GAO-23-105523: Unemployment Insurance: Data Indicate Substantial Levels of Fraud during the Pandemic; DOL Should Implement an Antifraud Strategy
Summary
GAO report GAO-23-105523, "Unemployment Insurance: Data Indicate Substantial Levels of Fraud during the Pandemic; DOL Should Implement an Antifraud Strategy," a report to congressional requesters dated December 2022. It states that Congress created four new UI programs during the pandemic and that, according to DOL data, approximately $878 billion in benefits were paid across all UI programs from April 2020 through September 2022. GAO finds that measures and estimates considered together indicate substantial levels of fraud and potential fraud, though none completely and reliably shows its extent. It reports that as of December 2022 DOL had not developed an antifraud strategy based on GAO's Fraud Risk Framework, recommends that DOL do so, and states that DOL partially agreed.
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“GAO-23-105523: Unemployment Insurance: Data Indicate Substantial Levels of Fraud during the Pandemic; DOL Should Implement an Antifraud Strategy.”
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United States Government Accountability Office
Report to Congressional Requesters
UNEMPLOYMENT
December 2022
INSURANCE
Data Indicate
Substantial Levels of
Fraud during the
Pandemic; DOL
Should Implement an
Antifraud Strategy
GAO-23-105523
December 2022
UNEMPLOYMENT INSURANCE
Data Indicate Substantial Levels of Fraud during the
Pandemic; DOL Should Implement an Antifraud
Highlights of GAO-23-105523, a report to
Strategy
congressional requesters
Why GAO Did This Study What GAO Found
The UI system has faced long-standing Considered together, measures and estimates indicate substantial levels of fraud
challenges with program integrity, and potential fraud in unemployment insurance (UI) programs during the
which worsened during the pandemic. pandemic. However, each measure and estimate has strengths and limitations,
Congress created four new UI and none completely and reliably indicates the extent of fraud in UI programs
programs to support workers during during the pandemic.
the pandemic. According to DOL data,
approximately $878 billion in benefits Examples and Definitions of Measures and Estimates
were paid across all UI programs from
April 2020 through September 2022.
The unprecedented demand for
benefits and the need to implement the
new programs quickly increased the
risk of financial fraud. Due to this risk
and other challenges, GAO added the
UI system to its High-Risk List in June
2022.
GAO was asked to review matters
relating to the scope and severity of
fraudulent activity in the UI system
during the pandemic. This report
examines what measures and
estimates indicate about the extent of
UI fraud during the pandemic and the
extent to which DOL designed and
implemented a strategy to manage UI
fraud risks. GAO reviewed information
that federal and state entities used to
measure and estimate the extent of
fraud in UI programs, then evaluated
DOL’s activities against leading
practices in GAO’s Fraud Risk
Framework. This report is part of an
ongoing body of work to better
understand and manage federal fraud,
including future work to improve
estimates of fraud.
What GAO Recommends
GAO recommends that DOL develop DOL has taken steps to address UI fraud risks. For example, DOL issued
and implement an antifraud strategy for guidance, provided funding to states, and deployed teams to recommend
UI programs that is consistent with improvements to state UI programs. While these steps help prevent, detect, and
leading practices from GAO’s Fraud respond to fraud, as of December 2022, DOL has not yet developed an antifraud
Risk Framework. DOL partially agreed strategy based on leading practices in GAO’s Fraud Risk Framework. Also, it has
with the recommendation and noted not yet addressed the six October 2021 recommendations GAO made including
plans to address it. to identify, assess the impact of, and prioritize UI fraud risks. These are essential
View GAO-23-105523. For more information, pieces to inform an overall antifraud strategy. Without an antifraud strategy, DOL
contact Seto Bagdoyan at (202) 512-6722 or is not able to ensure that it is addressing the most significant fraud risks facing
BagdoyanS@gao.gov, or Jared Smith at (202) the UI system in alignment with the Fraud Risk Framework.
512-2700 or SmithJB@gao.gov.
United States Government Accountability Office
Contents
Letter 1
Background 5
Federal and State Measures and Estimates Indicate Substantial
Fraud and Potential Fraud in UI Programs during the Pandemic
but Do Not Fully Reflect the Extent of Fraud 14
DOL Has Taken Steps to Address UI Fraud Risks but Has Not
Designed and Implemented a Strategy to Manage These Risks 31
Conclusions 41
Recommendation for Executive Action 42
Agency Comments and Our Evaluation 42
Appendix I Open Unemployment Insurance–Related Recommendations to the
Department of Labor 45
Appendix II State Reports on Unemployment Insurance (UI) Fraud and
Potential Fraud 47
Appendix III Objectives, Scope, and Methodology 51
Appendix IV Comments from the Department of Labor 55
Appendix V GAO Contacts and Staff Acknowledgments 59
Tables
Table 1: Examples of Federal and State Entities’ Unemployment
Insurance (UI) Fraud Measures and Key Strengths and
Limitations 16
Table 2: Examples of Federal and State Entities’ Unemployment
Insurance (UI) Fraud-Related Measures and Key
Strengths and Limitations 20
Table 3: Examples of Methods Used to Estimate Extent of
Unemployment Insurance (UI) Fraud and Potential Fraud
Page i GAO-23-105523 UI Fraud
at the Federal and State Levels and Key Strengths and
Limitations 24
Table 4: Summary of Approaches Used, Populations Assessed,
and Time Periods Covered by Three State-Level Reports 28
Table 5: Key Elements of an Antifraud Strategy as Described in
the Fraud Risk Framework 40
Table 6: GAO’s 19 Recommendations to the Department of Labor
(DOL) to Improve the Unemployment Insurance (UI)
System, Open as of December 15, 2022 45
Figures
Figure 1: The Four Components of the Fraud Risk Framework and
Selected Leading Practices 13
Figure 2: Examples of Court-Adjudicated Unemployment
Insurance Cases during the Pandemic and Related Fraud
Risks, Charges, Sentences, and Restitution Amounts 18
Page ii GAO-23-105523 UI Fraud
Abbreviations
BAM Benefit Accuracy Measurement
CARES Act Coronavirus Aid, Relief, and Economic Security
Act
DHS Department of Homeland Security
DOJ Department of Justice
DOL Department of Labor
EOUSA Executive Office for United States Attorneys
ETA Employment and Training Administration
FPUC Federal Pandemic Unemployment
Compensation
Fraud Risk Framework A Framework for Managing Fraud Risks in
Federal Programs
MEUC Mixed Earner Unemployment Compensation
NASWA National Association of State Workforce
Agencies
NUIFTF National Unemployment Insurance Fraud Task
Force
OIG Office of Inspector General
OMB Office of Management and Budget
PEUC Pandemic Emergency Unemployment
Compensation
PUA Pandemic Unemployment Assistance
SSA Social Security Administration
SWA state workforce agency
TEN Training and Employment Notice
UI Unemployment Insurance
UIPL Unemployment Insurance Program Letter
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Page iii GAO-23-105523 UI Fraud
Letter
441 G St. N.W.
Washington, DC 20548
December 22, 2022
Congressional Requesters
The unemployment insurance (UI) system has faced long-standing
challenges with effective service delivery and program integrity, which
worsened during the COVID-19 pandemic because of historic levels of
job loss. 1 The CARES Act, enacted on March 27, 2020, created three
new federally funded temporary UI programs that expanded UI benefit
eligibility, enhanced benefits, and extended benefit duration. 2 The
temporary programs supplemented existing UI programs known as
“regular” UI, which is a federal-state partnership that provides temporary
financial assistance to eligible workers who become unemployed through
no fault of their own. 3 The federal government directly funded the
administration of, and benefits for, the new pandemic UI programs and
relied on state workforce agencies (SWA) to determine claimants’
eligibility, process claims, and issue benefits to individuals. 4 From April
2020 through September 2022, expenditures across the UI system
1The UI system includes UI programs that were established prior to the COVID-19
pandemic (including the regular UI program and Extended Benefits), and programs
established in response to the COVID-19 pandemic (such as Pandemic Unemployment
Assistance and Federal Pandemic Unemployment Compensation, among others).
2Pub. L. No. 116-136, §§ 2102, 2104, 2107, 134 Stat. 281, 313-28. In addition, the
Consolidated Appropriations Act, 2021 created one additional temporary, supplemental UI
program. Pub. L. No. 117-2, § 9013(a), 135 Stat. 4, 119; Pub. L. No. 116-260, div. N, tit. II,
§ 261(a)(1), 134 Stat. 1182, 1961.
3In this report, we refer to the UI program—excluding the temporary UI programs created
by the CARES Act and other legislation—as the regular UI program and the benefits paid
under the program as regular UI benefits. We refer to the temporary UI programs created
by the CARES Act and the Consolidated Appropriations Act, 2021 as pandemic UI
programs.
4SWAs are responsible for administering unemployment insurance programs, among
other things.
Page 1 GAO-23-105523 UI Fraud
totaled approximately $878 billion, according to Department of Labor
(DOL) data. 5
States faced challenges processing a historically high number of claims
and ensuring that eligible individuals received timely and appropriate
benefit amounts during the pandemic. These timely payments allowed
unemployed workers to address financial hardships such as inability to
pay for rent, utilities, and groceries. The unprecedented demand for UI
benefits and the programmatic flexibilities allowed during the pandemic
also increased the risk of financial fraud as well as other improper
payments. 6 The DOL Inspector General testified in March 2022 that the
unprecedented infusion of federal COVID-19 relief funds into UI programs
during the pandemic gave individuals and organized crime groups a high-
value target to exploit. 7 In October 2021, we found that federal and state
5This amount includes about $209 billion in expenditures under the regular UI and
Extended Benefits programs, and about $669 billion in expenditures under pandemic UI
programs, which expired on September 6, 2021. The expenditure amounts for the
temporary programs include all compensation paid throughout the existence of the
programs. These programs were generally created at the end of March 2020 and expired
in September 2021, though some payments may have occurred after September 2021 for
weeks of unemployment prior to the programs’ expiration. We obtained April 2020 through
September 2022 expenditure amounts for the regular UI program, the Extended Benefits
program, and the pandemic UI programs on October 12, 2022 from DOL’s data
downloads website at https://oui.doleta.gov/unemploy/DataDownloads.asp.
6Fraud involves obtaining something of value through willful misrepresentation. The
Payment Integrity Information Act of 2019 defines an improper payment 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. 31 U.S.C. § 3351(4). As such, improper payments refer to
all kinds of erroneous payments, including but not limited to those resulting from fraud.
While all financial fraud contributes to improper payments, non-financial fraud, such as
fraudulently obtaining identification documents, may not result in an improper payment.
7Larry D. Turner, Inspector General, Department of Labor, Office of Inspector General,
testimony before the U.S. Senate Committee on Homeland Security and Governmental
Affairs, 19-22-003-315, March 17, 2022.
Page 2 GAO-23-105523 UI Fraud
entities continued to investigate and report on high levels of fraud,
potential fraud, and fraud risk in UI programs during the pandemic. 8
The increased significance of the UI system during the pandemic drew
attention to its vulnerabilities and susceptibility to fraud, waste, abuse,
and mismanagement. Based on findings from the DOL Office of Inspector
General (OIG) and prior GAO reports, and the urgent need to address
persistent issues in the UI system—including funding uncertainties and
outdated IT systems—we determined in June 2022 that the UI system
should be on our High-Risk List and made an out-of-cycle high-risk
designation. 9 This designation is intended to help spur progress in
resolving persistent issues by shining a spotlight on such issues and
ways the federal government can lead efforts to find solutions. We
reported that such efforts include addressing our 19 open
recommendations and those of the DOL OIG. See appendix I for a
detailed list of the open recommendations.
You asked us to review matters relating to the scope and severity of
fraudulent activity in the UI system during the pandemic. This report
addresses (1) what existing federal and state measures and estimates
indicate about the extent of fraud and potential fraud in UI programs
during the pandemic and (2) the extent to which DOL designed and
implemented a strategy to manage UI fraud risks.
To address our first objective, we requested, identified, and reviewed
relevant reports and other reporting mechanisms from federal and state
8GAO, Additional Actions Needed to Improve Accountability and Program Effectiveness of
Federal Response, GAO-22-105051 (Washington, D.C.: October 27, 2021). Fraud risk
(which is a function of likelihood and impact) exists when people have an opportunity to
engage in fraudulent activity, have an incentive or are under pressure to commit fraud, or
are able to rationalize committing fraud. Fraud risk includes existing circumstances that
provide an opportunity to commit fraud. In this report, we define potential fraud as
transactions or activity that have indicators that may suggest fraud, and we define fraud as
transactions or activities that have been confirmed to be fraudulent via an adjudicative or
other formal determination process.
9GAO, Unemployment Insurance: Transformation Needed to Address Program Design,
Infrastructure, and Integrity Risks, GAO-22-105162 (Washington, D.C.: June 7, 2022). The
High-Risk List highlights federal programs and operations that we have determined are in
need of transformation. It also names federal programs and operations that are vulnerable
to waste, fraud, abuse, and mismanagement. We release a High-Risk series report every
2 years at the start of each new congress. As in this case, we sometimes make out-of-
cycle designations to highlight urgent issues, help ensure focused attention, and maximize
the opportunity for the federal government to take action.
Page 3 GAO-23-105523 UI Fraud
entities related to measuring and estimating the extent of fraud and
potential fraud in UI programs during the pandemic. 10 The scope of our
review was from March 2020—the beginning of the pandemic—through
March 2022. These are the two most recent years available at the time of
our selection. Throughout this report, we use the phrase “fraud measure”
to discuss counts related to proven fraud such as adjudicated cases of
fraud. We use the phrase “fraud estimate” to discuss estimates that
attempt to quantify what could be determined to be fraud, although such
cases have not yet been proven. Finally, we use the phrases “fraud-
related” and “potential fraud” to describe measures and estimates that
attempt to quantify the extent of fraud indicators, but do not suggest a
potential or actual determination of fraud. In addition, we reviewed
Department of Justice (DOJ) case information to identify federal fraud-
related charges related to UI as of July 31, 2022, and analyzed related
federal court documents. 11 We also interviewed DOL officials and federal
law enforcement officials about the extent of fraud in UI programs during
the pandemic.
Additionally, we analyzed UI financial transaction data and individual
program files from DOL’s website to determine total expenditures under
the regular UI program, the Extended Benefits program, and the
pandemic UI programs from April 2020 through September 2022. We also
analyzed fraud estimates for the regular UI program reported in DOL’s
Benefit Accuracy Measurement Data Summary for performance year
2021 and the underlying documentation. In addition, we analyzed
amounts of overpayments due to fraud as reported by states to DOL. We
assessed the reliability of all data used in these three analyses, and
determined they were sufficiently reliable for our purposes.
To address the second objective, we evaluated DOL’s UI fraud risk
management activities against the leading practices in GAO’s A
Framework for Managing Fraud Risks in Federal Programs (Fraud Risk
10Throughout this report, we refer to measures as counts of detected activities, and to
estimates as projections or inferences based on measures, assumptions, or analytical
techniques. Estimates are often used when direct measures are unavailable, incomplete,
or unreliable. Appendix II provides a bibliography of the reports we received from SWAs,
state auditors, and state Attorneys General that discussed fraud or potential fraud in UI
programs. We excluded those reports that did not contain a measure or estimate of fraud
or potential fraud, such as those that focused on improper payments but lacked any fraud
or fraud-related measures or estimates.
