-105331 (Washington, D.C.: May 18, 2023). 

45GAO defines fraud as obtaining a thing of value through willful misrepresentation. Willful 

misrepresentation can be characterized by making material false statements of fact based 

on actual knowledge, deliberate ignorance, or reckless disregard of falsity. Other entities 

use broader definitions that include settlements, suspected fraud, or prevented fraud. 

These varying definitions can result in different reported fraud amounts, which could 

prevent comparison and summary across agencies. See GAO-23-106110. 

Use of Varying Fraud 

Definitions Impacts Reporting

Determining the Amount of 

Undetected Fraud Is 

Challenging
Page 34 GAO-24-105833 Federal Fraud Estimate

to estimate a rate of fraud, or data analytics to attempt to identify 

previously undetected fraud.46

Given the hidden nature of fraud, a certain portion of fraud will go 

undetected. Further, multiple agency and OIG officials stated that the 

extended time it takes to investigate and prosecute cases of fraud can 

also make it more challenging to use existing data to determine the extent 

of fraud. These time lags can make it difficult to determine the prevalence 

or amount of fraud at a given time, as the data may reflect events that 

occurred years in the past. Officials with one OIG stated that while cases 

generally took 1-1/2 to 2 years to move from identification to conviction, 

some cases could take a decade to complete. 

Further, not all potential fraud is investigated or prosecuted. For example, 

an OIG official noted that OIG can only investigate the “worst of the worst” 

and that OIG does not have the capacity to investigate many possible 

instances of fraud. To help prioritize scarce investigative resources, some 

investigative organizations have thresholds that may impact the fraud 

they investigate (see sidebar). Further, the statute of limitations may 

cause investigative organizations to prioritize some cases over others.

As demonstrated by our and other estimates of the extent of fraud in the 

federal government or programs, expertise and data-analysis capacity are 

needed to develop fraud estimates. For example, our government-wide 

fraud estimate required an investigative data request from the OIGs of the 

12 selected agencies, as well as additional data, such as from OIG 

semiannual reports to Congress. We also made use of significant fraud 

46Department of Agriculture, The Extent of Trafficking in the Supplemental Nutrition 

Assistance Program: 2015-2017 (Alexandria, VA: September. 2021); Department of 

Labor, Office of Inspector General, 19-22-005-03-315; Small Business Administration, 

Protecting the Integrity of the Pandemic Relief Programs: SBA’s Actions to Prevent, 

Detect and Tackle Fraud. (Washington, D.C.: June 2023); and Small Business 

Administration, Office of Inspector General, White Paper Report 23-09. 

Fraud Is Not Easy to Detect, 

Investigate, or Prove

Case Thresholds Limit Amount of Fraud 

Investigated and Adjudicated 

Case thresholds can limit what cases are 

pursued for investigation and potential referral 

for prosecution and adjudication. Officials at 

one Office of Inspector General’s office stated 

that for certain contract fraud investigations to 

be initiated, the size of the contract needed to 

be at least $2 million and an estimated fraud 

loss of at least $500,000. Other investigations, 

such as those for certain types of direct 

assistance, had a lower threshold of $30,000. 

Officials did note, though, that if thresholds 

are not met, the investigation might still occur 

if there are other concerns, such as corruption 

by public officials. 

Source: Office of Inspector General officials. │ 

GAO-24-105833

Expertise and Data Analysis 

Capacity Needed for 

Estimation Efforts
Page 35 GAO-24-105833 Federal Fraud Estimate

expertise to interpret these data and statistical expertise to further 

analyze the data and develop our estimate.

Agency and OIG officials told us that additional fraud expertise and dataanalysis capacity would help improve the accuracy of fraud measures and 

estimates.47 These officials stated that agencies needed a certain level of 

sophistication in their oversight efforts before they would be positioned to 

accurately estimate fraud. For example, if an agency’s internal controls 

and analysis efforts were deficient, it would be difficult to develop data 

that would be useful for estimation.

Our 2023 survey of 24 CFO Act agencies identified significant challenges 

related to expertise, data, and tools for fraud risk management.48 In 

particular, agencies reported challenges related to the availability of 

resources such as staff, access to data-analysis tools and techniques, 

and access to data to look for fraud as their top challenges to fraud risk 

management efforts. These challenges could also impact the ability to 

develop effective fraud estimates.

OIG officials also expressed concerns about the development and use of 

estimation redirecting oversight resources to estimation activities versus 

oversight. The officials stated that estimation activities are typically time 

consuming and could redirect already scarce staff resources from audit or 

investigative work.

We previously reported in the International Journal of Government 

Auditing on the opportunities for data analytics, including the use of 

artificial intelligence to help identify potential fraud, which could further aid 

estimation.49 However, agencies have reported that artificial intelligence 

can also pose new risks to oversight efforts, such as when used by 

47In March 2022, we recommended that Congress consider establishing a permanent 

analytics center of excellence to aid the oversight community in identifying improper 

payment and fraud. We found that without permanent government-wide analytics 

capabilities to assist the oversight community, agencies will have limited resources to 

apply to nonpandemic programs to ensure robust financial stewardship, as well as to 

better prepare for applying fundamental financial and fraud risk management practices to 

future emergency funding. See GAO-22-105715. 

48GAO-24-106565. 

49GAO, “Artificial Intelligence Creates New Opportunities to Combat Fraud,” International 

Journal of Government Auditing (Summer 2020). 

Artificial Intelligence Creates 

Opportunities for Improved Fraud 

Detection but Also for Fraud

We have previously reported that artificial 

intelligence has created opportunities for 

improved oversight and fraud detection. 

Artificial intelligence can use algorithms and 

models to reveal anomalous patterns, 

behaviors, and relationships—with speed, at 

scale, and in depth—that was not possible 

previously. 

Despite these opportunities, artificial 

intelligence can also pose new risks to 

agencies and others, such as by creating fake 

images to assist with developing falsified 

documentation or to create fake audio to 

assist in impersonation schemes. 

Sources: GAO, “Artificial Intelligence Creates New 

Opportunities to Combat Fraud,” International Journal of 

Government Auditing (Summer 2020); Department of Health 

and Human Services, Health Sector Cybersecurity 

Coordination Center: AI-Augmented Phishing and the Threat 

to the Health Sector (Oct. 26, 2023); and Department of 

Homeland Security, Increasing Threat of Deepfake Identities.

| GAO-24-105833
Page 36 GAO-24-105833 Federal Fraud Estimate

fraudsters to execute new fraud schemes, which may evade detection 

(see sidebar).

Current guidance on the collection of data for fraud estimation is limited to 

efforts to support OIG semiannual reports to Congress and confirmed 

fraud reporting to OMB. Further, there are no plans to expand the use of 

fraud estimation to enhance fraud risk management in the executive 

branch or to leverage data experts across government to support such 

estimates. Despite these limitations, opportunities exist government-wide 

to build on current OIG oversight and agency program integrity efforts and 

increase the availability of data, expertise, and data-analytics capacity 

needed to develop estimates. Further, our Schedule Assessment Guide 

notes the value in using timelines and key milestones to help guide 

implementation for projects, which can include government-wide 

initiatives.50

OMB and CIGIE issue guidance and requirements to the executive 

branch agencies and OIG community, respectively. While OMB has 

issued management guidance to agencies, including on their 

responsibilities for improving payment integrity and fraud risk 

management, it has not established guidance or plans for guidance on 

data collection to support fraud estimation. There are also existing 

requirements for fraud-related data collection and reporting, such as 

through the Inspector General Act, as well as associated data systems 

and processes to meet those requirements.51 However, this collection and 

reporting is not designed to support fraud measurement or estimation. 

CIGIE has developed Quality Standards for Investigations that require 

investigative data to be stored in a manner that allows effective retrieval, 

reference, and analysis, while ensuring the protection of sensitive data. 

These standards were not developed to enable fraud measurement or 

estimation.52

The federal government has also established data-capacity resources to 

aid program integrity, and these resources could also aid estimation. For 

example, the Department of the Treasury’s Office of Payment Integrity, 

which includes the Do Not Pay Business Center and the Payment 

50GAO, Schedule Assessment Guide: Best Practices for Project Schedules, GAO-16-89G

(Washington, D.C.: Dec. 22, 2015).

515 U.S.C. App. § 5. 

52The Council of the Inspectors General on Integrity and Efficiency, Quality Standards for 

Investigations (Nov. 15, 2011).

