Full text
Report Number: R-21-011
A Performance Audit Report Presented to the Legislative Post Audit Committee
Evaluating the Kansas Department
of Labor’s Response to COVID-19
Unemployment Claims (Part 2)
August 2021
KANSAS LEGISLATIVE
DIVISION of
POST AUDIT
1
Introduction
The Legislative Post Audit Committee requested this audit at its September 2,
2020 committee meeting.
Objectives, Scope, & Methodology
The audit included three questions. For reporting purposes, we divided those
questions into two separate audit reports. This audit report is the second and final
report and answers the following question:
1. What factors caused delays in the Kansas Department of Labor’s
unemployment claims processing during the COVID-19 pandemic?
To answer this question, we spoke with officials from the Kansas Department of
Labor (KDOL) and reviewed KDOL staffing, incident, and call center reports. We
reviewed relevant reports from the U.S. Department of Labor’s Office of the Inspector
General. We attempted to use U.S. Department of Labor unemployment claims data
to compare processing times in Kansas to other states. However, the data contained
significant errors so we couldn’t use it. However, we did contact officials from the
Idaho, Nebraska, and Oklahoma labor departments for comparative information. Our
work primarily focused on 2020 and early 2021, during the COVID-19 pandemic.
This audit also includes an updated unemployment insurance fraud estimate. In
February 2021 we released the first part of this audit. In that audit we reported a
preliminary estimate of how much fraud could have occurred in Kansas in 2020. In
this report, we used KDOL claims data from January 2020 through February 2021 to
provide a more precise estimate. We used KDOL claims data and a neural network
computer model for our estimate. It is important to note that about 9,000 claims
applications were missing from KDOL’s data used for our estimate. However, given
that there were 1.08 million claims applications in total, the missing applications
should have little effect on our overall conclusions. We also found inconsistencies
and potential errors with the date payments were made in KDOL’s claims data. This
did not affect our overall fraud estimate but could affect the distribution of
payments by month. Finally, we were unable to report on KDOL’s detailed staffing
numbers because we did not have a detailed report of staffing allocation.
Important Disclosures
We conducted this performance audit 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. Overall, we believe the
evidence obtained provides a reasonable basis for our findings and conclusions
based on those audit objectives.
2
Audit standards require us to report our work on internal controls relevant to our
audit objectives. In this audit we reviewed KDOL internal controls to ensure claims
are processed timely and accurately. KDOL followed U.S. Department of Labor
accuracy and timeliness quality control measures. The U.S. Department of Labor
allowed states to suspend these measures early in the pandemic. As a result, parts of
KDOL’s quality control process were temporarily suspended in 2020.
Finally, several members of this audit team were either victims of unemployment
fraud, identity theft related to unemployment claims, or knew someone that was a
victim. We concluded these events did not constitute an actual impairment to the
team’s independence or ability to objectively complete this audit.
More specific details about the scope of our work and the methods we used are
included throughout the report as appropriate.
Our audit reports and podcasts are available on our website (www.kslpa.org).
3
Rapid program changes, historically high unemployment
claims, and an ill-equipped computer processing system
created delays in claims processing during the pandemic in
Kansas.
Unemployment Insurance Program Background
The Kansas Department of Labor (KDOL) administers the regular unemployment
insurance program and gives financial aid to unemployed individuals.
•
The regular unemployment insurance program is a joint program between
federal and state governments. Although there are broad federal guidelines
over the program, states establish their own criteria for who is eligible for
regular unemployment insurance. States also decide the amount and
duration of regular unemployment benefits.
•
In Kansas, individuals must meet several criteria to qualify for regular
unemployment benefits. For example, individuals must have worked for
enough time and left work through no fault of their own (such as a medical
emergency or layoffs, etc.).
•
Generally, Kansas employers are assessed a tax that funds the state’s
unemployment insurance trust fund. That fund pays weekly benefits to
unemployed individuals for 16 to 26 weeks depending on the state’s
unemployment rates.
•
Kansas employees do not contribute any money to the trust fund.
In 2020, the federal government created several temporary unemployment
insurance programs to help individuals who lost their jobs due to COVID-19.
•
The COVID-19 pandemic significantly increased the unemployment rate
nationally and in Kansas. Before the pandemic, the national unemployment
rate was about 4% in January 2020 (about 3% in Kansas). By April 2020, the
national unemployment rate rose to about 15% (about 12% in Kansas).
•
In March 2020, Congress passed the CARES Act to help individuals the
pandemic negatively affected. The act included funding for new
unemployment insurance programs.
•
The new programs differed from regular unemployment insurance in a few
ways. For example, the new programs were entirely federally funded.
Additionally, all the new programs were temporary and have either expired or
are scheduled to expire in September 2021.