11For purposes of this report, we define fraud-related charges as charges related to a
criminal case containing fraud charges.
Page 4 GAO-23-105523 UI Fraud
Framework)—specifically those leading practices related to developing an
antifraud strategy. 12 We also reviewed DOL policies, procedures, and
guidance to identify newly established controls designed to prevent fraud
from occurring. Further, we interviewed DOL and DOL OIG officials about
fraud risk management efforts.
Appendix III provides a more detailed description of our objectives, scope,
and methodology.
We conducted this performance audit from November 2021 to December
2022 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 and conclusions based on our audit objectives. We believe
that the evidence obtained provides a reasonable basis for our findings
and conclusions based on our audit objectives.
Background
Federally Funded UI The CARES Act created three new federally funded temporary UI
Programs in Response to programs that expanded UI benefit eligibility and enhanced benefits. 13
COVID-19 These programs were subsequently extended and amended by the
Consolidated Appropriations Act, 2021, as well as the American Rescue
Plan Act of 2021, and expired in September 2021. 14
12GAO, A Framework for Managing Fraud Risks in Federal Programs, GAO-15-593SP
(Washington, D.C.: July 28, 2015). The Fraud Risk Framework contains four components:
(1) commit; (2) assess; (3) design and implement; and (4) evaluate and adapt. Within the
four components, there are overarching concepts and leading practices. In October 2021,
we assessed the extent to which DOL’s fraud risk management activities aligned with
leading practices under the second component of the Fraud Risk Framework—assess.
See GAO-22-105051. For this report, we selected two of the 15 leading practices from the
third component—design and implement—that are most relevant to this objective based
on a review of DOL documents and discussions with DOL officials responsible for fraud
risk management.
13Pub. L. No. 116-136, §§ 2102, 2104, 2107, 134 Stat. 281, 313-28. The CARES Act also
addressed other elements of the UI system. For example, the act authorized certain
flexibilities for states to hire additional staff and to participate in Short-Time Compensation
programs, which allow workers to work reduced hours while receiving partial pay and
partial UI benefits.
14Twenty-four states ended their participation in at least one of these programs before the
programs expired in September 2021.
Page 5 GAO-23-105523 UI Fraud
1. Pandemic Unemployment Assistance (PUA) authorized UI benefits
for individuals not otherwise eligible for UI benefits, such as self-
employed workers and independent contractors, who were unable or
unavailable to work as a result of specified COVID-19 reasons. 15
2. Federal Pandemic Unemployment Compensation (FPUC)
generally authorized an additional weekly benefit for individuals who
were eligible for weekly benefits under the permanent UI programs—
for example, regular UI—and the temporary CARES Act programs. 16
3. Pandemic Emergency Unemployment Compensation (PEUC)
generally authorized additional weeks of UI benefits for those who had
exhausted their regular UI benefits. 17
In addition, the Consolidated Appropriations Act, 2021 created the Mixed
Earner Unemployment Compensation (MEUC) program. This program
was extended by the American Rescue Plan Act of 2021 and expired in
September 2021. 18 According to DOL, the MEUC program was intended
to cover regular UI claimants whose benefits did not account for
significant self-employment income and who thus may have received a
15At the time of the program’s expiration in September 2021, PUA generally authorized up
to 79 weeks of benefits. Pub. L. No. 117-2, § 9011(a), (b), 135 Stat. 4, 118; Pub. L. No.
116-260, div. N, tit. II, § 201(a), (b), 134 Stat. 1182, 1950-1951 (2020); Pub. L. No. 116-
136, § 2102, 134 Stat. 281, 313 (2020).
16FPUC generally authorized an additional $600 benefit through July 2020 as well as an
additional $300 benefit for weeks beginning after December 26, 2020, through the end of
the program. Pub. L. No. 117-2, § 9013, 135 Stat. 4, 119; Pub. L. No. 116-260, div. N, tit.
II, § 203, 134 Stat. 1182, 1953; Pub. L. No. 116-136, § 2104 Stat. 281, 318.
17At the time of the program’s expiration, PEUC generally authorized an additional 53
weeks of benefits for claimants who were fully unemployed. Pub. L. No. 117-2, § 9016(a),
(b), 135 Stat. 4, 119-120; Pub. L. No. 116-260, div. N, tit. II, § 206(a), (b), 134 Stat. 1182,
1954; Pub. L. No. 116-136, § 2107, 134 Stat. 281, 323.
18The MEUC program, which was voluntary for states, authorized an additional $100
weekly benefit for certain UI claimants who received at least $5,000 of self-employment
income in the most recent tax year prior to their application for UI benefits between
December 27, 2020 and September 6, 2021. Pub. L. No. 117-2, § 9013(a), 135 Stat. 4,
119; Pub. L. No. 116-260, div. N, tit. II, § 261(a)(1), 134 Stat. 1182, 1961.
Page 6 GAO-23-105523 UI Fraud
lower regular UI benefit than the benefit they would have received had
they been eligible for PUA. 19
UI Program Administration The federal government and states work together to administer UI
and Funding programs. 20 States design and administer their own UI programs within
federal parameters. DOL oversees states’ compliance with federal
requirements, such as by reviewing state laws to confirm they are
designed to ensure payment of benefits when due. According to DOL,
state statutes establish specific benefit structures, eligibility provisions,
benefit amounts, and other aspects of the program. Regular UI benefits—
those provided by state UI programs before the CARES Act was
enacted—are funded primarily through state taxes levied on employers
and are intended to replace a portion of a claimant’s previous
employment earnings, according to DOL. 21
During the pandemic, states continued to operate the regular UI program
while administering the pandemic UI programs. Generally, states were
permitted to ease certain nonmonetary requirements, such as waiving
work-search requirements, in response to the spread of COVID-19. 22
While states were permitted temporary flexibility regarding work search,
DOL determined that states could not waive the requirement to be
available and able to work. DOL noted that states had flexibility to
determine what it meant for an individual to be able to work and be
available to work during the pandemic.
In the UI system, program integrity is a shared responsibility between the
federal and state governments. DOL provides general support and
19According to DOL, 51 states and territories elected to participate in the MEUC program,
with Idaho and South Dakota opting not to participate, but 23 states terminated their
participation in June or July 2021. The remaining 28 states and territories continued
participating in the MEUC program until it expired in September 2021, including Maryland,
which intended to terminate participation but did not because of litigation at the state level,
according to DOL.
20Fifty-three SWAs administer UI programs across the 50 states, the District of Columbia,
Puerto Rico, and the U.S. Virgin Islands. For purposes of this report, when we refer to
states’ administration of the UI program, we include both states and territories.
21To be eligible for regular UI benefits, applicants must generally demonstrate workforce
attachment, be able and available to work, and be actively seeking work. 42 U.S.C. §
503(a)(12). Administration of the regular UI program is financed by a federal tax on
employers, according to DOL.
22Pub. L. No. 116-127, § 4102(b), 134 Stat. 178, 194 (2020).
Page 7 GAO-23-105523 UI Fraud
technical assistance, and states assume responsibility for determining
eligibility, ensuring accurate benefit payments, and preventing fraud and
other improper payments. 23
The unprecedented demand for UI benefits and the urgency with which
states implemented the new programs during the pandemic increased the
risk of improper payments, including but not limited to those due to fraud.
DOL uses its Benefit Accuracy Measurement (BAM) program to estimate
the amount and rate of improper payments. 24 Under the BAM program,
each state reviews a number of randomly selected cases on a weekly
basis and reconstructs the UI claims process to assess the accuracy of
the payments that were made. 25 A BAM investigator reviews each
sampled claim and identifies errors and the causes of the error, including
those caused by fraudulent activity. 26 Additional details, along with a
discussion of limitations of the BAM program, are presented below.
23An improper payment is defined 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, but is not limited to, any payment to an ineligible recipient. See 31 U.S.C. §
3351(4). When an agency cannot determine, due to lacking or insufficient documentation,
whether a payment is proper, the payment shall be treated as an improper payment. See
31 U.S.C. § 3352(c)(2).
24According to DOL, although temporary UI programs, like the CARES Act UI programs,
have generally not been subject to the BAM program or improper payment estimation,
DOL has extrapolated and applied the improper payment rates generated by BAM to
PEUC and FPUC and included them in the UI improper payment estimate for fiscal year
2021 reporting. On July 14, 2022, DOL announced its plan to estimate the rate of
improper payments for PUA and to report a statistically valid national improper payment
rate by fall 2022. However, in its fiscal year 2022 reporting on paymentaccuracy.gov, DOL
stated that in October 2022, OMB requested that DOL conduct further analysis of the
outcomes recorded through the PUA case review process. Also, according to DOL, OMB
and DOL agreed to collaborate in conducting this additional analysis but DOL reported
that it cannot be completed in time to meet the fiscal year 2022 reporting deadline.
Therefore, according to DOL, OMB allowed additional time to conduct this analysis and
report on PUA outcomes in fiscal year 2023.
25BAM is a statistical survey used to identify and support resolutions of deficiencies in a
state’s UI system. BAM is also used to identify the root causes of improper payments and
supports other analyses conducted by DOL to identify improper payment prevention
strategies and measure progress in meeting improper payments reduction strategies.
26Reviewer determinations of an error caused by fraudulent activity do not constitute a
formal legal adjudication. However, according to DOL officials, some state BAM units are
able to establish—complete an investigation and make a determination of fraud—errors
caused by fraud.
Page 8 GAO-23-105523 UI Fraud
Before the pandemic, DOL regularly reported billions of dollars in annual
estimated improper payments in UI. It reported an increase from $8.0
billion (9.2 percent improper payment rate) for fiscal year 2020 to $78.1
billion (18.9 percent improper payment rate) for fiscal year 2021. 27 For
fiscal year 2022, DOL reported estimated improper payments of $18.9
billion (22.2 percent improper payment rate). 28 Improper payments could
suggest that a program may be vulnerable to fraud. However, improper
payments represent all overpayments and underpayments resulting from
any type of intentional or unintentional error, and this amount is not a
valid indicator of the extent of fraud in a particular program.
Fraud and Fraud Risk Fraud involves obtaining something of value through willful
Management misrepresentation, which is determined through the judicial or other
adjudicative systems. 29 DOL’s OIG reported in November 2021 that
fraud—specifically claimants who received UI benefits through fraudulent
schemes such as those perpetrated during the COVID-19 pandemic—
was one of the leading causes of improper payments. However, it did not
report on a specific amount of fraud, as we discuss below. 30
In October 2021, we found that fraudulent and potentially fraudulent
activity in the UI program increased substantially after implementation of
the pandemic UI programs, relative to the amount of such activity in the
27DOL’s OIG reported that an independent auditor had concluded that DOL had not met
three of the six requirements for compliance with the Payment Integrity Information Act for
the UI program for fiscal year 2021. Department of Labor, Office of Inspector General, The
U.S. Department of Labor Did Not Meet the Requirements for Compliance with the
Payment Integrity Information Act for FY 2021, Report No. 22-22-007-13-001
(Washington, D.C.: July 1, 2022).
28As of November 28, 2022, DOL’s OIG had not yet reported on DOL’s compliance with
the Payment Integrity Information Act for fiscal year 2022. As described above, DOL’s
fiscal year 2022 improper payment estimates do not include PUA claims.
29According to DOL, because states may use different definitions for categorizing an
overpayment as fraudulent, an overpayment that is classified as fraudulent in one state
might not be classified as fraudulent in another state.
30Department of Labor, Agency Financial Report, Fiscal Year 2021 (Washington, D.C.:
November 19, 2021). As reported in DOL’s Benefit Accuracy Measurement Data
Summary for performance year 2021, leading causes of improper payments included
unreported or misreported benefit year earnings, issues involving the claimant’s reasons
for separating from work, and other eligibility issues. These other eligibility issues include
refusal of suitable work, self-employment, a noncitizen not authorized to work, and when a
claimant filed a UI claim using the identity of another person. DOL also identified these
causes as contributing to the fraud rate.
Page 9 GAO-23-105523 UI Fraud
regular UI program before the pandemic. 31 The increased amount of
benefits awarded and the PUA program’s initial reliance on self-
certification, as discussed below, gave criminals incentive and
opportunities to commit fraud. DOL officials also identified other factors—
including significant increases in claims workload, new and inexperienced
staff, and quick implementation of new programs—that provided
additional opportunities for exploitation of program and system
vulnerabilities. In addition, DOL officials stated that the UI programs
during the pandemic were a key target for fraud because fraudsters could
receive a large amount of money in one payment.
In its fiscal year 2020 Agency Financial Report, DOL acknowledged an
increase in potentially fraudulent activity related to organized fraud
schemes targeting the pandemic UI programs. 32 Moreover, according to
National Association of State Workforce Agencies (NASWA) officials, the
UI system has faced unrelenting attacks by foreign organized crime
groups during the pandemic. 33 Also, in a March 2021 press release, the
U.S. Secret Service noted that its early investigation and analysis
indicated that international organized criminal groups have targeted UI
funds using stolen identities to file for UI benefits.
Our prior review of DOL OIG reports, state audits, and DOJ cases
identified several fraud risks in the UI programs and identified factors
contributing to fraud risk. 34 Specifically, we identified several fraud cases
that relied on a variety of mechanisms, which illustrate some of the fraud
risks in pandemic UI programs: 35
• applicants’ falsifying information on income or employment eligibility to
receive benefits;
• applicants’ using stolen identities or personally identifiable information
to apply for benefits or receive benefits;
31GAO-22-105051.
32Department of Labor, Agency Financial Report Fiscal Year 2020 (Washington, D.C.:
November 16, 2020).
33NASWA represents all 50 SWAs, the District of Columbia, and U.S. territories.
34GAO-22-105051.
35The fraud risks identified in this report do not constitute an exhaustive list of all fraud
risks affecting the UI programs.
Page 10 GAO-23-105523 UI Fraud
• applicants’ applying for, or receiving, benefits by using fake identity
information;
• applicants’ submitting fraudulent claims or erroneously receiving
benefits in multiple states;
• prison inmates’ applying for benefits while misrepresenting their
eligibility; and
• current or former federal or state or territory employees’ misusing their
positions to fraudulently obtain benefits for themselves or others.
In addition, the DOL OIG reported in October 2020 that the PUA program
in particular was at high risk for fraud due to its unique program rules and
eligibility requirements. 36 Specifically, the CARES Act allowed PUA
applicants to self-certify their eligibility and did not require them to provide
any documentation of self-employment or prior income. In October 2021,
we reported that relying on program participants to self-report and self-
certify information on agency forms, instead of verifying such information
independently, could cause an agency to miss opportunities to prevent
program fraud and abuse. 37 To help address this risk, the Consolidated
Appropriations Act, 2021, enacted in December 2020, included a
requirement for individuals to submit documentation of employment or
self-employment when applying for PUA. In addition, the expanded
coverage offered under the PUA program posed significant challenges to
states as they implemented processes to determine initial and continued
program eligibility for participants. PUA claims accounted for a significant
portion of UI claims, especially during the beginning of the pandemic. For
example, DOL reported that PUA claims comprised over 40 percent of
3,256,000 UI claims submitted nationwide within the week ending May
23, 2020.