Opportunities to Expand 

Fraud Estimation
Page 37 GAO-24-105833 Federal Fraud Estimate

Integrity Center of Excellence, offers data-analytics capacity to help 

agencies address payment integrity challenges. These centers have 

access to data-analysis resources and fraud expertise to help prevent 

improper payments due to fraud and have worked with numerous 

datasets for programs with different fraud risks. Specifically, these centers 

help provide support to agency payment integrity efforts with various 

expenditure types, such as direct beneficiary payments, contracts, and 

grants. Because of these efforts, the Department of the Treasury has 

unique insights that could be used to evaluate and identify methods to 

expand government-wide fraud estimation. OMB has also directed 

agencies to use Do Not Pay’s analytic capacity to identify potentially 

fraudulent payments to help enhance program integrity. However, these 

centers have not been leveraged to evaluate and identify methods to 

expand fraud estimation to date.

Treasury’s Office of Payment Integrity is well positioned— with the 

expertise, data, and analytic tools—to evaluate and advance approaches 

the federal government can take to estimate fraud in support of fraud risk 

management. Moreover, as fraud-related data available to the Office of 

Payment Integrity expands or improves, it would also be well positioned 

to refine estimates.

The Fraud Risk Framework notes that assessing the likelihood and 

impact of inherent fraud risk may include involving qualified specialists, 

such as statisticians and subject-matter experts.53 These specialists may 

contribute expertise and guidance when employing techniques like 

analyzing statistically valid samples to estimate fraud losses and 

frequency.

Federal internal control standards require managers to use quality 

information.54 Specifically, internal control standards require management 

to obtain relevant data from reliable internal and external sources in a 

timely manner based on the identified information requirements. The 

standards also direct management to process the obtained data into 

quality information that supports the internal control system. This involves 

processing data into information and then evaluating the information so 

that it is quality information.

53GAO-15-593SP. 

54GAO-14-704G. 
Page 38 GAO-24-105833 Federal Fraud Estimate

By leveraging existing oversight mechanisms and payment integrity datacapacity resources, the government would be better positioned to 

address the different challenges in fraud estimation. In doing so, agencies 

and others, such as the OIGs, will be better positioned to use estimation 

to help demonstrate the scope of the problem, prioritize resources, and 

demonstrate return on investment for fraud risk management activities.

Our estimate of direct annual financial losses from fraud affecting the 

federal government provides insights not obtained through previous 

analysis and reporting on the extent of fraud across the federal 

government. The significant estimated annual loss from fraud—ranging 

from $233 billion to $521 billion—reinforces the importance of fraud risk 

management, with an emphasis on prevention.

With additional data and more granular estimates, such as at the program 

level, agencies would be better positioned to leverage this information to 

strategically manage fraud risk. For example, targeted estimates can 

provide a better understanding of the scope of the problem in different 

program areas, help prioritize resources, and demonstrate return on 

investment from fraud prevention and detection efforts. However, the 

federal government faces challenges in producing more precise fraud 

estimates, including incomplete and varyingly recorded data on identified 

fraud. A government-wide approach is required to address these 

challenges. Centralized guidance from OMB for improving data collection 

to support fraud estimates can provide a more uniform approach to what 

data are collected, and how. Further, identifying ways to expand the use 

of fraud estimation—leveraging the significant analytics expertise and 

data repository of Treasury’s Office of Payment Integrity—will help 

strengthen antifraud efforts and promote fiscal sustainability governmentwide.

We are making three recommendations, including one to OMB in 

collaboration with CIGIE, one to OMB, and one to the Department of the 

Treasury in consultation with OMB. Specifically:

The Director of the Office of Management and Budget, in collaboration 

with the Council of the Inspectors General on Integrity and Efficiency, 

should develop guidance on the collection of Office of Inspector General 

(OIG) data to support fraud estimation. The guidance should

• identify and establish consistent data elements and terminology for 

use across OIGs;

Conclusions

Recommendations for 

Executive Action
Page 39 GAO-24-105833 Federal Fraud Estimate

• include a timeline for implementation and key milestones; and

• leverage existing data systems and processes, as appropriate. 

(Recommendation 1)

The Director of the Office of Management and Budget, with input from 

executive branch agencies, should develop guidance on the collection of 

executive agency data to support fraud estimation. The guidance should

• identify and establish consistent data elements and terminology for 

use across agencies;

• include a timeline for implementation and key milestones; and

• leverage existing data systems and processes, as appropriate. 

(Recommendation 2)

The Secretary of the Treasury, in consultation with the Office of 

Management and Budget, should establish an effort to evaluate and 

identify methods to expand government-wide fraud estimation to support 

fraud risk management. This effort should

• initially prioritize program areas at increased risk of fraud;

• be responsive to changes in the availability or quality of data; and

• leverage data-analytics capabilities, such as within the Office of 

Payment Integrity, which includes the Do Not Pay program. 

(Recommendation 3)

We provided a draft of this report to OMB, CIGIE, and the Department of 

the Treasury for review and comment. We received written comments 

from OMB, which are reproduced in appendix III and summarized below. 

Both CIGIE and the Department of the Treasury provided comments via 

email in lieu of formal, written comments. We also provided a draft of the 

report to the 12 selected agencies and their respective OIGs for technical 

comments, if any. In response, we received technical comments from the 

Departments of Health and Human Services, Labor, and Justice; the 

Small Business Administration; and the Department of Labor’s OIG, 

which we incorporated as appropriate. The other agencies and OIGs did 

not provide comments.

In its written comments, OMB generally agreed with the two 

recommendations directed to it and with the need for improved data 

collection and reporting at the agency and program levels. Separately, 

OMB informed us that it had been in touch with CIGIE and they will work 

Agency Comments 

and Our Evaluation
Page 40 GAO-24-105833 Federal Fraud Estimate

together to determine appropriate next steps regarding our 

recommendations.

OMB agreed with several aspects of our report, including the following: 

• Federal agencies must do a better job assessing and preventing fraud 

risk and they should more completely and consistently apply GAO’s 

Fraud Risk Framework to their programs to ensure that fraud risks are 

properly assessed, mitigated, and monitored on an ongoing basis. 

• Rigorous analysis of fraud and fraud risk, at the program level, can be 

highly valuable in driving agency action and ongoing leadership 

prioritization of combatting fraud. 

• The level of risk can vary substantially between agencies and 

programs. 

• The amount of captured fraud and recoveries underestimates total 

loss from fraud. 

OMB also expressed support for our ongoing analysis of fraud risks, 

efforts to estimate program-specific fraud rates to inform future program 

design, and guidance provided by our Fraud Risk Framework. OMB 

highlighted its collaboration with us and others to identify and reduce 

fraud risk. For example, OMB noted collaboration through the Joint 

Financial Management Improvement Program’s Payment Integrity 

Initiative and the issuance of a Controller Alert on identifying and 

assessing fraud risks.55 OMB also described several actions agencies 

have taken to reduce fraud and improper payments. This includes 

implementing additional safeguards and investing in antifraud and 

modernization efforts for state unemployment systems.

We appreciate the past coordination with OMB, as well as its efforts and 

those of agencies, to combat fraud. We look forward to continued 

coordination and collaboration government-wide on such efforts. We 

agree that progress has been made on fraud risk management. We also 

agree that there is more to be done to prevent and reduce fraud. 

55Joint Financial Management Improvement Program (JFMIP) is a cooperative venture 

between GAO, OMB, the Office of Personnel Management, and the Department of the 

Treasury. JFMIP, Payment Integrity Initiative: A Three Year Plan to Advance Payment 

Integrity, JFMIP-24-02 (Feb. 2024). See also Office of Management and Budget, 

Establishing Financial and Administrative Controls to Identify and Assess Fraud Risk, CA23-03 (Washington, D.C., Oct. 17, 2022).
Page 41 GAO-24-105833 Federal Fraud Estimate

Beyond those points of agreement, however, OMB raised concerns about 

our estimate and how it would be interpreted. Specifically, OMB stated 

that our estimate was based on a “simulation model” rather than analysis 

of estimated losses by individual federal programs and that our 

government-wide estimate would not provide agency- or program-specific 

insights for fraud prevention. OMB also questioned the plausibility of our 

estimate. 

Our estimated range of fraud loss is based on a well-established 

simulation model. As we make clear in the report, it was designed to 

provide a government-wide estimate, as opposed to agency- or programlevel estimates. We disagree with OMB’s implication that our estimate is 

not based on fraud loss data from federal agencies or programs. To the 

contrary, fraud frequency and loss data, covering fiscal years 2018 

through 2022, collected from the 12 selected OIGs, serve as the primary 

basis for our estimate. These data reflect adjudicated and potential fraud 

activity within the agencies and programs. They also provide the basis for 

our simulation of undetected fraud. While we aggregated these data in 

the simulation model for statistical reliability reasons, to suggest that our 

model is not grounded in relevant, appropriate, agency or program data is 

inaccurate. Because these data are not publicly or readily available in a 

format suitable for fraud estimation, we developed a rigorous approach to 

collect, collate, and assure reliability for simulation purposes. 