•
The new programs expanded unemployment benefits to include more people
who lost their jobs due to COVID-19. For example,
4
o The Pandemic Unemployment Assistance (PUA) program extended
benefits to several new classes of workers. This included the self-employed
(e.g., independent contractors) and gig workers like Uber drivers. Under
this program, individuals not eligible for regular unemployment insurance
could receive up to about $500 per week for 39 weeks (a maximum of
about $19,500) under the terms of the original CARES Act in 2020.
o The Federal Pandemic Unemployment Compensation (FPUC) program
also gave an extra $600 per week to anyone already receiving
unemployment benefits, for a period from late March through late July
2020. FPUC was renewed at a level of $300 per week in late December
2020 and expires in September 2021. FPUC was temporarily replaced by
the Lost Wages Assistance (LWA) program in 2020. LWA is a disaster
recovery fund administered by the Federal Emergency Management
Agency, but it was temporarily used in place of FPUC during the
pandemic.
o Under the terms of the original CARES Act, the Pandemic Emergency
Unemployment Compensation (PEUC) program provided up to 13 weeks
of additional unemployment benefits to claimants once they exhausted all
other unemployment benefits.
•
Benefits paid out for the temporary federal unemployment programs do not
come from a state’s unemployment trust fund. They are paid with federal
funds.
Kansas and other states across the U.S. experienced claims processing delays
during the pandemic.
•
To receive benefits, individuals must first apply for unemployment insurance.
Once submitted, a claim goes through several steps. Those include eligibility
determination, calculating benefits, and submitting payment. It is important
this processing happens timely. Delays in any part of this process could delay
payment to individuals needing assistance.
•
High unemployment rates contributed to delays in claims processing in
Kansas and nationally. As mentioned above, by April 2020, the national
unemployment rate was about 15% (about 12% in Kansas). That’s higher than
any other time in recent history. For context, at their highest, unemployment
rates were about 10% nationally and about 7% in Kansas during the Great
Recession in 2009 and 2010.
o In 2020, high unemployment rates meant many people were suddenly
applying for benefits at once. Typically, unemployment rates rise over a few
months, giving state labor departments time to respond to the increase in
claims. However, the immediacy of the pandemic caused unemployment
rates to surge very quickly. This put a tremendous amount of strain on
states’ unemployment processing systems, creating errors and processing
delays.
5
o In Kansas, there were several reports of individuals waiting several weeks
or months to receive unemployment benefits. KDOL data showed
individuals called its customer service call center about 12.5 million times in
April 2020, sometimes calling multiple times a day. These calls significantly
outnumbered the 33 full-trained customer service representatives KDOL
reported at that time. This suggests many people needed assistance in
applying for or receiving unemployment benefits. Federal and media
reports during the pandemic showed these delays also occurred nationally
during the pandemic.
•
We were unable to compare Kansas’ claims processing times to other states.
We attempted to use federal unemployment claims data to compare Kansas
to other states. However, the federal data contained significant errors that
prevented us from using it. Additionally, that data only measured time to
payment after an application was submitted and approved. It did not capture
any delays in getting an application submitted.
•
This audit focused on identifying the main causes for Kansas’ delays in claims
processing. We worked with KDOL officials to understand what caused these
delays. As discussed below, the main issues were the outdated computer
system and its upkeep, as well as call center staffing issues.
Implementation and Computer System Issues
KDOL relied on an outdated, piecemeal, and poorly maintained unemployment
computer system during the pandemic.
•
States use large, powerful computer systems to process unemployment
claims. Among other things, these systems hold eligibility rules and historic
claims data. Generally, when someone applies for benefits, their application
runs through these systems to determine eligibility, benefit amount, and
process payment.
•
Kansas’ unemployment computer system was created in the early 1970’s and
was centered around a mainframe computer. The mainframe operated on an
older, lesser-used coding language. Over the years, the outdated coding
language created challenges for KDOL. For example, there are few IT staff
available that are still familiar with the mainframe’s coding language. This
makes it difficult to maintain and update the system. Additionally, the
outdated code requires KDOL staff to navigate between several screens to
process a single claim. In some cases, staff are unable to use a mouse to enter
or retrieve information. In these cases, all information must be hard coded
into the system, taking time and special training.
•
Over the years, KDOL had to add modern programs around the outdated
mainframe, creating a piecemeal system. Increasingly, the mainframe had to
interact with programs that operated on modern computer code. For
example, the state’s online application site operates on modern code.
6
However, that site must communicate with the mainframe system. As a
solution, KDOL installed an intermediate program called Rocket that allows
the two systems to communicate. All programs, new and old, must work in
unison to process claims. Periods of high claims volume stressed the
connection between these programs, which led to system issues and delays.
•
Historically, KDOL did not properly document changes to the computer code,
creating a risk for system error. Unemployment systems run on a significant
amount of underlying computer code. Periodically, that code needs revision.
When this happens, programmers should follow a uniform and well
documented process to maintain system integrity. Poorly organized
documentation and coding increase the risk for system error. That’s because
programmers can’t be sure what changes were made, and how additional
changes will impact the existing code. According to KDOL, staff over the years
did not follow these best practices, resulting in a disorganized coding
structure. Because of staff turnover and poor practices, current programmers
use the system without full knowledge of how the code functions. According
to KDOL, staff begun documenting changes to the coding structure during
the pandemic.
Frequent changes to the state’s unemployment computer system during the
pandemic created system errors and processing delays.