The objective of fraud risk management is to help ensure program
integrity by continuously and strategically mitigating both the likelihood
and effects of fraud. When fraud risks can be identified and mitigated,
fraud may be less likely to occur. Although the occurrence of fraud
indicates there is a fraud risk, a fraud risk can exist even if actual fraud
has not yet occurred or been identified. Effectively managing fraud risk
helps to ensure that federal programs’ services fulfill their intended
purpose—that funds are spent effectively, and that assets are
36Department of Labor, Office of Inspector General, COVID-19: States Cite Vulnerabilities
in Detecting Fraud While Complying with the CARES Act UI Program Self-Certification
Requirement, Report No. 19-21-001-03-315 (Washington, DC: October 21, 2020).
37GAO-22-105051.
Page 11 GAO-23-105523 UI Fraud
safeguarded. Executive branch agency managers are responsible for
managing fraud risks and implementing practices for combating those
risks.
Recognizing fraud risks and deliberately managing them in an emergency
environment can help federal managers safeguard public resources while
providing needed relief. Managers may perceive a conflict between their
priorities to fulfill the program’s mission—such as efficiently disbursing
funds or providing services to beneficiaries, particularly during
emergencies—and taking actions to safeguard taxpayer dollars from
improper use. However, the purpose of proactively managing fraud risks,
even during emergencies, is to facilitate, not hinder, the program’s
mission and strategic goals by ensuring that taxpayer dollars and
government services serve their intended purposes.
In July 2015, we issued the Fraud Risk Framework, which 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, risk-based way. 38 The Fraud Reduction and
Data Analytics Act of 2015 required the Office of Management and
Budget (OMB) to establish guidelines for federal agencies to create
controls to identify and assess fraud risks and to design and implement
anti-fraud control activities. 39 The act further required OMB to incorporate
the leading practices from the Fraud Risk Framework in the guidelines.
Although the Fraud Reduction and Data Analytics Act of 2015 was
repealed in March 2020, the Payment Integrity Information Act of 2019
requires these guidelines to remain in effect, subject to modification by
OMB as necessary, and in consultation with GAO. 40 As depicted below in
figure 1, the Fraud Risk Framework describes leading practices within
38GAO-15-593SP.
39Pub. L. No. 114-186, 130 Stat. 546 (2016).
40Pub. L. No. 116-117, § 2(a), 134 Stat. 113, 131 - 132 (2020), codified at 31 U.S.C. §
3357. In October 2022, OMB issued a Controller Alert reminding agencies that consistent
with the guidelines contained in OMB Circular A-123, which are required by Section 3357
of the Payment Information Integrity Act of 2019, Pub. L. No. 116-117, they must establish
financial and administrative controls to identify and assess fraud risks. In addition, OMB
reminds agencies that they should adhere to the leading practices in GAO’s Fraud Risk
Management Framework as part of their efforts to effectively design, implement, and
operate an internal control system that addresses fraud risks. OMB, CA-23-03,
Establishing Financial and Administrative Controls to Identify and Assess Fraud Risk,
(October 17, 2022).
Page 12 GAO-23-105523 UI Fraud
four components: commit, assess, design and implement, and evaluate
and adapt.
Figure 1: The Four Components of the Fraud Risk Framework and Selected Leading Practices
In October 2021, we found that DOL had taken steps to prevent and
detect fraud in UI programs and had ongoing program integrity activities
to identify risk. 41 However, DOL had not comprehensively assessed fraud
risks in alignment with leading practices identified in the first and second
components of the Fraud Risk Framework. We made six
recommendations that DOL take actions to designate a dedicated
41GAO-22-105051.
Page 13 GAO-23-105523 UI Fraud
antifraud entity and comprehensively assess UI fraud risks in alignment
with leading practices. DOL neither agreed nor disagreed with these
recommendations. As of December 15, 2022, all six of these
recommendations remain open. We discuss the status of these
recommendations in more detail later in this report.
This report focuses on two leading practices within the third component of
the Fraud Risk Framework that are contingent upon creating a fraud risk
profile:
(1) using the fraud risk profile to help decide how to allocate resources
to respond to residual fraud risks and
(2) developing, documenting, and communicating an antifraud
strategy to employees and stakeholders that describes the program’s
activities for preventing, detecting, and responding to fraud.
Considered together, measures—counts of detected activities—and
Federal and State estimates—projections or inferences based on measures, assumptions,
Measures and or analytical techniques—indicate substantial levels of fraud and potential
fraud in UI programs during the pandemic. Federal and state entities have
Estimates Indicate produced several fraud and fraud-related measures and estimates of UI
Substantial Fraud and fraud during the pandemic. These measures and estimates reflect a
variety of characteristics and potential indicators of fraud. While each type
Potential Fraud in UI of measure and estimate has strengths and limitations, as described
Programs during the below, none completely and reliably indicates the extent of fraud in UI
programs during the pandemic.
Pandemic but Do Not
Fully Reflect the Based on one fraud measure—the amount of payments associated with a
formal determination of fraud reported by SWAs—UI fraud during the
Extent of Fraud pandemic is at least $4.3 billion according to state reporting from April
2020 through June 2022. However, this figure does not account for
potential fraud that has not yet been formally determined as such. Based
on one fraud-related measure—cases flagged by the DOL OIG as
potentially fraudulent—at least $45 billion in UI payments from March
2020 through April 2022 have some indication of potential fraud in four
Page 14 GAO-23-105523 UI Fraud
high-risk areas. 42 However, some of the flagged transactions may not be
fraudulent and not all fraudulent transactions may be flagged. As a result,
this fraud-related measure can be used to identify certain types of
transactions that may be indicative of potential fraud, but cannot be
interpreted directly as a measure of the extent of fraud in the UI programs
during the pandemic.
In addition to fraud and fraud-related measures, estimates can be used to
approximate the extent of fraud and potential fraud beyond what can be
directly counted. Although no national estimate of UI fraud has been
reported that covers all UI programs and the full period of pandemic
spending, DOL has reported estimates of fraud for regular UI payments.
These estimates of fraud for regular UI payments, in combination, amount
to about $8.5 billion for performance year 2021, covering July 1, 2020
through June 30, 2021. This represents a fraud rate of almost 8.6 percent
for the period for the regular UI program. 43 If the lower bound of this fraud
rate (7.6 percent) was extrapolated to total spending across all UI
42Department 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.: September 21, 2022). DOL OIG
conducted analysis on UI claims data in four high-risk areas: (1) multi-state claimants; (2)
Social Security Numbers of the deceased; (3) federal prisoners; and (4) suspicious email
accounts.
43DOL uses its BAM program to estimate the amount and rate of improper payments,
including those caused by fraud. As discussed in greater detail in table 3, there are
limitations associated with these estimates. For example, the BAM program did not cover
the start of the pandemic due to a 3-month suspension of testing. Also, the BAM program
does not include payments for the pandemic UI programs and the estimates did not
include an estimate for PUA. In October 2020, the DOL OIG reported that the PUA
program in particular was at high risk for fraud due to its unique program rules and
eligibility requirements. Department of Labor, Office of Inspector General, Report No. 19-
21-001-03-315.
Page 15 GAO-23-105523 UI Fraud
programs during the wider pandemic period, it would suggest over $60
billion in fraudulent UI payments. 44
Fraud measures. Fraud measures are based on cases resolved via
adjudicative process or other formal determinations of fraud. According to
one fraud measure—the amount of payments associated with a formal
determination of fraud reported by SWAs—$4.3 billion in fraudulent UI
payments have been made during the pandemic from April 2020 through
June 2022. Fraud measures do not include fraudulent activity that is
undetected or yet to be adjudicated or formally determined to be fraud,
and therefore reflects an incomplete picture of the extent of fraudulent
activity. Table 1 describes examples of federal and state entities’ UI fraud
measures, and the associated key strengths and limitations.
Table 1: Examples of Federal and State Entities’ Unemployment Insurance (UI) Fraud Measures and Key Strengths and
Limitations
Proven fraud Description
Measure based on cases resolved via adjudicative process or other formal determination of fraud.
Key strengths Key limitations
Represents a legal or formal determination of Significantly understates fraudulent activity due to
fraud. limited resources to investigate, prosecute, and
Unlikely to count non-fraud. adjudicate cases and the difficulties inherent in
proving guilt.
44This approach relies on data from a manual file review of a statistical sample and falls
under the extrapolation from rates method described in table 3, and should be considered
in line with the limitations of both methods. To help account for these limitations and the
rough nature of our estimate, we rounded down to the nearest $10 billion. To specifically
address the uncertainty arising from the BAM program’s use of statistical sampling, we
used the lower limit of the BAM estimate. The available measures and estimates support
the use of the 2021 BAM fraud rate as an approximate lower, but not upper limit of the
fraud rate for all UI programs and the full period of pandemic spending. The actual amount
of fraud in UI programs during the pandemic may be substantially higher than the
estimated lower limit reported here. As noted in the next section, extrapolation is a
technique that can offer a rough or notional estimate of fraud or potential fraud even if
data on a specific measure or rate are unavailable, but may have limitations related to
validity, accuracy, and completeness.
Page 16 GAO-23-105523 UI Fraud
Examples of fraud measures
Count of cases adjudicated as fraud in court.
• From March 2020 through July 2022, at least 308 individuals have pleaded guilty to federal fraud-
related charges related to UI programs brought by the Department of Justice (DOJ) and at least two
individuals have been convicted at trial of related charges.a
• According to a December 2021 press release from Michigan’s state workforce agency (SWA), nine
people had pleaded guilty or been convicted of UI fraud, and three had been sentenced based on the
work of the Attorney General’s Michigan UI Fraud Task Force.
Count of fraud overpayments from cases resolved via formal determination of fraud.
• From April 2020 through June 2022, SWAs reported about $4.3 billion in overpayments from fraud
across the UI programs, including about $1.6 billion from Pandemic Unemployment Assistance (PUA),
$1.6 billion from Federal Pandemic Unemployment Compensation, $0.9 billion from the regular UI and
Extended Benefits programs, and $0.2 billion from Pandemic Emergency Unemployment
Compensation.b
Source: GAO analysis of fraud measures and Nobelus/stock.adobe.com (icon). | GAO-23-105523
a
This analysis is limited to the DOJ cases we identified from public sources, which may not include all
criminal and civil cases charged by DOJ as of July 31, 2022. In technical comments on a draft of this
report, DOJ officials indicated that the Executive Office for United States Attorneys’ (EOUSA) data
show that 296 individuals pleaded guilty to UI-related charges between March 2020 and July 2022.
However, DOJ officials also stated that EOUSA’s statistics are not inclusive of other DOJ
components. In addition, EOUSA’s case management system can provide information about
convictions at trial for pandemic fraud cases generally, but it does not have the ability to identify UI
fraud convictions following a trial. Of these 310 individuals, 207 have been sentenced as of July 31,
2022.
b
Because states may use different definitions, an overpayment that is classified as fraudulent in one
state might not be classified as fraudulent in another state. We accessed the fraud overpayments
data on August 24, 2022; these data are subject to change as more states report data and as states
revise previously reported data. The total PUA amount shown also includes fraud overpayments
related to identity theft.
One of the fraud measures described in the table above includes cases of
UI fraud adjudicated in federal court. These cases vary by fraud scheme,
charges filed, sentences and restitution amounts, and the details can
provide valuable information to better understand UI fraud. 45 Figure 2
provides examples of court-adjudicated UI cases during the pandemic
and related fraud risks, charges, sentences, and restitution amounts.
45While restitution can be a fraud measure for the specific case being prosecuted, there
are limitations associated with a fraud measure based on combining restitution amounts
across cases. For example, multiple parties might share the responsibility of paying
restitution. Additionally, the amount of restitution ordered may not be the same as the
amount that was fraudulently obtained. Further, restitution is not always likely to be paid.
We previously reported that collecting federal criminal restitution is a long-standing
challenge. GAO, Federal Criminal Restitution: Department of Justice Has Ongoing Efforts
to Improve Its Oversight of the Collection of Restitution and Tracking the Use of Forfeited
Assets, GAO-20-676R (Washington, D.C.: September 30, 2020).
Page 17 GAO-23-105523 UI Fraud
Figure 2: Examples of Court-Adjudicated Unemployment Insurance Cases during the Pandemic and Related Fraud Risks,
Charges, Sentences, and Restitution Amounts
Page 18 GAO-23-105523 UI Fraud
Fraud-related measures. Fraud-related measures are counts of
potentially fraudulent activities that have not yet been determined to be
fraud via adjudicative or another formal determination. One such measure
suggests that at least $45 billion in payments from March 2020 through
April 2022 have at least some indication of potential fraud based on four
high-risk areas. This measure suggests that there is a substantial amount
of potential fraud in UI programs, but does not reflect what share of that
potential fraud is actually fraudulent. 46
Fraud-related measures represent counts of indicators of potential fraud,
such as the number of cases flagged by internal control systems,
reported through fraud hotlines, or under investigation. However, because
fraud-related measures may both overstate (false positives) and
understate (false negatives) potential fraud, they are generally not valid or
reliable indicators of the extent of fraud. For example, the $45 billion
potential fraud measure described above potentially understates fraud
because it only includes the four high-risk areas. However, at the same
time, it may also overstate fraud because it may include flagged cases
that, if investigated and adjudicated, could be determined not to be
fraudulent.
As described in the fraud measures section above, the risk of
undercounting fraud is very high with adjudicated cases, while the risk of
false positives is very low. Among fraud-related measures, the risk of
false positives generally increases as more of the potentially
undercounted fraud is captured. Specifically, potential fraud that the
government investigated and took some action to remedy but did not
adjudicate, or has not yet adjudicated, likely captures more fraudulent
activity than fraud measures do, but also likely captures some cases that
are not fraud. In addition, potential fraud that is reported but has not yet
gone through due process may provide some greater sense of other
cases yet to be adjudicated, but also potentially includes more false
46Department 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, DC: September 21, 2022). This is
likely an understatement of the extent of potential fraud because the analysis is limited to
identifying potential fraud in four high-risk areas: (1) multi-state claimants; (2) Social
Security Numbers of the deceased; (3) federal prisoners; and (4) suspicious email
accounts. DOL officials told us that it is likely that there is significant fraud outside of these
four categories. For example, DOL OIG officials noted that while the OIG’s investigative
work has seen thousands of instances of claims filed using stolen identities during its
investigations, this analysis does not include stolen identities.
Page 19 GAO-23-105523 UI Fraud
positives. Furthermore, flagged transactions identified through data
analytics can be produced with relatively few resources compared to
prosecutions and investigations. 47 However, these flagged transactions
may still fail to capture fraudulent activity for which identification tools
have not been developed and will likely capture the most non-fraudulent
activity. Table 2 describes examples of federal and state entities’ UI
fraud-related measures, and the associated key strengths and limitations.
Table 2: Examples of Federal and State Entities’ Unemployment Insurance (UI) Fraud-Related Measures and Key Strengths
and Limitations
Cases accepted for Description
prosecution Measure based on counts of potential fraud cases brought by the Department of Justice (DOJ) or other
prosecutorial agencies.
Key strengths Key limitations
Likely to capture fraudulent activity not yet included May also include cases that do not involve fraud.
in the proven fraud category, with relatively low Likely omits many cases that are fraudulent.
likelihood of counting non-fraud.