OMB’s narrow view of fraud is generally confined to confirmed fraud, 

which is a subset of adjudicated fraud cases. All adjudicated fraud—but 

especially what is measured in OMB’s confirmed fraud reports—reflects 

only a small portion of the full extent of fraud. Further, OMB’s comments 

overlook the intent and objective of our effort to develop a first-ever,

government-wide estimate of losses due to fraud. A government-wide 

estimate can help OMB, program officials, or Congress begin to 

understand and assess the scope of the problem and drive action to 

address it.

Our estimate also builds on the intent of the Fraud Reduction and Data 

Analytics Act of 2015 (FRDAA), and its successor, the Payment Integrity 

Information Act of 2019 (PIIA).56 FRDAA and certain provisions in PIIA 

were enacted to improve agencies’ controls and procedures to assess 

and mitigate fraud risks and improve data analytics to identify, prevent, 

and respond to fraud. PIIA included requirements for OMB to take actions 

56Pub. L. No. 114-186, 130 Stat. 546 (June 30, 2016); Pub. L. No. 116-117, § 2(a), 134 

Stat. 113, 131-32 (2020) (codified at 31 U.S.C. §3357). 
Page 42 GAO-24-105833 Federal Fraud Estimate

to support agencies’ fraud risk management, such as by establishing an 

Interagency working group on government-wide payment integrity 

improvement. While OMB has recently initiated such meetings, its lag in 

doing so represents lost opportunities for agencies to share and receive 

information that could have supported their payment integrity efforts—

particularly amid the challenges associated with pandemic spending. 

The need—and requirements—for agencies to take action to address 

fraud was reiterated in our February 2023 testimony, which identified 

agencies’ continued lag in implementing fraud risk management activities 

as a major factor contributing to pandemic programs' exposure to fraud.57

In November of that year, we reported that of the 173 recommendations 

we had made to over 40 agency or program offices to improve fraud risk 

management since 2015, over half (95) remained unimplemented.58

As discussed in our report, we recognize the value of more granular-level 

estimates, such as those at the agency or program level. This is why we 

made recommendations to improve the data available so that such 

estimates could be developed in the future. We see this government-wide 

estimate as an important first step. 

Our report repeatedly cautions against any attempt to use our estimated 

range to reverse engineer how much may have been lost due to fraud for 

any particular agency or program or predict future fraud losses. OMB did 

this, however, in its written comments questioning the plausibility of our 

estimate. Specifically, OMB took our calculation comparing the fraud 

estimate to annual obligations and inappropriately applied it to various 

program areas. In doing so, OMB said that certain areas of federal 

spending have no or low fraud risk, removed those areas of spend from 

its calculation, and then applied the upper end of the percentage range to 

the remaining spending. OMB noted this results in an implausibly high 

level of fraud loss from all other federal programs and suggests longstanding fraud across many federal programs over multiple years 

comparable to pandemic spending fraud. 

We disagree with OMB’s approach, assumptions, and conclusions on this 

issue. The extent and impact of fraud is not easily identified through 

informal means, and OMB has not performed a meaningful analysis with 

supporting data of fraud risk in individual federal programs. For example, 

57GAO, Emergency Relief Funds: Significant Improvements Are Needed to Address Fraud 

and Improper Payments, GAO-23-106556 (Washington, D.C: Feb. 1, 2023).

58GAO-24-106565.
Page 43 GAO-24-105833 Federal Fraud Estimate

OMB summarily concludes that interest on public debt and other large 

portions of federal outlays are at low or no risk of fraud and generally 

excludes them in their calculation of a fraud rate. Notwithstanding the fact 

that fraud can add to the federal debt, which directly increases interest 

payments on that debt, OMB’s argument to exclude large portions of 

federal outlays from the fraud rate is not supported by evidence. It is not 

based on actual data, backed by the extensive literature on fraud, or 

methodologically and statistically grounded. While we recognize that 

some programs are at a lower risk of fraud, all federal programs and 

operations are at risk of fraud. Some individuals or groups will seek to 

gain through fraud when and wherever there is opportunity. 

We acknowledge risk varies across programs and environments; that 

variation is reflected in our estimated range of fraud. OMB’s focus on the 

upper end of the range when considering normal risk environments leads 

to a distorted view of the reasonableness of our estimate. We note in the 

report that higher-risk environments, such as we observed with pandemic 

spending, are associated with estimates on the higher end of the range. 

Lower risk environments are associated with estimates on the lower end 

of the range. 

As discussed in our report, particularly in our methodological appendix, 

our analysis was based on fraud frequency and loss data, a review of 

relevant literature, interviews with subject matter experts, and our 

extensive knowledge of program fraud across the federal government. 

We also met with knowledgeable agency and OIG officials and conducted 

extensive work to analyze, select, and use data for our model. After 

running our simulations, we took multiple steps to assess the 

reasonableness of our results before finalizing our estimated range. Our 

results were further reviewed by the selected agencies and their OIGs. As 

such, our results provide a reasonable estimate of government-wide fraud 

loss based on the data collected in the timeframe of our review. 

Further, as our report states and OMB acknowledges in its comments, 

our model was developed to estimate government-wide federal fraud 

losses, and its dollar range and percent should not be applied to the 

agency, program, or operation level. It is, therefore, inappropriate for 

OMB to attempt to apply a percentage based on our estimated range of 

fraud losses to subsets of programs or outlays.

OMB also commented that we declined to share our methodology or 

show the specific programs and assumptions that informed our model. 

We disagree. Our report includes a detailed appendix that outlines our 
Page 44 GAO-24-105833 Federal Fraud Estimate

objectives, scope, and methodology. It also describes key assumptions 

used in our estimate of government-wide fraud. Further, we met with 

OMB staff throughout our engagement and multiple times after providing 

our draft report for review and comment to discuss the details of our 

report and methodology. 

OMB also noted that our reliance on a simulation model to produce what 

it described as unrealistic estimates was concerning given our reporting in 

this and prior reports regarding the availability of data for fraud estimation. 

OMB referenced a 2023 report we issued on the challenges associated 

with determining the total extent of fraud.59 That report summarizes many 

of the same issues we discuss in this report, including the limitations with 

existing data. 

OMB’s comments take our prior work out of context and do not recognize 

the substantial data collection, collating, analysis, and expertise that we 

used to develop this estimated range of fraud loss. First, we developed an 

estimate of fraud, as opposed to a measure of fraud. An estimate, which 

is a projection or inference based on fraud or fraud-related measures, 

assumptions, or analytical techniques, was necessary because direct 

measures of fraud are incomplete or unreliable. We determined that 

available data were sufficiently available and reliable to develop an 

estimated range of fraud loss across the federal government. Throughout 

our report, we acknowledge the inherent challenges with estimating 

federal fraud losses. Our recommendations are intended to improve the 

data available so that more granular estimates can be developed to help 

the government strategically manage fraud risk in the future.

Second, as we detail in this report, to estimate the range of total direct 

annual financial losses from fraud, we assessed different methods that 

could be used to estimate fraud. Given available data and our 

government-wide scope, we selected a Monte Carlo simulation to develop 

our estimate. A Monte Carlo simulation is a well-established method that 

can be used to estimate ranges for events where there is a high degree of 

uncertainty or limited data. OMB’s guidance on regulatory analysis notes 

Monte Carlo simulations as an analytic approach to account for 

uncertainty.60

59GAO-23-106110. 

60Office of Management and Budget, Circular No. A-4, Regulatory Analysis, (Washington, 

D.C.: Nov. 9, 2023). 
Page 45 GAO-24-105833 Federal Fraud Estimate

We therefore maintain that our methodology, including its assumptions 

and given its limitations as disclosed in the report, was sound and 

appropriate. Therefore, our estimated range of annual losses due to 

fraud, based on fiscal year 2018 through 2022 data, is realistic and 

reflects various risk environments during that period. 

In email communication, CIGIE indicated that it appreciated our work and 

our statement that federal agencies need robust processes in place to 

prevent, detect, and respond to fraud. CIGIE also stated that it would 

work with OMB in fiscal year 2024 to consider how the federal inspector 

general community might improve the availability of fraud-related data to 

expand government-wide fraud estimation and support fraud risk 

management, which relates to our second recommendation. 

In email communication, the Department of the Treasury indicated that it 

concurred with our third recommendation that it establish an effort to 

evaluate and identify methods to expand government-wide fraud 

estimation to support fraud risk management. The Department of the 

Treasury also provided technical comments that we incorporated into the 

report, as appropriate.