Examples of fraud-related measures
Count of cases with charges filed.
• Since March 2020, DOJ has publicly announced charges related to UI fraud. As of July 31, 2022, at
least 226 individuals are facing federal fraud-related charges related to UI.a
• As of September 2022, the Department of Labor (DOL) Office of Inspector General (OIG) reported that
its investigations have resulted in charging more than 1,000 individuals with crimes involving UI fraud
since March 2020.
• The Pennsylvania Attorney General’s office had brought charges against 56 individuals as of June
2022 for theft and related charges arising from fraudulent applications for PUA, according to an official
from this office.
• In Michigan, according to a press release from the state workforce agency (SWA), 54 individuals were
charged with UI fraud by either state or federal authorities as of December 2021.b
• According to an official with the Colorado Attorney General’s office, from June 2021 through June
2022, Colorado’s SWA referred 31 cases of potential UI fraud involving 77 claims to state prosecutors,
with the related restitution sought for these cases totaling over $800,000.
Cases accepted for Description
investigation Measure based on counts of investigative actions by law enforcement or other investigative agencies.
Key strengths Key limitations
Some indication that knowledgeable law Similar to proven fraud and cases accepted for
enforcement experts find sufficient evidence of prosecution, with a greater risk of including cases that
potential fraud to warrant an investigation. are not fraudulent.
This method may provide some sense of other Likely omits cases that are fraudulent.
cases yet to be adjudicated or charged.
47Data analytics can include predictive analytics, data mining, and data matching
techniques that enable programs to identify potential fraud or improper payments, either
prior to or after payments are made. See GAO, A Framework for Managing Fraud Risks in
Federal Programs, GAO-15-593SP (Washington, D.C.: July 2015).
Page 20 GAO-23-105523 UI Fraud
Examples of fraud-related measures
Count of ongoing investigative actions.
• From the start of the pandemic through March 2022, the U.S. Secret Service had investigated
approximately 930 cases related to fraud in UI programs in coordination with DOL OIG and other
members of the National Unemployment Insurance Fraud Task Force (NUIFTF).c
• Colorado’s SWA had 55 active potential fraud investigations as of June 15, 2022, according to an
official with the Colorado Attorney General’s office.
• California’s SWA reported that a total of 1,525 investigations related to UI fraud had been conducted
within the past 15 months, according to a June 2022 press release.
Dollar value of overpayments associated with cases under investigation for fraud.
• As of March 2022, NUIFTF had generated 92 leads related to potential UI fraud since the beginning of
the pandemic, according to an NUIFTF official. Based on these leads, the NUIFTF identified a
cumulative total loss of over $300 million for fiscal year 2021 and over $100 million in fiscal year 2022.
• As of June 2022, DOL OIG UI investigations have resulted in over $850 million in investigative
monetary results. The DOL OIG has also referred over 10,000 fraud matters that do not meet federal
prosecution guidelines back to SWAs for further action.
Applications flagged Description
for potential fraud Measure based on applications for UI benefits flagged as potentially fraudulent through internal controls,
data analytics, or referrals.
Key strengths Key limitations
Computer-aided detection, including comparisons Lack of complete or reliable data for known flags of
to outside records, allows for an efficient review of potential fraud, or fraud schemes for which no flags
cases using multiple indicators or flags. have been developed, may limit ability to identify
May be used by various federal and state entities, potentially fraudulent payments.
which creates opportunities for additional links Design of specific flags may capture more non-
among data sources that could enhance analytic fraudulent activity than measures of cases accepted
capabilities. for investigations.
Design of flags likely varies across implementing
entities.
Examples of fraud-related measures
Count of claims identified through internal controls or data analytics as potentially fraudulent.
• Based on a comparison with records of deceased individuals, incarceration data, and other ineligible
groups, the Colorado state auditor identified 8,200 claims as likely or potentially fraudulent between
March 2020 and April 2021.
Count of total dollars associated with potentially fraudulent claims.
• In September 2022, the DOL OIG reported a total of about $45 billion in potentially fraudulent
payments from March 2020 through April 2022. The OIG identified these potentially fraudulent
payments using data analytics to detect suspicious payments involving multi-state claimants, Social
Security Numbers of the deceased, federal prisoners, and suspicious email accounts.d
• In April 2021, Washington’s state auditor reported that it identified almost $643 million in claims
submitted using stolen identities. In addition, the state auditor reported that it identified over $460
million in potentially fraudulent claims. It identified these claims based on data for certain
characteristics it identified as correlated with fraud, such as evidence of potentially stolen identities.e
• The Colorado state auditor identified $73.1 million in likely or potentially fraudulent payments between
March 2020 and April 2021.f
Source: GAO analysis of fraud-related measures and Nobelus/stock.adobe.com (icons). | GAO-23-105523
a
This analysis is limited to the DOJ cases we identified from public sources, which may not include all
criminal and civil cases charged by DOJ as of July 31, 2022. This is in addition to the individuals who
Page 21 GAO-23-105523 UI Fraud
have already pleaded guilty or been convicted at trial. The number of individuals facing UI fraud-
related charges has continued to grow in the past two years and will likely increase, as these cases
take time to develop. The statute of limitations for mail fraud and wire fraud prosecutions is 5 years
(18 U.S.C. § 3282), except for mail and wire fraud schemes that affect a financial institution, in which
case the statute is 10 years (18 U.S.C. § 3293). Also, based on our analysis, these cases can take
many years to resolve. In technical comments on a draft of this report, DOJ officials indicated that the
Executive Office for United States Attorneys’ (EOUSA) data show 574 individuals faced UI charges
as of July 31, 2022. This does not include data from other DOJ components.
b
In addition to these 54 individuals charged, as noted earlier, nine people have pleaded guilty or been
convicted of UI fraud, and three have been sentenced.
c
NUIFTF is a prosecutor-led, multi-agency task force with representation from federal and state
agencies that collaborate to investigate and prosecute UI fraud.
d
In September 2022, DOL OIG reported an updated amount of potentially fraudulent payments. In
analyses for earlier reporting, DOL OIG reviewed potentially fraudulent payments involving federal
prisoners. However, in its September 2022 reporting, DOL OIG noted that it did not have access to
DOJ’s Bureau of Prisons data to determine an increase in potential fraudulent payments related to
federal prisoners. Alert Memorandum: Potentially Fraudulent Unemployment Insurance Payments in
High-Risk Areas Increased to $45.6 Billion, Report No. 19-22-005-03-315 (September 21, 2022).
e
In technical comments on a draft of this report, officials from the Employment Security Department of
the State of Washington noted that March 2020 yielded a large number of fraudulent claims.
However, the state has since implemented additional controls to reduce fraud.
f
In technical comments on a draft of this report, Colorado Department of Labor and Employment
officials noted that the potential fraud identified by the state auditor was based on fraud indicators
developed by the state auditor. According to officials, the department cross-referenced the potential
fraud found by the auditors with its indicators and found that many instances of fraud had already
been identified and others had been determined not to be fraudulent after investigation. The
department continues to actively investigate reports of fraud.
Taken together, the fraud and fraud-related measures described above
indicate substantial fraud and potential fraud in UI programs during the
pandemic. As described above, fraud measures reported by states
indicate that UI fraud during the pandemic exceeds $4 billion and fraud-
related measures suggest that at least $45 billion in payments have some
indication of potential fraud. However, existing fraud and fraud-related
measures do not reliably indicate the extent of fraud, due to the various
concerns regarding false positives and false negatives described above.
Moreover, because various fraud and fraud-related measures focus on
unique aspects of fraud and potential fraud, use different time periods,
and rely on different approaches, they cannot be added together to
meaningfully reflect the extent of fraud. For example, combining one
source that identifies a number of cases under investigation with another
source that identifies the dollar value of recoveries associated with
adjudicated cases is not meaningful. Combining these sources is not
meaningful because (1) the cases and dollars are not compatible units of
analysis and (2) potential fraud under investigation counts cases that
might be fraud while adjudicated cases count cases that have already
been determined to be fraud. As described in the next section, estimation
can attempt to address both the undercounting of fraud that results from
Page 22 GAO-23-105523 UI Fraud
fraud measures and the uncertainty about the extent of fraud indicated by
fraud-related measures.
Fraud and fraud-related estimates. Estimates can be used to
approximate the extent of fraud and potential fraud beyond what can be
directly counted. For example, estimates attempt to account for the
variation among and limitations of fraud and fraud-related measures, as
described above. As noted above, no national estimate of UI fraud has
been reported that covers all UI programs and the full period of pandemic
spending. However, DOL has reported state-level estimates of fraud in
the regular UI program that, in combination, amount to about $8.5 billion
for performance year 2021, which covers July 1, 2020 through June 30,
2021. 48 This represents a fraud rate of almost 8.6 percent for the regular
UI program for the period.
Fraud and fraud-related estimates vary in the method used, scope, and
purpose. Further, each estimate faces challenges related to validity,
accuracy, and completeness, which often limits the ability to meaningfully
combine them. Available estimates provide additional evidence of
substantial levels of UI fraud and potential fraud during the pandemic, but
none completely or reliably indicates the extent of fraud in UI programs.
Table 3 describes examples of methods used by federal and state entities
to estimate the extent of UI fraud and potential fraud, and the related key
strengths and limitations.
48DOL’s fiscal year reporting is based on information gathered for this performance year.
Page 23 GAO-23-105523 UI Fraud
Table 3: Examples of Methods Used to Estimate Extent of Unemployment Insurance (UI) Fraud and Potential Fraud at the
Federal and State Levels and Key Strengths and Limitations
Extrapolation Description
from rates Estimation based on either calculating or using an existing rate of fraud or potential fraud for one population (i.e.,
selected programs or time periods), and applying it to a new population.a
Key strength
Potential for a rough or notional estimate of fraud or potential fraud even if data on a specific measure or rate are
unavailable.
Key limitations regarding validity, accuracy, and completeness
Limitations regarding validity Limitations regarding accuracy Limitations regarding completeness
There are a wide variety of rates that The limitations associated with Because rates based on fraud and
represent different things (for each type of measure and estimate fraud-related measures and estimates
example, the proportion of flagged still exist (i.e., extrapolation from do not account for all fraud,
applications to the total population, or rates based on measures that extrapolations of these rates similarly
overpayments related to adjudicated undercount fraud will also do not provide a complete picture.
fraud as a proportion of all undercount fraud). Moreover,
payments). Sometimes reports are changes in the nature of programs
not clear about the rate used, so a and fraud may limit the ability of
reader will have to determine if the prior rates to accurately reflect
rate refers to an aspect of current rates.
adjudicated fraud or potential fraud,
and whether the rate represents a
direct measure or estimate.b
Examples
• New Mexico’s state auditor estimated that New Mexico’s state workforce agency (SWA) made $250 million in
overpayments—of which $133 million were the result of fraud—from the week ending on April 18, 2020 through
the week ending on April 17, 2021. To develop this estimate, the state auditor multiplied the 2020 overpayment
rate by the total benefit amounts for regular UI, Federal Pandemic Unemployment Compensation (FPUC),
Pandemic Unemployment Assistance (PUA), and temporary compensation paid.c The state auditor then
multiplied the 2020 fraud rate by the estimated total benefits amount.
• The Department of Labor (DOL) Office of Inspector General (OIG) applied the fiscal year 2021 DOL-reported
improper payment rate of 18.71 percent to its estimate of $872.5 billion in pandemic UI payments. It assumed
that the pandemic rate of improper payments would be this high, in order to conclude that at least $163 billion
in pandemic UI benefits could have been paid improperly.d The OIG further speculated that a significant portion
of these estimated improper payment amounts could be attributable to fraud. However, there is no evidence or
data to quantify the portion attributable to fraud.
The DOL OIG’s improper payment estimate of $163 billion was based on the regular UI program and has
limitations regarding validity, accuracy, and completeness described above. For example, the information
required to claim UI benefits under the PUA program differed greatly from regular UI. One difference was that
claiming UI benefits under the PUA program did not require employer certification for self-employed individuals.
This could have impacted the total number of people who would apply for benefits and the proportion that might
attempt to do so fraudulently, both of which would result in a different rate of fraudulent applications. The lack
of certification required may make it more difficult to validly determine whether a claim was fraudulent, and may
make it hard to determine how different these rates may be. Moreover, the CARES Act allowed PUA applicants
to self-certify their eligibility and did not require them to provide any documentation of self-employment or prior
income.
Page 24 GAO-23-105523 UI Fraud
Manual file Description
review of a Estimation based on a trained reviewer analyzing a statistically-generated sample of claims with results generalized
statistical to the full population.
sample
Key strength
A statistical sample can serve as a valid representation of the entire population. Sampling allows reviewers to invest
more resources in researching sampled cases than would be possible if every item had to be reviewed.
Key limitations regarding validity, accuracy, and completeness
Limitations regarding validity Limitations regarding accuracy Limitations regarding completeness
The definitions and judgments of Because reviews are highly To be complete, a sampling strategy
fraud may vary due to state dependent on the training and skill must be validly generalizable to the
differences, reviewer differences, and of the reviewer (i.e., they are intended population.
other factors. resource intensive), it can be
difficult to complete the required
number of reviews needed for
generalizability, or to thoroughly
and accurately complete each
review.
Reviewers typically have less
information than what would be
gathered during investigative and
adjudicative processes, which may
limit their ability to accurately
determine which cases are
fraudulent.
Examples
• Under DOL’s Benefit Accuracy Measurement (BAM) program, each SWA reviews a number of randomly
selected cases on a weekly basis and reconstructs the UI claims process to assess the accuracy of the
payments that were made.e As part of these assessments, investigators survey or interview the claimant and all
prior or current employers relevant to the claim. Once the investigation is complete, the BAM investigators
categorize overpayments by cause, including fraud.
In its BAM Data Summary for performance year 2021, DOL reported a total estimate of fraud in the regular UI
program of about $8.5 billion with a fraud rate of almost 8.6 percent. The performance year covered July 1,
2020 through June 30, 2021 and the estimate was based on overpayments determined by investigators as
caused by fraud.f
An important limitation is that states may vary in how they define standards for fraud, train investigators, and
validate judgments. As a result, some investigator determinations may be inaccurate, comparisons of rates
across states may be misleading, and aggregation of state estimates into a total should be interpreted with
these considerations in mind.
It is also important to note other limitations to BAM fraud rate estimates during the pandemic. While these are
limitations related to overall improper payment estimates, they also apply to the subset of estimated
overpayments determined by investigators as caused by fraud.
• BAM program did not cover the start of the pandemic due to a 3-month suspension of testing. For
performance year 2020, DOL allowed states to suspend BAM assessments from April through June 2020
to enable the states to reassign staff to address increased claims volume.g As a result of suspending BAM,
the DOL OIG reported that $64.3 billion (74 percent) of the total $86.9 billion of regular UI benefit
payments went untested for that performance year. The DOL OIG report stated that DOL had met all the
statutory criteria for compliance with the Payment Integrity Information Act of 2019 for fiscal year 2020.h
For performance year 2021, DOL fully resumed BAM testing for improper payments.
Page 25 GAO-23-105523 UI Fraud
• BAM program does not include payments for the pandemic UI programs. For performance years 2020 and
2021, the BAM program only included regular UI claims and did not include payments associated with
pandemic UI programs. For fiscal year 2021 improper payment reporting, DOL applied the estimated
improper payment rate from BAM to calculate the estimated improper payment amounts for FPUC and
PEUC. The estimated improper payment amounts for these two programs were incorporated into the
overall estimated improper payment amount for the UI program. However, this overall estimated improper
payment amount for the UI program did not include an estimate for PUA. The CARES Act allowed PUA
applicants to self-certify their eligibility and did not require them to provide any documentation of self-
employment or prior income. In October 2021 we found that this was a factor that increased the possibility
of fraud.i
On July 14, 2022, DOL announced its plan to estimate the rate of improper payments for PUA and to report a
statistically valid national improper payment rate by fall 2022.j However, in its fiscal year 2022 reporting on
paymentaccuracy.gov, DOL stated that in October 2022, the Office of Management and Budget (OMB)
requested that DOL conduct further analysis of the outcomes recorded through the PUA case review process.
In addition, according to DOL, OMB and DOL agreed to collaborate in conducting this additional analysis but
DOL reported that it could not complete this analysis in time to meet the fiscal year 2022 reporting deadline.
Therefore, according to DOL, OMB allowed additional time to conduct this analysis and report on PUA
outcomes in fiscal year 2023.
• DOL OIG statistically sampled and reviewed claims from four selected states to identify pandemic-related UI
funds paid improperly. Of the four states tested, from March 28, 2020, through September 30, 2020, DOL OIG
estimated $9.9 billion of the $71.7 billion (almost 14 percent) in PUA and FPUC benefits were likely paid to
fraudsters.k
• At the state level, an independent accounting firm engaged by the Michigan SWA used a sampling technique to
identify potential UI fraud during the pandemic. Specifically, it drew a sample of 7,741 claims from March 1,
2020 to October 2, 2020 and 7,096 claims from October 3, 2020 to September 30, 2021 using 38 fraud risk
indicators. The firm concluded that the SWA paid out an estimated $8.36 billion to $8.51 billion on potentially
fraudulent claims but avoided paying out $28.7 billion on other potentially fraudulent claims. This estimate did
not attempt to determine what proportion of potentially fraudulent payments were likely to be fraud.
Modeling or Description
forecasting Estimate based on economic, statistical, and simulation models that rely on variables and data from data analytics,
manual file reviews, or other statistical and administrative data.
Key strengths
A wide range of techniques can be used to correct for common technical issues (e.g. impute data, control for
confounding variables, and account for probabilistic uncertainty).
Key limitations regarding validity, accuracy, and completeness
Limitations regarding validity Limitations regarding accuracy Limitations regarding completeness
There are a wide variety of ways to Lack of consistent or complete data Extremely resource intensive to gather
design and implement models and requires assumptions and data from disparate sources and
forecasts, and various design choices imputations that may limit power modify these sources so they can be
have impacts on the ability to validly and accuracy of models. used together.
estimate fraud. Resulting data may retain many of the
limitations associated with each type of
measure, as described above.
Page 26 GAO-23-105523 UI Fraud
Example
• In Kansas, the state auditor’s office used a machine learning technique to estimate UI fraud during the
pandemic. Specifically, it used a neural network—a form of machine learning used to replicate human decision-
making. To train and validate the neural network, state auditor staff manually reviewed a random sample of
1,000 unique claims to identify 26 potential indicators of fraud. Then, the state auditor ran the approximately
1.08 million unique claims received from January 2020 to February 2021 through the neural network. The
neural network classified $380 million as potentially fraudulent, including $309 million that had been flagged by
the state and $71 million not previously flagged.l
Source: GAO analysis of methods used to estimate the extent of UI fraud at the federal and state levels and rawku5/stock.adobe.com (icons). | GAO-23-105523
a
A rate is generally expressed as the proportion of a measured or estimated value relative to an
overall total.
b
Some reports also use improper payment rates, which do not, and are not intended to, reflect fraud
rates.
c
This work was conducted by New Mexico’s Legislative Finance Committee. The committee’s role
includes an audit function. While New Mexico has an Office of the State Auditor, for the purposes of
this report, we refer to the Legislative Finance Committee as a state auditor.
d
The DOL OIG’s 18.71 percent improper payment rate does not include unknown payments. When
unknown improper payments are included, the total improper payment rate is 18.92 percent. Larry D.
Turner, Inspector General, DOL, Office of Inspector General, testimony before the U.S. Senate
Committee on Homeland Security and Governmental Affairs, Number 19-22-003-03-315, March 17,
2022.
e
DOL uses its BAM program to determine the accuracy of UI benefit payments and to estimate the
amount and rate of improper payments, including the amount and rate of overpayments determined
by investigators as due to fraud. The results of the BAM statistical samples are used to estimate
accuracy rates for the populations of paid and denied claims.
f
The BAM Data Summary for performance year 2022 was not available at the time of GAO’s review.
g
DOL’s performance year for reporting improper payment estimates covers July 1 of the previous year
through June 30 of the current year. For example, DOL’s fiscal year 2020 improper payment estimate
generally covers the performance year from July 1, 2019, through June 30, 2020. However, the
sampling and investigation program was suspended for the quarter April 1, 2020, through June 30,
2020, because of operational flexibilities provided to states in response to the pandemic, according to
DOL.
h
In addition, DOL’s OIG reported that DOL received direction from OMB to use the results from the
first three quarters of the program year for its improper payment reporting in fiscal year 2020 and that
DOL’s decision to suspend fourth quarter program year testing was approved by OMB. DOL, OIG,
The U.S. Department of Labor Complied with the Payment Integrity Information Act for FY 2020, but
Reported Unemployment Insurance Information Did Not Represent Total Program Year Expenses,
Report No. 22-21-007-13-001 (Washington, D.C.: August 6, 2021).
i
To help address this risk, the Consolidated Appropriations Act, 2021, enacted in December 2020,
included a requirement for individuals to submit documentation of employment or self-employment
when applying for PUA. Pub. L. No. 116-260, div. N, tit. II, § 241(a), 134 Stat. 1182, 1959-60.
j
Specifically, to calculate the PUA improper payment rate, DOL and state staff stated they would
review a sample of PUA cases from 26 states, including the 10 states with the highest PUA outlays—
representing 74 percent of all PUA outlays—and 16 randomly-selected states. DOL does not have
plans to estimate improper payments for the MEUC program, according to officials, because the
program only operated between January and September of 2021. Officials explained that in
accordance with OMB guidance, DOL is not required to estimate or report improper payments for this
program because it existed for less than one year.
DOL OIG, COVID-19: ETA and States Did Not Protect Pandemic-Related UI Funds from Improper
k
Payments Including Fraud or From Payment Delays, Report No. 19-22-006-03-315 (Washington,
D.C.: September 30, 2022).
l
As of September 2022, the Kansas SWA continues to review issues related to UI fraud.
Page 27 GAO-23-105523 UI Fraud
As explained above, none of these estimates fully reflects the extent of UI
fraud during the pandemic. Further, estimates are not available, alone or
in combination, that cover the entire period of the pandemic or the entire
UI system. Moreover, differences across individual state estimates in the
approaches used, populations assessed, and time periods covered can
prevent these estimates from being meaningfully combined into broader
totals. For example, table 4 summarizes the approaches used,
populations assessed, and time periods covered by three state-level
reports.
Table 4: Summary of Approaches Used, Populations Assessed, and Time Periods Covered by Three State-Level Reports
Source of report Approach used Population assessed Time period covered by reporting
Colorado state auditor Measure of flagged cases Potential Fraud March 2020 to April 20, 2021
using data analytics
Michigan state workforce Estimate based on manual Potential Fraud March 1, 2020 to September 30, 2021
agency file review
New Mexico state auditor Estimate based on Fraud April 18, 2020 to April 17, 2021
extrapolation from rates
Source: GAO summary of elements of three state-level reports on fraud and potential fraud. | GAO-23-105523
As summarized in table 4, the Michigan SWA’s report on fraud includes
an estimate of potential fraud based on a manual file review of a
statistical sample covering claims from March 1, 2020 through September
30, 2021. In contrast, New Mexico’s state auditor applied a primarily pre-
pandemic fraud rate, taken from prior BAM estimates, to claims made
between April 18, 2020 and April 17, 2021, in order to estimate fraud.
The Michigan SWA and the New Mexico state auditor reports used
different approaches and are estimating two different populations.
Specifically, the Michigan SWA’s report estimates potential fraud (the
number of transactions that would have any indicator of fraud if all were
reviewed) and the New Mexico SWA estimates fraud (cases that, if
adjudicated, would likely be fraudulent). Their estimates also have two
different levels of reliability since the Michigan SWA’s report estimate is
based on a review of a statistical sample of pandemic claims, while the
New Mexico state auditor’s estimate is extrapolated from a fraud rate for
primarily pre-pandemic claims. Therefore, attempting to combine these
estimates into a broader total could be misleading due to differences in
both the population estimated and the reliability of the estimate.
The limitations in combining estimates and measures potentially increase
with each additional source. For example, the Colorado state auditor used
Page 28 GAO-23-105523 UI Fraud
data analytics to flag likely or potentially fraudulent benefits payments
between March 2020 and April 20, 2021. Similar to the example above,
the Colorado state auditor assessed potential fraud, which is not
comparable to the New Mexico state auditor’s estimate of fraud, and
would likely make combining these two misleading. Moreover, because
the Colorado state auditor reports a measure of potential fraud, while the
Michigan SWA reports an estimate of fraud, any attempt to combine
these two figures would require careful consideration of differences in
reliability in order to ensure that the result is not misleading.
Due to the lack of consistent and reliable estimates that cover all UI
payments during the pandemic, it is not currently possible to combine
existing estimates and measures to make meaningful statements about
the extent of fraud in UI programs during the pandemic.
DOL’s BAM program uses a consistent methodology across states, which
does allow for meaningful combination of state-level estimates into a
national estimate of about $8.5 billion in fraudulent regular UI payments
for performance year 2021, which equates to an estimated fraud rate of
about 8.6 percent from July 1, 2020 through June 30, 2021. However,
these estimates are incomplete because the BAM program does not
include any pandemic UI program payments. Moreover, the estimates
must be interpreted with caution due to differences across states in how
they determine whether sampled claims are fraudulent.
Other measures and estimates discussed above suggest that the UI
spending excluded from the BAM estimates may have higher rates of
fraud. For example, in September 2022, the DOL OIG reported that it
estimated about $9.9 billion in fraudulent PUA and FPUC payments when
it reviewed four states between the end of March 2020 and the end of
September 2020. 49 The individual fraud rates that the DOL OIG
calculated for the four states under review were generally higher than the
estimated fraud rates reported under the BAM program. This difference
was partially driven by higher estimates of fraud in PUA, which was not
covered by the BAM program. The DOL OIG had previously reported in
October 2020 that the PUA program in particular was at high risk for fraud
49Department of Labor, Office of Inspector General, COVID-19: ETA and States Did Not
Protect Pandemic-Related UI Funds from Improper Payments Including Fraud or From
Payment Delays, Report No. 19-22-006-03-315 (Washington, D.C.: September 30, 2022).
Page 29 GAO-23-105523 UI Fraud
due to its unique program rules and eligibility requirements. 50 The BAM
program also did not cover the first three months of the pandemic, and
some states reported the early pandemic period was associated with a
higher risk for fraud. 51
The above evidence supports the use of the BAM 2021 fraud rate to
roughly extrapolate a lower-bound for the extent of fraud in all UI
programs during the wider pandemic period. Specifically, if the lower
bound of DOL’s estimated national fraud rate (7.6 percent) in the regular
UI program for July 1, 2020, to June 30, 2021, was applied more broadly
to all pandemic payments from roughly April 2020 through December
2021 (about $849 billion), then the estimated total fraud in UI programs
during the pandemic would be greater than $60 billion. 52
As described above, available measures and estimates are incomplete
and do not fully reflect the extent of fraud and potential fraud in UI
programs during the pandemic. The above extrapolation offers a rough
lower bound estimate. In ongoing work, we are evaluating whether and
how fraud and fraud-related measures, in combination with existing fraud
and potential fraud estimates and other data, can be used to make more
comprehensive and precise conclusions about the extent of fraud. These
efforts aim to identify different analytic approaches and related data that
could be used to estimate the amount of fraud in federal programs and
50Department of Labor, Office of Inspector General, Report No. 19-21-001-03-315.
51For example, the California State Auditor reported on the lack of controls in place at the
start of the pandemic. Auditor of the State of California, Employment Development
Department: Significant Weaknesses in EDD’s Approach to Fraud Prevention Have Led to
Billions of Dollars in Improper Benefit Payments, Report 2020-628.2. (Sacramento, CA:
2021).
52This approach relies on data from a manual file review of a statistical sample and falls
under the extrapolation from rates method described in table 3, and should be considered
in line with the limitations of both methods. To help account for these limitations and the
rough nature of our estimate, we rounded down to the nearest $10 billion. To specifically
address the uncertainty arising from the use of statistical sampling, we used the lower limit
of the performance year 2021 BAM fraud rate estimate. The available measures and
estimates support the use of the 2021 BAM fraud rate as an approximate lower, but not
upper limit of the fraud rate for all UI programs and the wider period of pandemic
spending. The actual amount of fraud in UI programs during the pandemic may be
substantially higher than the estimated lower limit reported here. Extrapolation is a
technique that can offer a rough or notional estimate of fraud or potential fraud even if
data on a specific measure or rate are unavailable, but may have limitations related to
validity, accuracy, and completeness.
Page 30 GAO-23-105523 UI Fraud
operations, including UI. We intend to report on these efforts in the future,
and to potentially include specific estimates of fraud, as feasible.
DOL Has Taken
Steps to Address UI
Fraud Risks but Has
Not Designed and
Implemented a
Strategy to Manage
These Risks
DOL Has Taken Steps to DOL has taken various steps to address fraud risks in the UI system.
Address UI Fraud Risks Specifically, DOL has provided SWAs with fraud-related guidance, tools
and resources, and funding. In addition, DOL collaborated with the DOL
OIG to share information on emerging UI fraud issues and coordinate
fraud prevention and recovery efforts.
Guidance. During the pandemic, DOL provided states with fraud-related
guidance through two types of advisory documents—UI Program Letters
(UIPL) and Training and Employment Notices (TEN). Specifically, DOL
issued advisory documents containing fraud-related information on
various topics covering fraud prevention, detection, and response. For
example,
• Fraud prevention. In TEN No. 03-20, issued in August 2020, DOL
encouraged states to share information on existing and emergent
fraud schemes. Further, this guidance stated that timely
communication of this information is critical to preventing fraud.
• Fraud detection. In TEN No. 05-20, issued in September 2020, DOL
provided states with guidance on resources available to identify when
a claimant has returned to work and is continuing to claim UI benefits.