We are sending copies of this report to the appropriate congressional 

committees, the Director of the Office of Management and Budget, the 

Executive Director of the Council of the Inspectors General on Integrity 

and Efficiency, the Secretary of the Treasury, and other interested 

parties. In addition, the report is available at no charge on the GAO 

website at https://www.gao.gov.
Page 46 GAO-24-105833 Federal Fraud Estimate

If you or your staff have any questions about this report, please contact 

Rebecca Shea, (202) 512-6722, SheaR@gao.gov or Jared Smith, (202) 

512-2700, SmithJB@gao.gov. Contact points for our Offices of 

Congressional Relations and Public Affairs may be found on the last page 

of this report. GAO staff who made key contributions to this report are 

listed in appendix IV.

Rebecca Shea

Director, Forensic Audits and Investigative Service

Jared B. Smith

Director, Applied Research and Methods
Page 47 GAO-24-105833 Federal Fraud Estimate

List of Committees

The Honorable Gary C. Peters

Chairman

The Honorable Rand Paul, M.D.

Ranking Member

Committee on Homeland Security and Governmental Affairs

United States Senate

The Honorable James Comer

Chairman

The Honorable Jamie Raskin

Ranking Member

Committee on Oversight and Accountability

House of Representatives

The Honorable Pete Sessions

Chairman

The Honorable Kweisi Mfume

Ranking Member

Subcommittee on Government Operations and the Federal Workforce

Committee on Oversight and Accountability

House of Representatives
Appendix I: Objectives, Scope, and 

Methodology

Page 48 GAO-24-105833 Federal Fraud Estimate

This report discusses (1) our estimate of the range of total direct annual 

financial losses from fraud affecting federal programs and operations and 

(2) opportunities and challenges in fraud estimation to support fraud risk 

management. We performed this work under the Comptroller General’s 

authority to conduct evaluations to address the broad interest of 

Congress on the extent of fraud affecting the federal government.

To inform both objectives, we interviewed agency and Offices of Inspector 

General (OIG) officials from 12 agencies. We primarily interviewed 

officials responsible for program integrity, criminal investigations, 

investigative data collection, and audits. These agencies were selected 

based on obligation levels for fiscal years 2018 through 2022 and include 

those agencies with the top 10 obligations for one of the fiscal years. We 

made these selections using budget obligation data available through the 

Office of Management and Budget’s (OMB) MAX A-11 Data Entry 

Information System (MAX).1 Combined, the 12 agencies represent 

approximately 90 percent of all government obligations from fiscal years 

2018 through 2022. The 12 selected federal agencies are the 

Departments of Homeland Security, Defense, Labor, Transportation, 

Education, Agriculture, the Treasury, Health and Human Services, and 

Veterans Affairs, Office of Personnel Management, Small Business 

Administration, and Social Security Administration. We also reviewed 

relevant federal government requirements for existing fraud-related data 

and reporting.

In addition, we interviewed Department of Justice officials, including from 

the Executive Office for United States Attorneys; Criminal Division; Civil 

Division; Bureau of Justice Statistics; and the Office of Audit, 

Assessment, and Management, among others. We also met with officials 

from the Council of the Inspectors General on Integrity and Efficiency 

(CIGIE), Pandemic Response Accountability Committee, the Internal 

Revenue Service – Criminal Investigation, and the Congressional 

Research Service. 

1The Office of Management and Budget’s MAX A-11 Data Entry System (MAX) is a 

government-wide system used to share information and services among government 

agencies and to collect and process most of the information required for preparing the 

President’s Budget of the federal government. 

Appendix I: Objectives, Scope, and 

Methodology
Appendix I: Objectives, Scope, and 

Methodology

Page 49 GAO-24-105833 Federal Fraud Estimate

We also identified and reviewed fraud measurement and estimation 

studies to inform both objectives.2 Specifically, we identified and reviewed 

fraud measurement and estimation studies developed by U.S. 

government, international, academic, and others with subject-matter 

expertise. We generally focused on the studies published between fiscal 

years 2013 and 2022. In total, on the basis of web and literature database 

research, we considered 46 studies that were relevant for our review. We 

used these studies to assess estimation and measurement 

methodologies, the amount of fraud estimated or measured, and 

challenges in estimating and measuring fraud. As appropriate, we also 

used the studies to assess the reasonableness of our fraud estimate. Our 

estimate was in line with fraud estimates and analysis developed by other 

governments, as well as relevant nongovernmental organizations with 

fraud expertise.

We also interviewed selected fraud experts to gather additional 

information related to fraud measurement and estimation. We selected 

these individuals based on their wide-ranging knowledge of fraud in the 

international, academic, or private sector. These experts included those 

from the Association of Certified Fraud Examiners; the Centre for 

Cybercrime and Economics Crime at the School of Criminology and 

Criminal Justice at the University of Portsmouth in the United Kingdom; 

and the creator of the Corruption, Crime, and Compliance blog.

Information about the extent of fraud across the federal government is 

limited. Various data are available about fraud that has been investigated 

and adjudicated, but this information does not provide a complete 

understanding of the extent of fraud. We considered several factors in 

determining our methodology for estimating fraud, including our 

knowledge of likely available data, prior fraud estimation methods, and 

the known challenges of estimating fraud.

In considering prior methods used to estimate fraud, we found that these 

methods generally fell into the following three categories:

• studies whereby a group of knowledgeable individuals is surveyed to 

determine what they think the likely amount or rate of fraud is. 

2For the purposes of this report, we define fraud “measure” as a count of detected fraud or 

fraud-related activities. We define fraud “estimate” as a projection or inference based on 

fraud or fraud-related measures, assumptions, or analytical techniques, where direct 

measures are incomplete or unreliable. 

Estimate of the 

Range of Total Direct 

Annual Financial 

Losses from Fraud 

Affecting Federal 

Programs and 

Operations
Appendix I: Objectives, Scope, and 

Methodology

Page 50 GAO-24-105833 Federal Fraud Estimate

Because these surveys rely on people’s perceptions, they can be 

biased, depending on the views of those surveyed;

• the use of a statistically valid sample of transactions and detailed 

analysis to attempt to identify fraud that occurred in those transactions 

in order to extrapolate to the population of transactions. For example, 

in 2023, we relied on statistical sampling to develop an estimate of 

unemployment insurance fraud.3 Given the scope of government 

activities and transactions, this approach was not feasible for our 

work. Moreover, given the hidden nature of fraud, even a detailed 

analysis can still miss instances of potential fraud, which would bias 

the results to indicate less fraud is present; and

• the use of data analytics to identify transactions with evidence of 

potential fraud. These analyses are typically not based on statistical 

samples for the purpose of extrapolation. For example, some 

agencies have collected program data that have been used to detect 

potential fraud, such as through data matching, data mining, and 

network analysis.4 This type of information can serve as inputs to 

develop broader models on the extent of fraud in a program area or 

the risk of fraud in individual transactions. However, aggregate 

government-wide program-level data and program-specific details 

needed to pursue this approach were not available.

Due to the limited applicability of these methods for the purpose of 

producing a broader government estimate, we developed a novel method 

that relied on Monte Carlo simulation to extend the available data to 

estimate fraud. Monte Carlo simulation is a well-established probabilistic 

method for estimating a range of outcomes under different assumptions 

and scenarios where there is uncertainty. We chose this method because 

it provided a structured approach to account for the available data and 

addressed the multiple, substantial sources of uncertainty associated with 

the application of those data to fraud estimation.5 The approach has 

similarities to the three different approaches noted above, in that it 

involves obtaining information from experts in the field, analysis of 

3GAO, Unemployment Insurance: Estimated Amount of Fraud during Pandemic Likely 

Between $100 Billion and $135 Billion, GAO-23-106696 (Washington, D.C.: Sept 12, 

2023).

4Network analysis is a quantitative approach to identifying and graphically representing 

potentially unknown relationships among individuals or organizations. 

5The general purpose of a Monte Carlo simulation is to capture the uncertainty in a 

process by randomly generating a range of values consistent with that process. Monte 

Carlo simulations can be useful for understanding the range of potential outcomes that 

can arise under different assumptions and scenarios.
Appendix I: Objectives, Scope, and 

Methodology

Page 51 GAO-24-105833 Federal Fraud Estimate

existing datasets, and the extension of available information to an 

unobserved population.

We used the simulation to estimate a range of the total direct annual 

financial losses from fraud. The simulation was developed primarily using 

data from fiscal years 2018 to 2022, which was organized into three fraud 

categories further described below. Our approach is sensitive to the 

assumptions made about fraud and accounts for data uncertainty and 

limitations. The data available and used in our simulation had various 

limitations and assumptions that impact our estimate. The insights offered 

by simulations should be interpreted carefully. While we used an 

analytical approach to account for the inherent uncertainties associated 

with fraud estimation and data limitations, the actual amount of direct 

annual financial losses resulting from fraud affecting federal programs 

and operations could be outside of the range of our estimate.