Specifically, DOL provided information on how states can use the
National Directory of New Hires and State Directory of New Hires
Page 31 GAO-23-105523 UI Fraud
databases to compare in-state and out-of-state employment
information to UI payments made to claimants. 53
• Fraud response. In UIPL No. 04-17, change 1, issued in August 2021,
DOL directed states to report instances of potential UI fraud to the
DOL OIG. In addition, DOL issued two guidance documents providing
instructions on the recovery of federally funded UI benefits. 54
Specifically, in May 2021, DOL provided guidance to states on the
proportional distribution methodology for recovering UI benefits held
by banks and financial institutions as a result of suspicious or
potentially fraudulent activity. DOL instructed states to request that
banks and financial institutions return held funds paid by multiple
states into the same bank account, with respect to the federally
funded UI programs, in an amount proportionate to what the states
contributed. DOL also encouraged states to make this request of
banks with respect to state-funded UI programs. In February 2022,
DOL provided states instructions on circumstances under which
states may waive recoveries of established overpayments made in
CARES Act UI programs. DOL also noted that recovery activities for
fraudulent overpayments may never be waived.
DOL officials noted that implementing and administering the CARES Act
UI programs was dynamic and challenging and that they tried to meet
state needs for guidance as quickly as possible. Also, as new legislation
was enacted, DOL needed to update program guidance. For example, the
Consolidated Appropriations Act, 2021 added several new program
integrity features to the CARES Act UI programs. 55 One of the new
program integrity features generally required PUA claimants to provide
documentation substantiating their prior employment or self-employment.
It also required PUA claimants to recertify with their state each week that
they continued to meet the eligibility requirement of not being able to work
as a result of COVID-19. 56 In response, in December 2020 DOL issued
53DOL encourages states to use the National Directory of New Hires and State Directory
of New Hires to identify overpayments, including fraud overpayments, due to unreported
or under-reported earnings by claimants while they claim UI benefits.
54UIPL Nos. 19-21 and 20-21, change 1.
55Pub. L. No. 116-260, div. N, tit. II §§ 241(a), 134 Stat. 1182, 1959-1960, 1963.
56In addition, it required states to have procedures for identity verification and for timely
payment of PUA benefits, to the extent reasonable and practicable. Pub. L. No. 116-260,
div. N, tit. II, § 242(a), 134 Stat. 1182, 1960.
Page 32 GAO-23-105523 UI Fraud
UIPL No. 09-21, which provided states with instructions for implementing
the new UI-related provisions of the law. 57
Tools and resources. To assist states in their efforts to prevent and
detect UI fraud and recover fraudulent and other improper payments,
DOL provided funding for the UI Integrity Center, operated by NASWA.
The UI Integrity Center supports SWAs in adopting and implementing
strategies to address fraud in UI programs at no cost to states. For
example, the UI Integrity Center offers training to states, including online
certificate programs with lessons available for state staff including
program leadership, UI operations integrity, fraud investigations, and data
analysis. The UI Integrity Center also operates an online, searchable,
knowledge-sharing platform that provides a repository of all its resources
including
• a model of state operational processes,
• promising state practices, and
• recommendations to strengthen UI program integrity.
During the pandemic, DOL and the UI Integrity Center held webinars for
states on issues related to UI fraud, including identity verification and
assessing fraud penalties.
Through its guidance documents, DOL encourages states to use the
tools, resources, and services available through the UI Integrity Center.
Some of the UI Integrity Center’s tools are contained in the Integrity Data
Hub. The Integrity Data Hub provides a secure, robust, centralized, multi-
state data system where participating state UI agencies can regularly
submit claims for multi-state cross-matching. Also via the Integrity Data
Hub, states have access to a suspicious actor repository, foreign internet
protocol address detection tool, and fraud alerts to facilitate information
sharing about fraud schemes between states and the DOL OIG.
In addition, during the pandemic, DOL supported the development and
acquisition of new tools and resources for the UI Integrity Center to
enhance the Integrity Data Hub, including
• An identity verification solution. DOL provided funding to procure and
implement an identity verification solution, which became available to
states in July 2020, according to DOL officials. This service provides
57UIPL No. 09-21.
Page 33 GAO-23-105523 UI Fraud
new datasets for the UI Integrity Center’s Integrity Data Hub to
conduct enhanced UI claimant identity verification by states to prevent
fraudulent claims from being paid based on false identities. It also
contains a cross-match with the Social Security Administration (SSA)
Death Master File to identify the use of a deceased person’s Social
Security number being used to file for benefits. As of October 2022,
there are 41 states using the identity verification service, according to
DOL officials.
• A bank account verification service. DOL provided funding to procure
and implement a bank account verification service, which became
available to states in February 2022, according to DOL officials. This
service aids states in proactively identifying and authenticating bank
account information provided by a UI claimant by validating the
account status and ensuring said claimant is the owner or authorized
user prior to initiating the UI benefit payment. As of October 2022, 31
states are using the bank account verification service, according to a
DOL official.
• An incarceration data exchange. A DOL official informed us that the
agency began working with SSA in August 2020 to establish a secure
data exchange that allows states to cross-match UI claims data with
incarceration records. This incarceration data exchange provides
states with the ability to cross-match UI claims information with SSA’s
prisoner data to aid states in determining if an individual meets UI
eligibility criteria. 58 In February 2021, DOL OIG issued an alert
memorandum stating that the Social Security numbers of over 13,000
potentially ineligible federal prisoners were used in attempts to file UI
claims. These attempts equaled more than $98 million in UI benefits.
While some states have access to various local, state, or federal
incarceration cross-matches, DOL encouraged all states to regularly
cross-match UI claims against prisoner records to ensure UI benefits
are only paid to eligible individuals. 59 As of August 2022, one state is
currently using the new incarceration data exchange, and DOL and
SSA are working with at least 10 states that have submitted a request
to access the incarceration data, according to DOL. According to a
DOL official, of the states not currently pursuing incarceration cross-
58To be eligible for UI benefits, an individual must be able, available, and actively seeking
work. Incarcerated individuals do not typically meet the eligibility requirements to receive
UI benefit payments as they would not be able or available for work while incarcerated.
UIPL No. 01-22.
59According to DOL officials, as of August 2022, twelve states receive incarceration data
through direct agreements with SSA and are not expected to request these data through
the secure data exchange facilitated by DOL.
Page 34 GAO-23-105523 UI Fraud
matches through this data exchange with SSA, many are using third-
party vendor services that provide incarceration data to states.
Funding. During the pandemic, DOL provided grant opportunities to
states to improve UI systems and processes, including addressing fraud
prevention and detection. As of August 2022, DOL provided states with
grants up to $525 million in CARES Act funds to address fraud in
pandemic UI programs and $140 million in American Rescue Plan Act of
2021 funds to address fraud in all UI programs, according to DOL
officials. 60 For example, as we found in June 2022, Arizona, Florida, and
Michigan used grant funding to hire program integrity staff. Some of these
staff have focused on identity theft cases or detecting and recovering
overpayments, according to DOL officials. 61 States have also used this
funding to engage third-party vendors to conduct fraud risk and
cybersecurity assessments of states’ UI systems and subscribe to identity
verification and fraud risk scoring services, according to DOL officials.
In addition, with separate funding provided by the American Rescue Plan
Act of 2021, DOL provided grant opportunities of up to $200 million to
support states in improving UI systems and processes including
addressing fraud prevention and detection. Through these grants,
multidisciplinary expert teams analyze state UI systems and process
challenges, and work with states to identify areas to enhance their
existing efforts. 62 The expert teams are to develop customized and
actionable recommendations for the states to implement using the grant
funds. In addition to implementing the expert teams’ recommendations,
states may request permission from DOL to use any excess funds from
60As of July 22, 2022, DOL announced five grants totaling $665,000,000 for fraud
prevention, detection, investigation, and recovery activities in pandemic UI programs and
the regular UI program. UIPL No. 28-20; UIPL No. 28-20, change 1; UIPL No. 28-20,
change 2; UIPL 28-20, change 4; and UIPL No. 22-21.
61GAO, Unemployment Insurance: Pandemic Programs Posed Challenges, and DOL
Could Better Address Customer Service and Emergency Planning, GAO-22-104251
(Washington, D.C.: June 7, 2022).
62Each expert team is comprised of experts including a fraud specialist, equity/customer
experience specialist, UI program specialist, business intelligence analysts, computer
systems engineer/architect, and project manager. See Grant Opportunity to Support
States Following a Consultative Assessment for Fraud Detection and Prevention,
Promoting Equitable Access, and Ensuring the Timely Payment of Benefits, including
Backlog Reduction, for all Unemployment Compensation (UC) Programs, UIPL No. 02-22
(Washington, D.C.: November 2, 2021).
Page 35 GAO-23-105523 UI Fraud
these grants to further improve UI systems and processes, according to
DOL guidance. 63
As of August 2022, 13 states—Alabama, Colorado, Connecticut,
Delaware, Iowa, Kansas, Kentucky, Nebraska, Nevada, Pennsylvania,
Virginia, Washington, and Wisconsin—had received final
recommendations from expert teams in analyzing state UI systems and
process challenges, according to DOL officials. 64 Expert teams made
recommendations related to fraud prevention and detection to all 13
states. For example, these recommendations include:
• Improving identity verification. Expert teams recommended improving
identity verification in 11 states for which they made fraud prevention
and detection recommendations. For example, an expert team
recommended that one state implement elements of the National
Institute of Standards and Technology’s identity verification standards
when a claimant submits an application to optimize its fraud
prevention and detection efforts. 65 Specifically, the expert team
recommended that the state integrate its identity verification solution
into its overall claims process. 66 The recommendation notes that DOL
strongly encourages states to adopt an array of solutions and
techniques to detect and fight fraud and to have robust strategies in
place to verify the identity of individuals applying for UI benefits. 67
• Implementing NASWA’s Behavioral Insights Toolkit. An expert team
recommended that one state use NASWA’s Behavioral Insights
Toolkit—a collection of resources, articles, templates, and how-to
information—to simplify communications to claimants to help them
quickly respond to information requests that would deter would-be bad
63UIPL No. 02-22.
64In addition, five other states—Arizona, Illinois, Michigan, New Hampshire, and Oregon—
were expected to receive expert teams’ recommendations by September 2022, according
to DOL officials. In summer 2022, another six states—Idaho, Indiana, Maryland, Missouri,
Rhode Island, and Wyoming—began working with expert teams. In addition, DOL is
currently recruiting additional states to begin work in winter 2022.
65In UIPL No. 22-21, DOL encouraged states to implement National Institute of Standards
and Technology-compliant identity proofing requirements before claimants start filling out
UI claims applications and for re-accessing their accounts.
66Currently, this SWA conducts identity verification of claims manually.
67UIPL Nos. 23-20; 28-20; 28-20, change 1; 28-20, change 2; and 22-21.
Page 36 GAO-23-105523 UI Fraud
actors or uninformed claimants from continuing to file or report wages
incorrectly.
• Implementing risk analytics. An expert team recommended that one
state implement data analytics to group UI claims into risk categories
based on their probability of fraud. Under this method, the SWA would
use data analytics to assign a single aggregate fraud risk score to
each claim. Claims with the highest risk scores are triaged and
referred for investigation to determine the likelihood of fraudulent
activity. According to the expert team, this action could provide an
additional layer of fraud detection. The expert team concluded that
states that utilize a fraud risk scoring method see increased staff
productivity, as investigations are limited to the most egregious risks
and staff need less time to evaluate claims with potential fraud. In 8 of
the 13 states, an expert team recommended automating aspects of
the claims review process. Specifically, in one state, an expert team
noted that the SWA’s claims review process had a critical gap in fraud
prevention and detection and demonstrated the need for an
automated data cross-matching tool that provides access to an
expanded set of UI-specific data. To address the state’s limited pool
of external data available for cross-matching, the expert team
recommended the SWA integrate resources from the Integrity Data
Hub directly into its case management system to create a centralized
repository of fraud prevention and detection information for
investigators.
DOL’s national and regional offices are working with states on
implementing the expert teams’ recommendations, according to DOL
officials. For example, DOL is providing technical assistance and helping
states determine how to prioritize the recommendations based on the
state’s needs. Also, DOL officials told us that NASWA is offering project
management support to states that have received expert teams’
recommendations at no additional cost to the states. In June 2022, DOL
published information on trends it identified during the first year of expert
teams as a resource for all states. 68 For example, DOL identified trends in
expert teams’ recommendations such as the need to integrate case
management systems to help prioritize investigations and automate task
assignments. According to DOL, implementing this type of
recommendation would enable states to monitor the status of claims in
real time, and streamline analytics and reporting features.
68https://oui.doleta.gov/unemploy/pdf/TigerTeamCohortTrendsJune_2022.pdf. Accessed
August 24, 2022.
Page 37 GAO-23-105523 UI Fraud
Collaboration with DOL OIG. According to DOL officials, DOL meets
regularly with the DOL OIG to discuss emerging UI fraud issues,
streamline communication with states, and coordinate fraud prevention
and recovery efforts. For example, DOL is working with the UI Integrity
Center and DOL OIG to provide the DOL OIG with data that could
indicate fraudulent activity. In addition, in January 2022, regional offices
within the Employment and Training Administration and DOL OIG began
hosting joint quarterly conference calls with states to discuss fraud trends
and prosecution activities.
DOL Has Not Designed While DOL has taken steps to address UI fraud risks, as described
and Implemented an above, its approach has been ad hoc. Specifically, it has not designed
and implemented an antifraud strategy to guide its actions based on a
Antifraud Strategy Based
fraud risk profile in alignment with leading practices in the Fraud Risk
on a Fraud Risk Profile Framework. 69
The Fraud Risk Framework calls for a strategic approach for assessing
and managing fraud risks. Specifically, the third component of the Fraud
Risk Framework—design and implement—calls for federal managers to
design and implement a strategy with specific control activities to mitigate
assessed fraud risks and collaborate to help ensure effective
implementation. Managers who effectively manage fraud risks develop
and document an antifraud strategy that describes the program’s
approach for addressing the prioritized fraud risks identified during the
fraud risk assessment.
In October 2021, we recommended that DOL assess fraud risks in
alignment with the leading practices outlined in the Fraud Risk
Framework. 70 Specifically, we recommended that DOL
69Although the UI program is a federal-state partnership, the Fraud Risk Framework states
that managers of federal programs maintain primary responsibility for enhancing program
integrity. However, the Fraud Risk Framework also notes the importance of working with
stakeholders on fraud risk management, which, for the UI program, includes states.
70We also recommended that DOL designate a dedicated entity and document its
responsibilities for managing the process of assessing fraud risks to the UI program. This
entity should have, among other things, clearly defined and documented responsibilities
and authority for managing fraud risk assessment and facilitating communication among
stakeholders regarding fraud-related issues. In August 2022, DOL officials told us that
they are documenting a dedicated entity’s responsibilities for managing fraud risks.
Page 38 GAO-23-105523 UI Fraud
(1) identify inherent fraud risks facing the UI program; 71
(2) assess the likelihood and impact of inherent fraud risks facing the
program;
(3) determine fraud risk tolerance for the program; 72
(4) examine the suitability of existing fraud controls in the program
and prioritize residual fraud risks; and
(5) document the fraud risk profile for the program.
In August 2022, DOL officials told us that DOL’s Office of Unemployment
Insurance in collaboration with DOL’s Office of the Chief Financial Officer
began implementing these recommendations. This work remains
incomplete. DOL officials told us that DOL’s Office of Unemployment
Insurance is currently developing its fraud risk management process,
including further identifying and documenting inherent risks, impacts,
fraud likelihood, and fraud tolerance in the UI system. In addition, DOL
officials stated they are in the process of developing a fraud risk profile for
the UI program in alignment with the leading practices in the Fraud Risk
Framework.
As discussed in the Fraud Risk Framework, leading practices related to
designing and implementing an antifraud strategy include (1) using the
fraud risk profile to help decide how to allocate resources to respond to
residual fraud risks; and (2) developing, documenting, and
communicating an antifraud strategy to employees and stakeholders that
describes a program’s activities for preventing, detecting and responding
to fraud.
As explained in the Fraud Risk Framework, effective managers of fraud
risks use the program’s fraud risk profile to help decide how to allocate
71According to Federal Internal Control Standards, inherent risk is the risk to an entity prior
to considering management’s response to the risk (see GAO-14-704G, 7.03).
72According to Federal Internal Control Standards, risk tolerance is the acceptable level of
variation in performance relative to the achievement of objectives (see GAO-14-704G,
6.08).
Page 39 GAO-23-105523 UI Fraud
resources to respond to residual fraud risks. The responses to fraud risk
may include actions to accept, reduce, share, or avoid the risk. 73
An antifraud strategy describes existing fraud control activities as well as
any new control activities a program may adopt to address residual fraud
risks—the risks that remain after inherent risks have been mitigated by
existing control activities. The antifraud strategy may be agency-wide or
directed at the individual program level. Effective antifraud strategies, as
described in the Fraud Risk Framework, reflect the key elements
explained in table 5.
Table 5: Key Elements of an Antifraud Strategy as Described in the Fraud Risk
Framework
Who is responsible for fraud Establish roles and responsibilities of those involved in
risk management activities? fraud risk management activities, such as the antifraud
entity and external parties responsible for fraud
controls, and communicate the role of the Office of
Inspector General to investigate potential fraud.
What is the program doing to Describe the program’s activities for preventing,
manage fraud risks? detecting, and responding to fraud, as well as
monitoring and evaluation.a
When is the program Create timelines for implementing fraud risk
implementing fraud risk management activities, as appropriate, including
management activities? monitoring and evaluations.
Where is the program focusing Demonstrate links to the highest internal and external
its fraud risk management residual fraud risks outlined in the fraud risk profile.
activities?
Why is fraud risk management Communicate the antifraud strategy to employees and
important? other stakeholders, and link antifraud efforts to other
risk management activities, if any.
Source: GAO-15-593SP. | GAO-23-105523
a
According to Federal Internal Control Standards, control activities are the policies, procedures,
techniques, and mechanisms that enforce managers’ directives to achieve the program’s objectives
and address related risks. Broadly speaking, the antifraud strategy itself can be viewed as a
preventive control activity, although it can inform other control activities, such as the content of fraud-
awareness training or the design of system edit checks. The antifraud strategy describes existing
fraud control activities, as well as any new control activities a program may have planned or adopted
73In general, managers accept certain risks that are within their defined risk tolerance and
take one of the other three actions in response to prioritized residual fraud risks that
exceed their defined risk tolerance. Specifically, managers may allocate resources to
prevent or detect fraud risks that exceed their risk tolerance but they may decide not to
allocate resources to further reduce unlikely, low-impact risks that fall within their risk
tolerance. Moreover, while managers may accept certain fraud risks, responding
appropriately to instances of actual fraud is essential for ensuring the continued
effectiveness of fraud risk management activities.
Page 40 GAO-23-105523 UI Fraud
to address any residual fraud risks. GAO, Standards of Internal Control in the Federal Government,
GAO-14-704G (Washington, D.C.: September 10, 2014).
DOL’s actions described above are steps to prevent, detect, and respond
to fraud, but DOL lacks a fraud risk profile to better inform an antifraud
strategy that targets fraud risks in a prioritized manner. In August 2022,
DOL officials told us they are in the process of developing a fraud risk
profile but they have not yet finalized the profile. Until DOL completes
these efforts and uses them to inform its antifraud strategy, consistent
with our prior recommendations, it cannot be sure it is identifying,
assessing, and prioritizing risks effectively. For example, this could
include fraud risks identified during the pandemic that may continue to
exist in the regular UI program after the expiration of the temporary UI
programs.
Designing and implementing an antifraud strategy that conforms to
leading practices in the Fraud Risk Framework would help DOL have
greater assurance as to whether existing fraud control activities are
efficiently and effectively addressing UI fraud risks within an established
tolerable level. Without an antifraud strategy based on a fraud risk profile,
DOL is not able to ensure that it is strategically addressing its most
significant fraud risks in alignment with the Fraud Risk Framework.
The unprecedented demand for UI benefits and the urgency with which
Conclusions states implemented the new pandemic programs increased UI fraud and
fraud risks.
While currently available measures and estimates do not reflect the full
extent of fraud, they provide important information on fraud risks facing
the UI program. Some fraud risks identified in the pandemic UI programs
may continue to exist in the regular UI program after the expiration of the
temporary UI programs. DOL has taken steps to address UI fraud and
fraud risks, including issuing guidance and distributing funding. However,
DOL has not developed and documented an antifraud strategy to guide
these actions in a prioritized manner.
While DOL is in the process of developing its fraud risk management
program, our six recommendations from October 2021 related to fraud
risk management and assessing fraud risks remain open. DOL must
implement these recommendations to be positioned to create and
execute an antifraud strategy, consistent with leading practices, that
targets fraud risks in a prioritized manner. Without these efforts, DOL
Page 41 GAO-23-105523 UI Fraud
cannot ensure that it is prioritizing UI fraud risks effectively in alignment
with the Fraud Risk Framework.
The Secretary of Labor should design and implement an antifraud
Recommendation for strategy for UI based on a fraud risk profile consistent with leading
Executive Action practices as provided in the Fraud Risk Framework. (Recommendation 1)
We provided a draft of this report to DOL, DOJ, and DHS for review and
Agency Comments comment. DOL provided written comments, which are reproduced in
and Our Evaluation appendix IV. DOL also provided technical comments, which we
incorporated as appropriate.
In its written comments, DOL noted that it develops, executes, and
updates a strategic plan for the UI system, which includes efforts to
address identified fraud risks and reduce improper payments. In addition,
DOL stated that it continues to explore, research, identify, invest in, and
provide states with new tools, resources, and guidance to help combat
the continually changing and emerging types of sophisticated fraud
impacting the UI system.
DOL partially agreed with our recommendation to design and implement
an antifraud strategy for UI based on a fraud risk profile. It noted that the
department is in the process of developing a UI fraud risk profile
consistent with leading practices as provided in GAO’s Fraud Risk
Framework and working to implement GAO’s previous recommendations.
However, it did not concur with GAO’s assertion that these
recommendations and the fraud risk profile must be completed before the
implementation of new antifraud strategies. We believe that developing,
documenting, and communicating an antifraud strategy can be done
concurrently with other antifraud efforts. However, to align the antifraud
strategy with leading practices, DOL should use the fraud risk profile to
help decide how to allocate resources and respond to residual fraud risks
in a targeted and prioritized manner.
DOJ provided technical comments which we incorporated as appropriate.
DHS had no comments on this report.
We also provided excerpts of this report to officials from California,
Colorado, Kansas, Michigan, New Mexico, Pennsylvania, and
Washington for review and comment and made technical corrections or
clarifications as needed.
Page 42 GAO-23-105523 UI Fraud
As agreed with your offices, unless you publicly announce the contents of
this report earlier, we plan no further distribution until 30 days from the
report date. At that time, the report will be available at no charge on the
GAO website at https://www.gao.gov.
If you or your staff have any questions about this report, please contact
Seto Bagdoyan, (202) 512-6722, BagdoyanS@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 V.
Seto J. Bagdoyan
Director, Forensic Audits and Investigative Service
Jared B. Smith
Director, Applied Research and Methods
Page 43 GAO-23-105523 UI Fraud
List of Requesters
The Honorable Mike Crapo
Ranking Member
Committee on Finance
United States Senate
The Honorable Rob Portman
Ranking Member
Committee on Homeland Security and Governmental Affairs
United States Senate
The Honorable James Comer
Ranking Member
Committee on Oversight and Reform
House of Representatives
The Honorable Kevin Brady
Ranking Member
Committee on Ways and Means
House of Representatives
Page 44 GAO-23-105523 UI Fraud
Appendix I: Open Unemployment Insurance–
Appendix I: Open Unemployment Insurance–
Related Recommendations to the Department
Related Recommendations to the
of Labor
Department of Labor
Table 6 below lists GAO’s 19 open recommendations to the Department
of Labor to improve the Unemployment Insurance system. The first five
recommendations listed warrant priority attention from heads of key
departments or agencies because implementation could:
• save large amounts of money;
• improve congressional and executive branch decision making on
major issues;
• eliminate mismanagement, fraud, and abuse; or
• ensure that programs comply with laws and funds are legally spent.
As of December 15, 2022, two of the five priority recommendations
remained open for 4 years.
Table 6: GAO’s 19 Recommendations to the Department of Labor (DOL) to Improve the Unemployment Insurance (UI) System,
Open as of December 15, 2022
Report No.,
No. Date Recommendation to DOL
1 GAO-22-104438, (priority) The Secretary of Labor should ensure the Office of Unemployment Insurance examines
June 7, 2022 and publicly reports on the extent and potential causes of racial and ethnic inequities in the
receipt of Pandemic Unemployment Assistance (PUA) benefits, as part of the agency’s efforts to
modernize UI and improve equity in the system. The report should also address whether there is
a need to examine racial, ethnic, or other inequities in regular UI benefit receipt, based on the
PUA findings.
2 GAO-22-105051, (priority) The Secretary of Labor should examine the suitability of existing fraud controls in the UI
October 27, 2021 program and prioritize residual fraud risks.
3 GAO-21-191, (priority) The Secretary of Labor should ensure the Office of Unemployment Insurance pursues
November 30, 2020 options to report the actual number of distinct individuals claiming benefits, such as by collecting
these already available data from states, starting from January 2020 onward.*
4 GAO-18-486, (priority) The Assistant Secretary of DOL’s Employment and Training Administration should
August 22, 2018 provide states with information about its determination that the use of state formal warning
policies is no longer permissible under federal law.
5 GAO-18-486, (priority) The Assistant Secretary of DOL’s Employment and Training Administration should
August 22, 2018 clarify information on work search verification requirements in its revised Benefit Accuracy
Measurement procedures. The revised procedures should include an explanation of what DOL
considers to be sufficient verification of claimants’ work search activities.
6 GAO-22-105162, The Secretary of Labor should develop and execute a transformation plan that meets GAO’s
June 7, 2022 high risk criteria for transformation; the plan should outline coordinated and sustained actions to
address known issues related to providing effective service and mitigating financial risk,
including ways to demonstrate improvements. Planned actions may include addressing audit
recommendations, and determining whether legislative changes are needed, as appropriate.
Planned actions may also include achieving quantifiable results in reducing improper payment
rates, including those related to fraud; improving efficiency in claims processing and restoring
pre-pandemic payment timeliness levels; better reaching current worker populations; and
enhancing equity in benefit distribution.
Page 45 GAO-23-105523 UI Fraud
Appendix I: Open Unemployment Insurance–
Related Recommendations to the Department
of Labor
Report No.,
No. Date Recommendation to DOL
7 GAO-22-104438, The Secretary of Labor should study and advise the Congress and other policymakers on the
June 7, 2022 costs, benefits, and risks of various options to systematically support self-employed and
contingent workers during periods of involuntary unemployment outside of declared disasters,
including considering options’ feasibility and approach to fraud prevention.
8 GAO-22-104251, The Secretary of Labor should ensure the Office of Unemployment Insurance review the
June 7, 2022 customer service challenges that states faced during the pandemic, identify comprehensive
information on customer service best practices, and provide states with this information to
assist them in improving service delivery.
9 GAO-22-104251, The Secretary of Labor should ensure the Office of Unemployment Insurance assesses lessons
June 7, 2022 learned from the pandemic to inform its future disaster responses efforts and support the
Congress on ways to address future emergencies.
10 GAO-22-105051, The Secretary of Labor should designate a dedicated entity and document its responsibilities
October 27, 2021 for managing the process of assessing fraud risks to the UI program, consistent with leading
practices as provided in our Fraud Risk Framework. This entity should have, among other
things, clearly defined and documented responsibilities and authority for managing fraud risk
assessments and for facilitating communication among stakeholders regarding fraud-related
issues.
11 GAO-22-105051, The Secretary of Labor should identify inherent fraud risks facing the UI program.
October 27, 2021
12 GAO-22-105051, The Secretary of Labor should assess the likelihood and impact of inherent fraud risks facing
October 27, 2021 the UI program.
13 GAO-22-105051, The Secretary of Labor should determine fraud risk tolerance for the UI program.
October 27, 2021
14 GAO-22-105051, The Secretary of Labor should document the fraud risk profile for the UI program.
October 27, 2021
15 GAO-21-387, The Secretary of Labor should ensure the Office of Unemployment Insurance collects data from
March 31, 2021 states on the amount of overpayments waived in the PUA program, similar to the regular UI
program.*
16 GAO-21-265, The Secretary of Labor should ensure the Office of Unemployment Insurance collects data from
January 28, 2021 states on the amount of overpayments recovered in the PUA program, similar to the regular UI
program.*
17 GAO-18-633, The Secretary of Labor should update agency guidelines to ensure that it clearly informs states
September 4, 2018 about the range of allowable profiling approaches.
18 GAO-18-486, The Assistant Secretary of DOL’s Employment and Training Administration should monitor
August 22, 2018 states’ efforts to discontinue the use of formal warning policies.
19 GAO-18-486, The Assistant Secretary of DOL’s Employment and Training Administration should monitor
August 22, 2018 states’ compliance with the clarified work search verification requirements.
Source: GAO. | GAO-23-105523
*This recommendation is partially addressed.
Page 46 GAO-23-105523 UI Fraud
Appendix II: State Reports on
Appendix II: State Reports on Unemployment
Insurance (UI) Fraud and Potential Fraud
Unemployment Insurance (UI) Fraud and
Potential Fraud
To address our first objective, we contacted state workforce agencies
(SWA), state auditors, and state Attorneys General offices. We asked
these entities to identify reports and other reporting mechanisms in place
from state entities related to measuring or estimating the extent of fraud
and potential fraud in UI programs during the pandemic. The scope of our
review was from March 2020—the beginning of the pandemic—through
March 2022. These are the two most recent years available at the time of
our selection. Throughout this report, we refer to measures as counts of
detected activities, and to estimates as projections or inferences based
on measures, assumptions, or analytical techniques. 1
We received 31 state reports from 18 states—issued from March 2020
through March 2022—that discussed fraud or potential fraud in UI
programs. We reviewed these reports and identified those that contained
a measure or estimate that indicates the extent of fraud. Of those, we
identified reports with sufficient information for us to determine the
methodology used to arrive at the measure or estimate. For these
remaining reports, we summarized the methodologies used, along with
strengths and limitations of each as they relate to determining the extent
of fraud. We selected examples from these reports to illustrate the variety
of fraud and fraud-related measures and estimates.
Arizona
1. Arizona Auditor General. State of Arizona: Report on Internal Control
and on Compliance, Year Ended June 30, 2020. A Report to the
Arizona Legislature. Phoenix, AZ: 2021.