Based on our understanding of fraud, investigations, available data, and 

the findings of other fraud measurement and estimation studies, we 

identified three fraud categories—adjudicated fraud, detected potential 

fraud, and undetected potential fraud. Within detected potential fraud, we 

developed three subcategories. See figure 10 for additional information 

on these categories and subcategories.

Figure 10: Fraud Categories: Adjudicated, Detected Potential, and Undetected Potential

Fraud Categories
Appendix I: Objectives, Scope, and 

Methodology

Page 52 GAO-24-105833 Federal Fraud Estimate

These categories reflect different degrees of certainty about the possibility 

of fraud. For example, an instance of adjudicated fraud is certainly 

fraudulent. In contrast, the certainty of fraud may be substantially lower 

for an instance of potential fraud that has been detected and not accepted 

for investigation.

We shared these categories and subcategories with OIG officials from the 

12 selected agencies, CIGIE, the Pandemic Response Accountability 

Committee, and the Department of Justice to obtain their feedback on the 

appropriateness of the categories. Generally, these officials agreed with 

our categories and subcategories. As appropriate, we made clarifications 

or revisions based on their input.

Using these three fraud categories, we collected and analyzed data from 

three key sources—OIG investigative systems, OIG semiannual reports, 

and OMB’s Paymentaccuracy.gov reporting. Data on undetected fraud, 

by their nature, do not exist. For the undetected fraud category, we relied 

on fraud studies for model inputs and assumptions. See figure 11 for the 

information and data sources used for each fraud category.

Data Collection and 

Analysis
Appendix I: Objectives, Scope, and 

Methodology

Page 53 GAO-24-105833 Federal Fraud Estimate

Figure 11: Information and Data Sources for the Simulation, by Fraud Category 

Individually, these data do not provide a comprehensive understanding of 

the extent of fraud but, when analyzed collectively and in our simulation, 

provide support for a government-wide estimate.

We generally collected data from each source for fiscal years 2018 

through 2022. The data collected have strengths and limitations that 

impact our estimate. For example, investigative data related to potential 

and adjudicated fraud were generally available from all 12 selected 

agencies, but there was variance in the terms used across the sources, 

and not all agencies had information for each subcategory of potential 

fraud. We considered these strengths and limitations in determining the 

data to collect and used information from fraud estimation studies 

developed by government, international, academic, and others with 

subject-matter expertise to adjust simulation assumptions and parameters 

to account for data limitations. Specifically, data sources for our 
Appendix I: Objectives, Scope, and 

Methodology

Page 54 GAO-24-105833 Federal Fraud Estimate

simulation and their related limitations include those related to 

investigative data, OIG semi-annual reports, and confirmed fraud 

amounts reports by agencies to OMB.

Investigative data. We requested data from the OIGs for the 12 selected 

agencies using a data collection instrument.6 The instrument requested 

OIG information organized into the fraud categories and subcategories, 

which we had previously discussed with the selected OIGs. The 

instrument requested information that was known to OIGs, such as 

hotline complaints and closed case information. We pretested the data 

collection instrument with three OIGs prior to collecting these data from all 

12 selected OIGs.

While we received information from all 12 of the selected agencies’ OIGs, 

the level of detail provided varied. Some OIGs were able to provide 

information for each of the data elements we requested. However, some 

did not maintain records in such a way that they could provide all 

information to us without extensive manual work. For example, some 

OIGs collected data at the case level, while others collected at the 

individual or entity level and were unable to consolidate at the case level. 

We addressed these differences in how our model analyzed the data.

We requested summary statistics and information for fiscal years 2018 

through 2022 for the following to inform the detected potential fraud 

category:

• number of allegations received in the fiscal year;

• number of allegations closed in the fiscal year, including those that 

were not converted to full investigations;

• number of investigations opened and closed, including details on 

whether the case was accepted for judicial, administrative, criminal, or 

civil action; and

• the dollar value of alleged direct federal financial losses for 

allegations, closed investigations, and judicial and administrative 

action.

6This information was often maintained in Office of Inspector General case management 

systems. 
Appendix I: Objectives, Scope, and 

Methodology

Page 55 GAO-24-105833 Federal Fraud Estimate

We also requested summary statistics and information for fiscal years 

2018 through 2022 for the following to inform the adjudicated fraud 

category:

• number of cases of adjudicated fraud by criminal, civil, or 

administrative case types; and

• the direct federal financial loss, by criminal, civil, or administrative 

case types.

We also requested additional summary statistics and information for fiscal 

years 2018 through 2022 on actions taken jointly with other OIGs.

We also requested that the OIGs

• describe any potential issues or limitation related to the data 

requested,

• provide their professional perspective on the portion of allegations 

from hotlines and other sources that are accepted for investigation or 

ultimately adjudicated as fraud, and

• describe any data system or other changes that might affect the 

information provided.

Finally, we requested data regarding the progress on actions taken for 

fiscal years 2013 through 2022 to reflect the extended investigative

timelines and the full extent of adjudicated and potential fraud in our 

simulation. These investigative data included information on the number 

of unique cases that included a potential financial loss related to judicial 

and administrative action and the status of certain closed and adjudicated 

cases for the extended period. We used these data to contextualize and 

validate the summary statistics collected for fiscal years 2018 through 

2022.

Office of Inspector General Semi-Annual Reports. OIGs are required

to report on their activities through semiannual reports.7 We collected and 

analyzed information reported in semiannual reports for the 12 selected 

agencies for fiscal years 2018 through 2022 to inform the detected 

potential fraud and adjudicated fraud categories. Specifically, we 

collected information on

7Under the Inspector General Act of 1978, as amended, federal inspectors general are 

required to submit semiannual reports to Congress describing the offices’ activities and 

accomplishments during that reporting period. 5 U.S.C. App. § 5.
Appendix I: Objectives, Scope, and 

Methodology

Page 56 GAO-24-105833 Federal Fraud Estimate

• investigative reports issued;

• cases referred to the Department of Justice for criminal prosecution;

• cases referred to state or local authorities for criminal prosecution;

• indictments, criminal informations, and convictions that resulted from 

prior referrals;8

• statistical summaries of investigative accomplishments; and

• hotline reporting statistics, and aggregated amounts of fraud.

We assigned data collected from the semiannual reports to the 

appropriate fraud category or subcategory.

Confirmed fraud. We collected and analyzed confirmed fraud data 

reported by agencies to OMB for fiscal years 2018 through 2022. 

Confirmed fraud data are reported to OMB by federal agencies and are 

available on OMB’s Paymentacuracy.gov.9 These data include our 12 

selected agencies and all other agencies that reported confirmed fraud 

amounts to OMB in our time period. According to OMB’s guidance for 

reporting, confirmed fraud

• is defined as the amount determined to be fraudulent through the 

adjudication process;

• does not include transactions determined by management to be 

anomalous or indicative of potential fraud that were referred to the 

agency’s OIG or the Department of Justice, unless the appropriate 

judicial or adjudicative process has made the determination; and

• does not represent anything settled out of court with or without 

admission of guilt.10

While confirmed fraud provides a direct measure of fraud, it represents 

only a portion of the total amount of fraud impacting the federal 

8A criminal information is a written accusation made by a public prosecutor, without the 

intervention of a grand jury. On the other hand, an indictment is a formal written 

accusation originating with a prosecutor and issued by a grand jury against a party 

charged with a crime.

9OMB requires agencies to provide certain information about improper payments and 

confirmed fraud. OMB publishes this information in a payment-integrity dashboard on 

Paymentaccuracy.gov.

10Office of Management and Budget, Transmittal of Appendix C to OMB Circular A-123, 

Requirements for Payment Integrity Improvement, M-21-19 (Washington, D.C.: Mar. 5, 

2021). 
Appendix I: Objectives, Scope, and 

Methodology

Page 57 GAO-24-105833 Federal Fraud Estimate

government. Confirmed fraud also does not include cases that never 

resulted in a conviction or a formal admission of wrongdoing. For these 

reasons, we considered other sources of information about fraud in our 

model, and we estimated undetected fraud, as well.

To determine the reliability of the investigative data, confirmed fraud data, 

and OIG semiannual report data, we met with knowledgeable agency and 

OIG officials and reviewed relevant documentation to identify any relevant 

data limitations that could impact our simulation. We also manually 

reviewed the data to verify the quality and completeness of the data. On 

the basis of the investigative data, confirmed fraud, and OIG semiannual 

report data collected, we assessed each specific dollar amount and count 

to identify the data for inclusion in the simulation. Specifically, we selected 

those data that offered the most credible and complete information for our 

fraud data categories. We concluded that these data were sufficiently 

reliable to inform the development of assumptions and parameters in our 

Monte Carlo analysis and to serve as inputs into that estimate.

In addition to fraud-related information sources, we also used obligation 

data available through OMB MAX. We used average obligations for fiscal 

years 2018 to 2022 to account for the size, in dollar amount, for agency 

operations in our simulation. We obtained these data for our 12 selected 

agencies and all other agencies.

To determine the reliability of the obligation data from OMB MAX, we 

reviewed relevant documentation on the data and conducted electronic 

testing. We concluded that these data were sufficiently reliable to 

determine the total obligations for each agency and as an input into our 

Monte Carlo analysis.

On the basis of the fraud categories, data sources, discussions with 

officials from the 12 selected agencies and their OIGs, and other experts, 

we developed a simulation framework to account for three limitations 

common in efforts to measure or estimate fraud.11 These are limitations 

related to the fact that

• not all fraud is adjudicated,

• not all fraud is detected, and

11We use the terms “model” and “simulation framework” both to refer to the general 

approach that we used to estimate the extent of fraud in the federal government. We use 

the term “simulation” to describe the implementation of the estimation approach.

Simulation Framework, 

Assumptions, and 

Limitations
Appendix I: Objectives, Scope, and 

Methodology

Page 58 GAO-24-105833 Federal Fraud Estimate

• measures and estimates based on potential fraud likely include some 

nonfraudulent activities.

Assumptions related to the nature of fraud. The foundation for our

simulation framework is a set of assumptions about the nature of fraud 

and how information in the different fraud categories relate to each other. 

For example, because of the hidden nature of fraud, we cannot observe 

or detect the complete set of fraudulent activities and associated financial 

loss—some portion of fraud and related financial loss will remain 

undetected. In addition, the process of investigating and adjudicating 

fraud can be costly. As a result, even if an agency or OIG is aware of 

potential financial losses from detected potential fraud, some portion of 

potential fraud may never be investigated or adjudicated. The statute of 

limitations could also impact the OIG’s ability to investigate some 

detected potential fraud. For example, if the statute of limitations were 

expiring before investigative activity could likely be completed, the case 

may be deprioritized. Relatedly, the average dollar amounts associated 

with cases of adjudicated fraud are likely higher than the average 

amounts associated with cases of detected potential fraud, which, in turn, 

would be greater than the average amounts associated with undetected 

fraud.

Given the above considerations, we identified the following baseline 

assumptions about the nature and characteristics of fraud. To the extent 

we could confirm the accuracy of these assumptions during our work, we 

did so.12 Specifically, we assumed that

• the total financial loss amount of fraud can be reasonably modeled 

using the number of occurrences of fraud and potential fraud, the 

financial loss amount associated with each occurrence, the size of 

agency operations, the characteristics of the agency, and the 

characteristics of fraud;

• on average, the financial loss amount associated with each 

adjudicated occurrence of fraud will be higher than the loss amount 

associated with each occurrence of detected potential fraud. We 

found that this expectation generally held in our observed data. We 

applied this assumption when simulating fraud categories with missing 

data;

12We describe the specific implementation of these assumptions, along with the 

distributions underlying Monte Carlo simulation, in the simulation procedures below. 
Appendix I: Objectives, Scope, and 

Methodology

Page 59 GAO-24-105833 Federal Fraud Estimate

• on average, the financial loss amount associated with each 

occurrence of undetected potential fraud will be lower than the loss 

amount associated with each occurrence of detected potential fraud. 

We assumed this relationship held when simulating fraud information 

categories with missing data. If this assumption does not hold, then 

the simulation may underestimate the potential loss from undetected 

fraud;

• on average, the number of occurrences of adjudicated fraud will be 

smaller than the number of occurrences of detected potential fraud. 

We found that this expectation generally held in our observed data; 

and

• the number of occurrences of undetected potential fraud will be closer 

in scale to the number of occurrences of detected potential fraud than 

to adjudicated fraud. If this assumption does not hold, then the 

simulation may under- or overestimate the potential loss from 

undetected fraud. To reduce the risk associated with this assumption, 

we performed model sensitivity checks that included different potential 

relationships between the number of occurrences of undetected 

potential fraud and detected potential fraud described in more detail 

later in this section.

Assumptions related to fraud occurrence and loss. In addition to the 

baseline assumptions about the nature and characteristics of fraud, we 

also applied various assumptions throughout the simulation process. 

Three additional assumptions related to fraud occurrence, and two related 

to fraud loss are described below.

• Assumptions applied to agencies. We made assumptions about the 

relationship between fraud at the 12 selected agencies and all other 

federal agencies. We had total obligations and OMB confirmed fraud 

data for all federal agencies as inputs to the simulation. We used data 

collected for the 12 selected agencies to inform our simulation of the 

number of fraud occurrences at other agencies. We took this 

approach, given that our agency selection included about 90 percent 

of agency obligations and also reflected a variety of different 

programs and operations. Collectively, for these other agencies, we 

assumed that the relationship between the number of fraud 

occurrences and financial loss associated with each individual 

occurrence of fraud was similar to the relationships observed at the 

selected agencies.

• Assumptions related to the occurrence of fraud. We assumed that the 

number of fraud occurrences for each agency is uniformly distributed 

between the minimum value and maximum value we identified from 
Appendix I: Objectives, Scope, and 

Methodology

Page 60 GAO-24-105833 Federal Fraud Estimate

the data collection process.13 For undetected potential fraud 

simulations, we assumed that the number of undetected potential 

fraud occurrences is the sum of the fraud occurrences in the three 

stages of detected potential fraud.14

• Assumptions about financial loss associated with individual 

occurrences of fraud. We assumed that the range of financial losses 

attributable to individual occurrences of fraud could be reasonably 

captured using a uniform distribution. This is described in greater 

detail below. To add variability to our estimate, we randomly 

generated financial amounts from a uniform distribution with a 

minimum and maximum defined by the amounts we observed across 

the 5 years of data for each agency.15 In cases where agencies did 

not provide any dollar amount associated with detected potential 

fraud, we follow the previously described assumption that, on 

average, the financial loss associated with each adjudicated 

occurrence of fraud would be higher than the loss associated with 

each occurrence of detected potential fraud, and we adjusted the 

financial loss amount based on the financial loss amounts observed in 

the previous steps of the simulation. The specific adjustments are 

provided in the simulation description.16

• Assumptions related to financial recoveries and fraud loss. Financial 

recoveries include assets or funds regained or ordered restored 

through a judicial or administrative action after it was determined that 

the funds or assets were lost, misappropriated, stolen, or misused. 

Financial recoveries may, but do not always, equate to the direct 

financial loss from fraud determined through the adjudicative process. 

In some instances, financial recoveries may include the recovery of 

13We also applied different simulations based on the assumptions that the number of 

fraud occurrences for each agency is uniformly distributed between two (one) standard 

deviations below the average and two (one) standard deviations above the average of the 

data. 

14We also applied different simulations based on the assumption that the number of 

undetected potential fraud occurrences is the same as the amount in stage 1 of detected 

potential fraud. 

15We also applied different simulations based on the assumption that the number of fraud 

occurrences for each agency is uniformly distributed between two standard deviations 

below the average and two standard deviations above the average of the calculated 

financial loss amount associated with each occurrence of fraud. As another approach, we 

repeated this design using one, rather than two, standard deviations.

16We also applied different simulations based on the assumptions that the size of financial 

loss amount factors is uniformly distributed between zero and the average of results of 

simulated detected potential fraud stage 1. 
Appendix I: Objectives, Scope, and 

Methodology

Page 61 GAO-24-105833 Federal Fraud Estimate

another agency’s financial loss from fraud, if the fraud was 

perpetuated against multiple agencies. In this situation, if amounts are 

reported by both agencies, then the financial recoveries would 

overstate the financial loss from fraud across the agencies. 

Conversely, recoveries may be less than the direct financial loss from 

fraud; for example, when a restitution order is less than the actual 

funds lost due to fraud.17 In this situation, recoveries would 

underestimate fraud.

• Assumptions related to OIG investigations of fraud relative to other 

crimes. OIGs track fraud and nonfraud cases in their case 

management systems. However, their systems do not always 

distinguish fraud cases from other crimes. For example, OIGs conduct 

investigations not related to fraud, such as employee misconduct. We 

adjusted the dollar losses associated with cases downward to adjust 

for this issue. Without this adjustment, our simulation would 

overestimate fraud for this data source.

The simulation involved a series of iterative steps. The result of each 

complete simulation step was an estimate of fraud. We ran the simulation 

1,000 times, which produced 1,000 estimates. These estimates were then 

used to formulate our range of estimated fraud affecting the federal 

government.

Each iteration of the simulation included the following steps, repeated for 

each fraud category.