California
2. Auditor of the State of California. Employment Development
Department: EDD’s Poor Planning and Ineffective Management Left It
Unprepared to Assist Californians Unemployed by COVID-19
Shutdowns. Report 2020-128/628.1. Sacramento, CA: 2021.
3. Auditor of the State of California. Employment Development
Department: Significant Weaknesses in EDD’s Approach to Fraud
Prevention Have Led to Billions of Dollars in Improper Benefit
Payments. Report 2020-628.2. Sacramento, CA: 2021.
1Estimates are often used when direct measures are unavailable, incomplete, or
unreliable.
Page 47 GAO-23-105523 UI Fraud
Appendix II: State Reports on Unemployment
Insurance (UI) Fraud and Potential Fraud
Colorado
4. Colorado Office of the State Auditor. Department of Labor and
Employment: Unemployment Insurance Benefits Performance Audit
(Public Report). Denver, CO: 2021.
5. Colorado Office of the State Auditor. State of Colorado: Statewide
Single Audit, Fiscal Year Ended June 30, 2020. Denver, CO: 2021.
Florida
6. State of Florida Auditor General, Reemployment Assistance Claims
and Benefits Information System (CONNECT). Report 2021-169.
Tallahassee, FL: 2021.
Illinois
7. Auditor General, State of Illinois. State of Illinois Department of
Employment Security: Individual Nonshared Proprietary Fund
Financial Statements For The Year Ended June 30, 2021.
Schaumburg, IL: 2022.
Kansas
8. Kansas Legislative Division of Post Audit. Evaluating the Kansas
Department of Labor’s Response to COVID-19 Unemployment Claims
(Part 1). Report R-21-003. Topeka, KS: 2021.
9. Kansas Legislative Division of Post Audit. Evaluating the Kansas
Department of Labor’s Response to COVID-19 Unemployment Claims
(Part 2). Report R-21-011. Topeka, KS: 2021.
Kentucky
10. Auditor of Public Accounts. Report of the Statewide Single Audit of the
Commonwealth of Kentucky Volume I For The Year Ended June 30,
2021. Frankfort, KY: 2022.
Louisiana
11. Louisiana Legislative Auditor. Improper Payments in the
Unemployment Insurance Program: Ineligible Recipients Based on
State Employment, Louisiana Workforce Commission. Baton Rouge,
LA: 2022.
12. Louisiana Legislative Auditor. Improper Payments in the
Unemployment Insurance Program: Overpayments and Rule
Violations, Louisiana Workforce Commission. Baton Rouge, LA: 2021.
Page 48 GAO-23-105523 UI Fraud
Appendix II: State Reports on Unemployment
Insurance (UI) Fraud and Potential Fraud
13. Louisiana Legislative Auditor. Louisiana Legislative Auditor 2022
Annual Report, Key Audit Issues, Act 461 Report. Baton Rouge, LA:
2022.
14. Louisiana Legislative Auditor. Louisiana Workforce Commission. State
of Louisiana Financial Audit Services Management Letter. Baton
Rouge, LA: 2021.
15. Louisiana Legislative Auditor, Performance Audit Services, Data
Analytics Unit. Improper Payments in the Unemployment Insurance
Program: Deceased Recipients, Louisiana Workforce Commission,
Baton Rouge, LA: Louisiana Legislative Auditor, 2021.
16. Louisiana Legislative Auditor, Performance Audit Services, Data
Analytics Unit. Improper Payments in the Unemployment Insurance
Program: Ineligible Recipients Based on Income, Louisiana Workforce
Commission, Baton Rouge, LA: 2021.
Michigan
17. Deloitte & Touche, LLP. State of Michigan Unemployment Insurance
Agency Fraud Measurement Estimation. Detroit, MI: 2021.
18. Deloitte & Touche, LLP. State of Michigan Unemployment Insurance
Agency Forensic Review. Detroit, MI: 2020.
Nebraska
19. Nebraska Auditor of Public Accounts. Attestation Report of the
Nebraska Department of Labor, July 1, 2020 through June 30, 2021.
Lincoln, NE: 2021.
New Mexico
20. New Mexico Legislative Finance Committee, Program Evaluation Unit.
Spotlight: Unemployment Insurance System Review, May 19, 2021.
NM: 2021.
Ohio
21. Ohio Auditor of State. Ohio Department of Job and Family Services:
Auditor’s Report on Unemployment Insurance Fraud for the Period
March 1, 2020 through February 28, 2021. Columbus, OH: 2021.
22. Ohio Auditor of State. Ohio Department of Job and Family Services:
Performance Audit Unemployment Compensation, September 23,
2021. Columbus, OH: 2021.
Oklahoma
Page 49 GAO-23-105523 UI Fraud
Appendix II: State Reports on Unemployment
Insurance (UI) Fraud and Potential Fraud
23. Oklahoma Office of the State Auditor and Inspector. State of
Oklahoma: Single Audit Report for the Fiscal Year Ended June 30,
2020. Oklahoma City, OK: 2021.
Oregon
24. State of Oregon Employment Department. Unemployment Insurance
Fraud Calendar Year 2020: Protecting Unemployment Insurance
during the COVID-19 Pandemic Health Crisis. EDPUB185 (0122).
OR: 2022.
Vermont
25. Office of the Vermont State Auditor. Unemployment Fraud and
Overpayment Review. VT: 2021.
Washington
26. Office of the Washington State Auditor. Accountability Audit Report:
Employment Security Department for the Period July 1, 2017 through
June 30, 2020. Report No. 1028000. Olympia, WA: 2021.
27. Office of the Washington State Auditor. Financial Statements Audit
Report: State of Washington for the Period July 1, 2019 through June
30, 2020. Report No. 1027566. Olympia, WA: 2020.
28. Office of the Washington State Auditor. Fraud Investigation Report:
Employment Security Department for the Investigation Period January
1, 2020, through December 31, 2020. Report No. 10219365. Olympia,
WA: 2021.
29. Office of the Washington State Auditor. Fraud Investigation Report:
Employment Security Department for the Investigation Period January
1, 2020 through December 31, 2020. Report No. 1028164. Olympia,
WA: 2021.
West Virginia
30. West Virginia Legislative Auditor, Performance Evaluation & Research
Division. Special Report: Workforce West Virginia Unemployment
Claims Data. PE 21-14-648. Charleston, WV: 2021.
Wisconsin
31. State of Wisconsin Department of Workforce Development. Fraud
Report to the Unemployment Insurance Advisory Council. UCD-
17392-P (R 03/22) Madison, WI: 2022.
Page 50 GAO-23-105523 UI Fraud
Appendix III: Objectives, Scope, and
Appendix III: Objectives, Scope, and
Methodology
Methodology
This report addresses (1) what existing federal and state measures and
estimates indicate about the extent of fraud and potential fraud in
Unemployment Insurance (UI) programs during the pandemic and (2) the
extent to which the Department of Labor (DOL) designed and
implemented a strategy to manage UI fraud risks.
Review of Federal and To address our first objective, we contacted DOL, the DOL Office of
State Measures and Inspector General (OIG), the Department of Justice (DOJ), the
Department of Homeland Security (DHS), state workforce agencies
Estimates
(SWA), state auditors, and state Attorneys General offices. We asked
officials from these entities to identify reports and other reporting
mechanisms in place from federal and state entities related to measuring
and estimating the extent of fraud and potential fraud in UI programs
during the pandemic. 1 We also independently identified state reports
related to measuring or estimating the extent of UI fraud during this time.
The scope of our review was from March 2020—the beginning of the
pandemic—through March 2022. These are the two most recent years
available at the time of our selection. 2
Throughout this report, we refer to measures as counts of detected
activities, and to estimates as projections or inferences based on
measures, assumptions, or analytical techniques. 3 Throughout this report,
we also carefully describe the population covered by existing measures
and estimates. Specifically, we use the phrase “fraud measure” to discuss
counts related to proven fraud such as adjudicated cases of fraud. We
use the phrase “fraud estimate” to discuss estimates that attempt to
quantify what could be “determined to be fraud,” although such cases
have not yet been proven. Finally, we use the phrases “fraud-related” and
“potential fraud” to describe measures and estimates that attempt to
quantify the extent of fraud indicators, but do not suggest a potential or
actual determination of fraud.
We reviewed the federal and state reports and identified those that
contained a measure or estimate that indicates the extent of UI fraud. Of
1UI programs during the pandemic include the temporary UI programs created by the
CARES Act and the Consolidated Appropriations Act, 2021 as pandemic UI programs,
along with the regular UI and Extended Benefits programs.
2Appendix II provides a bibliography of the reports we received from SWAs, state auditors,
and state Attorneys General that discussed fraud or potential fraud in UI programs.
3Estimates are often used when direct measures are unavailable, incomplete, or
unreliable.
Page 51 GAO-23-105523 UI Fraud
Appendix III: Objectives, Scope, and
Methodology
those, we identified reports with sufficient information for us to determine
the methodology used to arrive at the measure or estimate. For these
remaining reports, we summarized the methodologies used, along with
strengths and limitations of each as they relate to determining the extent
of fraud. We selected examples from these federal and state reports to
present in this report to illustrate the variety of fraud and fraud-related
measures and estimates.
In addition, we reviewed DOJ case information to identify federal fraud-
related charges related to UI as of July 31, 2022, and analyzed related
federal court documents. Specifically, to identify cases, we obtained DOJ
press releases through a subscription to Westlaw (a legal news service),
conducted periodic checks of the Westlaw database, and used other
available sources, such as the DOJ Fraud Section website. For identified
cases, we obtained relevant court documents by searching Public Access
to Court Electronic Records. 4 We reviewed DOJ information from March
2020 through July 2022 to capture federal charges from the start of the
pandemic through the most recent available at the time of our review.
This analysis is limited to the DOJ cases we identified from public
sources, which may not include all criminal and civil cases charged by
DOJ as of July 2022. Additionally, our analysis is based on known
information presented in court documents, and the specific details of
fraud cases and schemes in the court documents may not be complete.
For example, dollar amounts applied or obtained, or all fraud mechanisms
may not be identified in court documents. For purposes of this report, we
define “fraud-related charges” as charges related to a criminal case
containing fraud charges. Further, we interviewed DOL officials and
federal law enforcement officials about the extent of fraud in UI programs
during the pandemic.
As part of this objective, we also analyzed UI financial transaction data
and individual program files from DOL’s website to determine
expenditures under the regular UI program, the Extended Benefits
program, and the pandemic UI programs from April 2020 through
September 2022. Specifically, we analyzed the UI data from the
Employment and Training Administration’s (ETA) 5159 Claims and
4Public Access to Court Electronic Records is a service of the federal judiciary that
enables the public to search online for case information from U.S. district, bankruptcy, and
appellate courts. Federal court records available through this system include case
information (such as names of parties, proceedings, and documents filed) as well as
information on case status.
Page 52 GAO-23-105523 UI Fraud
Appendix III: Objectives, Scope, and
Methodology
Payment Activities reports, ETA 2112 UI Financial Transaction Summary
reports, 902P Pandemic Unemployment Assistance Activities reports, and
the 902M Mixed Earners Unemployment Compensation reports. We also
analyzed fraud estimates for the regular UI program reported in DOL’s
Benefit Accuracy Measurement Data Summary for performance year
2021 and the underlying documentation. In addition, we analyzed the
amounts of overpayments due to fraud as reported by states to DOL.
These data included the regular UI program and the pandemic UI
programs. Specifically, we analyzed quarterly data from ETA 227 UI
Overpayments reports and ETA 902P Pandemic Unemployment
Assistance Activities reports.
To assess the reliability of these data, we interviewed DOL OIG officials,
reviewed data from past years, and considered its consistency with other
available measures and estimates. We determined that the data were
sufficiently reliable to (1) combine into total UI expenditures during the
pandemic, (2) combine into an overall total amount of state-estimated
fraud and illustrate variation in fraud rates over time, and (3) combine into
an overall total amount of state-reported overpayments due to fraud.
We also used the analysis of fraud estimates for the regular UI program
reported in DOL’s Benefit Accuracy Measurement Data Summary for
performance year 2021 and the underlying documentation to support an
illustrative extrapolation of a lower bound of fraud in UI programs during
the pandemic. This extrapolation is based on the reported estimated fraud
rate and insights derived from other existing measures and estimates. We
determined that existing measures and estimates did not provide
sufficient evidence to use this approach to extrapolate an upper bound of
fraud in UI programs during the pandemic.
Evaluation of DOL’s To address the second objective, we evaluated DOL’s UI fraud risk
Efforts against Leading management activities against the leading practices in GAO’s A
Framework for Managing Fraud Risks in Federal Programs (Fraud Risk
Practices
Framework)—specifically those leading practices related to developing an
antifraud strategy. 5 The Fraud Risk Framework contains four
components: (1) commit; (2) assess; (3) design and implement; and (4)
evaluate and adapt. Within the four components, there are overarching
concepts and leading practices. In October 2021, we assessed the extent
5GAO, A Framework for Managing Fraud Risks in Federal Programs, GAO-15-593SP
(Washington, D.C.: July 28, 2015).
Page 53 GAO-23-105523 UI Fraud
Appendix III: Objectives, Scope, and
Methodology
to which DOL’s fraud risk management activities aligned with leading
practices under the second component of the Fraud Risk Framework—
assess. 6 For this report, we selected two of the 15 leading practices from
the third component—design and implement—that are most relevant to
this objective based on a review of DOL documents and discussions with
DOL officials responsible for fraud risk management. We also reviewed
DOL policies, procedures, and guidance to identify newly established
controls designed to prevent fraud from occurring. Further, we
interviewed DOL and DOL OIG officials about fraud risk management
efforts.
We conducted this performance audit from November 2021 to December
2022 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 and conclusions based on our audit objectives. We believe
that the evidence obtained provides a reasonable basis for our findings
and conclusions based on our audit objectives.
6GAO, Additional Actions Needed to Improve Accountability and Program Effectiveness of
Federal Response, GAO-22-105051 (Washington, D.C.: October 27, 2021).
Page 54 GAO-23-105523 UI Fraud
Appendix IV: Comments from the
Appendix IV: Comments from the Department
of Labor
Department of Labor
Page 55 GAO-23-105523 UI Fraud
Appendix IV: Comments from the Department
of Labor
Page 56 GAO-23-105523 UI Fraud
Appendix IV: Comments from the Department
of Labor
Page 57 GAO-23-105523 UI Fraud
Appendix IV: Comments from the Department
of Labor
Page 58 GAO-23-105523 UI Fraud
Appendix V: GAO Contacts and Staff
Appendix V: GAO Contacts and Staff
Acknowledgments
Acknowledgments
Seto J. Bagdoyan, (202) 512-6722, BagdoyanS@gao.gov
GAO Contacts
Jared B. Smith, (202) 512-2700, SmithJB@gao.gov
In addition to the contacts named above, Gabrielle Fagan (Assistant
Staff Director), Lauren Kirkpatrick (Analyst in Charge), Benjamin Bolitzer, Colin
Acknowledgments Fallon, Lisa Fisher, Hunter McCormick, Maria McMullen, Steven Putansu,
and Sabrina Streagle made key contributions to this report. Other
contributors include Amber Gray and Nick Weeks.
(105523)
Page 59 GAO-23-105523 UI Fraud
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