1. Using data on adjudicated fraud, we

a. calculated a range for the annual number of occurrences of 

adjudicated fraud for each agency.18 We selected the number of 

occurrences randomly from this range.

17In federal court, a convicted offender may be ordered to reimburse victims for financial 

losses incurred due to the offender’s crime. This reimbursement is called “restitution,” and 

it may be ordered for lost income, property damage, counseling, medical expenses, 

funeral costs, or other financial costs directly related to the crime.

18In this context of this discussion, the term “agency” refers to each of the 12 selected 

agencies and the group that contained all other federal agencies. For the federal agencies 

that were not among the 12 selected and for which data were collected, we estimated their 

fraud using OMB confirmed fraud amounts, obligations, and fraud data from the 12 

selected agencies.

Simulation Procedures
Appendix I: Objectives, Scope, and 

Methodology

Page 62 GAO-24-105833 Federal Fraud Estimate

b. calculated a range for the financial loss associated with each 

occurrence of adjudicated fraud for each agency. We randomly 

generated values from this range to simulate the losses 

associated with each potential occurrence of adjudicated fraud. 

For example, if we simulated that there were 100 occurrences of 

adjudicated fraud, then we would simulate 100 financial losses, 

one for each occurrence; and

c. combined the financial losses that we simulated for each 

occurrence and scaled the results based on agency obligations.19

2. We repeated steps a) through c) using data from the detected 

potential fraud category—stage 3. These data include remedies that 

were obtained using due process but where guilt, liability, or fault of 

fraud were not formally determined.

If financial information about such agency remedies did not exist for 

an agency, then we developed a range for the financial loss amount 

using the average simulated amounts for adjudicated fraud. In these 

cases, financial loss amounts were randomly drawn from a range 

defined to be between 50 percent and 150 percent of the mean value 

of the adjudicated fraud.

3. We repeated steps a) through c) using data from the detected 

potential fraud category—stage 2. These data include information 

about investigative inquiries into the facts of a case but where 

referrals have not been accepted for judicial or administrative actions.

If financial information about such investigative inquiries did not exist 

for an agency, then we developed a range for the financial loss 

associated with each individual fraud occurrence using the average 

simulated loss amounts from the prior step (stage 3—remedies that 

were obtained using due process but where guilt, liability, or fault of 

fraud were not formally determined). In such cases, financial loss 

19We used obligations to reflect that fraud can occur, and is best prevented, prior to when 

there is an outlay of funding. An obligation is a definite commitment that creates a legal 

liability on the part of the federal government for the payment of goods and services 

ordered or received, or a legal duty on the part of the United States that could mature into 

a legal liability by virtue of actions on the part of the other party beyond the control of the 

United States. Payment may be made immediately or in the future. An outlay occurs upon 

the issuance of checks, disbursement of cash, or electronic transfer of funds made to 

liquidate a federal obligation.
Appendix I: Objectives, Scope, and 

Methodology

Page 63 GAO-24-105833 Federal Fraud Estimate

amounts were randomly drawn from a range defined to be between 

25 percent and 100 percent of the mean value of the prior step.20

4. We repeated steps a) through c) using data from the detected 

potential fraud category—stage 1. These data include information 

about potential fraud detected by the federal government but not 

accepted for full investigation.

If financial information about such potential fraud did not exist for an 

agency, then we used the average simulated amounts from our prior 

step (stage 2—investigative inquiries into the facts of a case but 

where referrals have not been accepted for judicial or administrative 

action). In such cases, financial loss amounts were randomly drawn 

from a range defined to be between 0 percent and 100 percent of the 

mean value of the prior step. In addition, for 0 percent to 50 percent of 

the occurrences, we assumed no financial loss to account for 

uncertainty associated with false positives in fraud reporting.

5. We tested 12 different simulations for undetected fraud, each based 

on a different model of the relationship between undetected and 

detected fraud. For example, we ran a simulation that assumed that 

the number of occurrences of undetected fraud was equal to the 

number of occurrences of detected fraud. We also ran a simulation 

that assumed that the average financial loss amount associated with 

undetected fraud was similar to the average amount associated with 

cases detected by the federal government but not accepted for full 

investigation. These different undetected fraud models produced 

generally consistent simulation results of the total amount of financial 

loss from fraud.

6. We combined the interim simulation outputs that were generated from 

each step to develop the government-wide estimated fraud range. 

The range was calculated as the 5th percentile and 95th percentile of 

the 1,000 simulated runs. Because our simulation is constructed from 

the data of fraud occurrence and associated financial information 

measured on an annual basis, our simulation results are also annual 

financial loss estimates of the categories affecting federal programs 

and operations.

20The size of the adjustment depended on the agency. For agencies without financial 

information for the amounts associated with remedies that were obtained using due 

process but where guilt, liability, or fault of fraud were not formally determined, financial 

loss amounts were randomly drawn from a range defined to be between 50 percent and 

150 percent of the mean value. The size of the adjustments was calculated by testing the 

simulation procedures on agencies with information regarding all categories of fraud 

information.
Appendix I: Objectives, Scope, and 

Methodology

Page 64 GAO-24-105833 Federal Fraud Estimate

To assess the reasonableness of the simulation results, we took several 

steps. First, we compared interim simulation data calculated for each of 

the 12 selected agencies against information we had collected about 

fraud at those organizations. This information included 46 estimations and 

measurement studies from a variety of sources, including the OIGs for the 

Small Business Administration and the Department of Labor. In addition, 

we reviewed information that agencies reported on actions taken to 

combat fraud and obtain financial recoveries.

If the interim simulation data for an agency differed materially from 

available information, we adjusted the relevant simulation parameters to 

improve consistency. These adjustments were applied at the fraud 

category level. For example, the Department of Justice and the 

Department of Health and Human Services reported that the federal 

government won or negotiated more than $5 billion in health care fraud 

judgments and settlements in fiscal year 2021.21 On the basis of the 

findings in this report, we adjusted the simulation parameters for the 

Department of Health and Human Services to assure that interim 

simulation data for detected fraud did not conflict with the $5 billion.22

Given that the adjustments were made at the fraud category level, an 

agency might have no adjustment for the portion of the simulation 

associated with adjudicated fraud weights but an adjustment for the 

portion of the simulation associated with stage 1 detected potential fraud.

In addition, we ran separate simulations with alternative assumptions to 

assess the reasonableness of our results. As described previously, we 

considered multiple approaches to estimating undetected potential fraud. 

Similarly, we considered different approaches in the simulations based on 

the certainty in the categories of fraud (i.e., the adjudicated fraud category 

has higher certainty than the detected potential fraud in any stage). We 

also considered additional ranges for the simulated financial amounts 

21Department of Justice and Department of Health and Human Services, Annual Report of 

the Departments of Health and Human Services and Justice, Health Care Fraud and 

Abuse Control Program FY 2021 (July 2022). 

22The $5 billion in health care fraud was used to adjust fraud weights for detected fraud, 

which includes adjudicated fraud, investigations, and fraud that has been flagged by the 

government regardless of whether it has been accepted for investigation. We expected 

that the fraud amounts associated with the broader category of detected fraud would be 

greater than the amounts associated with civil settlements. To account for uncertainty in 

this relationship, we set $5 billion as the 10th percentile for interim simulation values 

associated with Health and Human Services detected fraud. This approach allowed for the 

possibility that the total amount of detected fraud could be lower than $5 billion for any 

given simulated year. 

Assessing the 

Reasonableness of 

Simulation Results
Appendix I: Objectives, Scope, and 

Methodology

Page 65 GAO-24-105833 Federal Fraud Estimate

associated with each fraud category. The purpose of this additional 

testing was to assess the sensitivity of our reported range to the 

assumptions underlying our simulation. Sensitivity was determined by 

examining how much our estimate changed, given the changes to the 

structure of our simulation. We found our range to be reasonably 

consistent, given the approaches that we tested.

The insight offered by simulations should be interpreted carefully. Our 

approach was not designed to provide precise predictions. Instead, it was 

meant to extend the current understanding about the likely extent of fraud 

in the federal government, given available data.

Our methodology results in two primary sources of uncertainty. The first 

source of uncertainty is the statistical uncertainty arising from the 

randomness of the simulation process. We intentionally included this 

randomness to help capture the uncertainty associated with the selected 

fraud information categories. For example, our simulation has parameters 

that account for uncertainty in the number of occurrences of fraud and the 

financial loss associated with each occurrence of fraud. The statistical 

uncertainty associated with our estimate is reflected in our reported 

range.

In addition to statistical uncertainty, our approach is affected by the 

uncertainty associated with the specification of our underlying model and 

the reliability of the underlying data. The results of our simulations depend 

on key assumptions we made about how the historical data might 

correspond to adjudicated fraud, detected potential fraud, and undetected 

potential fraud.

We believe our assumptions are reasonable, given our historical data and 

the degree of uncertainty involved. However, our estimates should not be 

generated to specific past or future results. In part, this is because 

resulting overall potential annual financial loss of fraud would ultimately 

depend on how federal agencies manage their specific fraud risks, and 

other factors, which we did not attempt to model.

If one or more of our assumptions, as implemented in our simulation, are 

incorrect, then we face an increased risk that our range will not capture 

the actual extent of fraud. This risk is especially high with the undetected 

fraud category. As described in the previous section, we attempted to 

mitigate this risk by checking if our results were reasonably consistent,

given changes to key assumptions underlying our simulation.

Simulation Interpretation 

and Uncertainty
Appendix I: Objectives, Scope, and 

Methodology

Page 66 GAO-24-105833 Federal Fraud Estimate

In addition, we reviewed published information about government and 

agency-specific fraud rates to ensure that our fraud estimate was in line 

with these alternative sources. These included fraud estimation or 

analysis efforts performed by the federal government, non-U.S. 

governments, and relevant nongovernmental organizations with fraud 

expertise. These studies include:

• the United Kingdom Public Sector Fraud Authority, which estimates 

fraud and error losses between 0.5 and 5 percent of government 

expenditures in 2020; 

• the Association of Certified Fraud Examiners that estimated in 2022 

that organizations lost about 5 percent of revenue to fraud each year; 

and

• the University of Portsmouth Centre for Counter Fraud Studies, which 

estimated fraud and error losses between 0.02 and 63.96 percent, 

with average losses of 6.42 percent between 1997 and 2020. 

Agencies differed in the methods and assumptions that they used to 

compile the data that we relied on to calculate our estimate. Our 

simulation results may be influenced by these differences. We worked to 

mitigate potential issues with our data sources by working closely with 

agencies and OIGs to better understand the limits of the underlying 

source data.

To identify opportunities and challenges in fraud estimation to support 

fraud risk management, we reviewed relevant agency and OIG 

documentation related to existing fraud-related measures, such as fraud 

estimation studies or documentation discussing existing measures. For 

example, we reviewed agency reports documenting estimation studies or 

how fraud measures were developed and any known caveats and 

limitations. We also reviewed OIG reports discussing known challenges 

with the data. We reviewed relevant requirements for existing fraudrelated data and reporting, including OIG semiannual reports, confirmed 

fraud reporting, and CIGIE annual reports to the President.

We evaluated the extent to which these data and information collection 

and use align with leading practices in GAO’s A Framework for Managing 

Fraud Risks in Federal Programs–specifically, leading practices related to 

assessing fraud risks and evaluating outcomes using a risk-based 

Opportunities and 

Challenges in Fraud 

Estimation to Support 

Fraud Risk 

Management
Appendix I: Objectives, Scope, and 

Methodology

Page 67 GAO-24-105833 Federal Fraud Estimate

approach.23 We also evaluated the extent to which existing fraud-related 

data and information collection and use align with the Standards for 

Internal Control in the Federal Government.24 Specifically, we determined 

that the information and communication component of internal control 

was significant to the objective, along with the underlying principles that 

management should use quality information to achieve the entity’s 

objectives.

We conducted this performance audit from February 2022 to April 2024 in 

accordance with generally accepted government auditing standards. 

Those standards require that we plan and perform the audit to obtain 

sufficient, appropriate evidence to provide a reasonable basis for our 

findings based on our audit objectives. We believe that the evidence 

obtained provides a reasonable basis for our findings based on our audit 

objectives.

23GAO, A Framework for Managing Fraud Risks in Federal Programs, GAO-15-593SP

(Washington, D.C.: July 28, 2015). 

24GAO, Standards for Internal Control in the Federal Government, GAO-14-704G

(Washington, D.C.: Sept. 10, 2014). 
Appendix II: Matters for Congressional 

Consideration

Page 68 GAO-24-105833 Federal Fraud Estimate

In a March 2022 testimony before the Senate Committee on Homeland 

Security and Governmental Affairs, we recommended 10 Matters for 

Congressional Consideration to strengthen internal controls and financial 

and fraud risk management practices across the government.1 As of 

March 2024, these matters remained open. 

• Congress should pass legislation requiring the Office of Management 

and Budget (OMB) to provide guidance for agencies to develop plans 

for internal control that would then immediately be ready for use in, or 

adaptation for, future emergencies or crises and requiring agencies to 

report these internal control plans to OMB and Congress. (Matter for 

Congressional Consideration 1)

• Congress should amend the Payment Integrity Information Act of 

2019 to designate all new federal programs making more than $100 

million in payments in any one fiscal year as “susceptible to 

significant improper payments” for their initial years of operation. 

(Matter for Congressional Consideration 2)

• Congress should amend the Payment Integrity Information Act of 

2019 to reinstate the requirement that agencies report on their 

antifraud controls and fraud risk management efforts in their annual 

financial reports. (Matter for Congressional Consideration 3)

• Congress should establish a permanent analytics center of 

excellence to aid the oversight community in identifying improper 

payments and fraud. (Matter for Congressional Consideration 4)

• Congress should clarify that (1) chief financial officers (CFO) at CFO 

Act agencies have oversight responsibility for internal controls over 

financial reporting and key financial management information that 

includes spending data and improper payment information; and (2) 

executive agency internal control assessment, reporting, and audit 

requirements for key financial management information, discussed in 

an existing Matter for Congressional Consideration in our August 

2020 report,2 include internal controls over spending data and 

improper payment information. (Matter for Congressional 

Consideration 5)

1GAO, Emergency Relief Funds: Significant Improvements Are Needed to Ensure 

Transparency and Accountability for COVID-19 and Beyond, GAO-22-105715

(Washington, D.C.: Mar. 17, 2022). 

2GAO, Federal Financial Management: Substantial Progress Made since Enactment of 

the 1990 CFO Act; Refinements Would Yield Added Benefits, GAO-20-566 (Washington, 

D.C.: Aug. 6, 2020). 

Appendix II: Matters for Congressional 

Consideration
Appendix II: Matters for Congressional 

Consideration

Page 69 GAO-24-105833 Federal Fraud Estimate

• Congress should require agency CFOs to (1) submit a statement in 

agencies’ annual financial reports certifying the reliability of improper 

payments risk assessments and the validity of improper payment 

estimates, and describing the actions of the CFO to monitor the 

development and implementation of any corrective action plans; and 

(2) approve any methodology that is not designed to produce a 

statistically valid estimate. (Matter for Congressional Consideration 6)

• Congress should consider legislation to require improper payment 

information required to be reported under the Payment Integrity 

Information Act of 2019 to be included in agencies’ annual financial 

reports. (Matter for Congressional Consideration 7)

• Congress should amend the DATA Act to extend the previous 

requirement for agency inspectors general to review the 

completeness, timeliness, quality, and accuracy of their respective 

agency data submissions on a periodic basis. (Matter for 

Congressional Consideration 8)

• Congress should amend the DATA Act to clarify the responsibilities 

and authorities of OMB and the Department of the Treasury for 

ensuring the quality of data available on USAspending.gov. (Matter 

for Congressional Consideration 9)

• Congress should amend the Social Security Act to accelerate and 

make permanent the requirement for the Social Security 

Administration to share its full death data with the Department of the 

Treasury’s Do Not Pay working system. (Matter for Congressional 

Consideration 10)
Appendix III: Comments from the Office of 

Management and Budget

Page 70 GAO-24-105833 Federal Fraud Estimate

Appendix III: Comments from the Office of 

Management and Budget
Appendix III: Comments from the Office of 

Management and Budget

Page 71 GAO-24-105833 Federal Fraud Estimate
Appendix III: Comments from the Office of 

Management and Budget

Page 72 GAO-24-105833 Federal Fraud Estimate
Appendix IV: GAO Contacts and Staff 

Acknowledgments

Page 73 GAO-24-105833 Federal Fraud Estimate

Rebecca Shea, (202) 512-6722 or SheaR@gao.gov

Jared B. Smith, (202) 512-2700, SmithJB@gao.gov 

In addition to the contacts named above, Heather Dunahoo, Moon Parks, 

Steven Putansu (Assistant Directors); Nick Weeks (Analyst in Charge); 

Tracy Abdo, Lisa Fisher, Amber D. Gray, Lijia Guo, and Abinash Mohanty 

made key contributions to this report. Also contributing to the report were 

Robert Bastian, Lilia Chaidez, Leia Dickerson, Colin Fallon, Carly 

McCann, Maria McMullen, Joe Rini, and Sabrina Streagle.

Appendix IV: GAO Contacts and Staff 

Acknowledgments

GAO Contacts

Staff 

Acknowledgments
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