•
The pandemic resulted in an extremely unique challenge for KDOL. Under
normal conditions, KDOL does not have to make many major edits or changes
to its unemployment computer system. When they did, the changes didn’t
need to happen immediately, giving them more of an opportunity to test the
changes before deployment. When the pandemic began, KDOL had to
quickly build and deploy several changes to its unemployment computer
system. The significant changes and hurried pace, combined with historically
high unemployment claims and an ill-equipped computer processing system
put KDOL at extremely high-risk for processing delays and errors.
•
The pandemic spurred lots of sudden changes at the state and federal level.
Government officials were trying to quickly implement new programs to help
address high unemployment caused by the pandemic.
o KDOL could not begin processing claims for the temporary federal
unemployment programs until it received and implemented changes
from the federal government. Federal documents showed it took between
one and two weeks for the U.S. Department of Labor (U.S. DOL) to issue
detailed guidance to states on how to implement the federal pandemic
programs. Once KDOL had the guidance, they began making necessary
changes to their systems. For example, KDOL officials told us they had to
build an entirely new program to administer the new PUA program, which
took time. KDOL officials told us getting the new PUA system to effectively
communicate with the outdated mainframe was extremely challenging
and took a significant amount of resources.
7
o KDOL also had to respond to additional program revisions from the U.S.
DOL throughout the pandemic. Figure 1 shows some of the key program
revisions during the pandemic. As the figure shows, the U.S. DOL issued
several program revisions during the pandemic. These revisions required
KDOL to review new guidance, edit the underlying code, and test changes
before deployment.
•
Changes to the state’s coding structure to implement new federal programs
and requirements created errors and delays. These changes caused problems
because the state’s unemployment system operated on a disorganized
coding structure. Despite an internal testing process, KDOL staff were unable
to prevent all changes from creating system errors. This led to several claims
processing issues. For example:
CARES
Act
First PUA
Payments
FPUC
Expires
LWA Program
Initiated
First LWA
Payments
Continued
Assistance
Act
American
Rescue
Plan Act
Mar
20
Apr
20
May
20
Jun
20
Jul
20
Aug
20
Sep
20
Oct
20
Nov
20
Dec
20
Jan
21
Feb
21
Mar
21
Apr
21
May
21
Federal Program Revisions that Required KDOL to Modify its UI
System(a)
Figure 1: KDOL had to modify its unemployment system several times
during the pandemic, which contributed to claims processing delays.
Programs:
FPUC: Federal Pandemic Unemployment Compensation
PUA: Pandemic Unemployment Assistance
PEUC: Pandemic Emergency Unemployment Compensation
LWA: Lost Wages Assistance
Source: KDOL officials and U.S. Department of Labor Guidance
(a)Does not include all federal changes made during the pandemic.
Kansas Legislative Division of Post Audit
First PEUC
and FPUC
•
8
o A coding issue made it appear that several claimants were no longer
eligible for benefits when they still had multiple weeks of eligibility
remaining. In this case, those claimants were denied payments because of
the error.
o Other coding issues denied claimants that were eligible for pandemic
related programs. KDOL officials told us these coding errors mostly
occurred as claimants were transitioning between unemployment
programs.
•
Coding issues are not easy to identify or fix, which creates payment delays for
claimants. In some cases, KDOL learned of these issues after the fact from
customer service representatives taking calls from the public. Additionally, it
takes specialized claims maintenance staff to review the claims and identify
the issue. KDOL told us they do not have many of these specialists because it
takes years of experience to gain the knowledge necessary for that position.
Once identified, IT staff must also find where in the code the issue originated
to fix it. These issues take time to fix, during which claimants may go without
benefits.
During the pandemic, a surge in valid and fraudulent claims strained the state’s
outdated and piecemeal unemployment system, leading to system failures and
claim delays.
•
Unemployment claims increased dramatically during the pandemic. Figure 2
shows claims filed from January 2020 through February 2021. As the figure
shows, claims for the state’s regular unemployment program increased from
3,000 initial claims in February 2020 to about 66,000 claims at the end of
March. That’s roughly a 22-fold increase in one month.
•
High claims volumes strained the state’s outdated and piecemeal system,
resulting in system failures and delays. As mentioned above, KDOL’s
unemployment computer system consisted of modern and outdated
programs. KDOL officials told us the outdated mainframe had issues
communicating with modern systems during high-volume times. For
example, the program responsible for connecting the mainframe to the
modern online application site crashed periodically during the pandemic.
Periods of high-claims volume contributed to these crashes. Claimants could
not file online claims during this time.
•
Fraudsters put additional strain on the state’s system. Fraudsters may be able
to automate their attacks against states’ systems. In doing so, they can
overwhelm state systems with a significant number of claims. This puts more
stress on already strained systems. As part of this audit, we estimated about
630,000 of the 1.08 million unique claims applications (59%) from January
2020 to February 2021 could have been fraudulent attempts. Our full fraud
estimate is discussed in more detail below, but it’s important to note that not
all 630,000 potentially fraudulent claims were paid.
9
Staffing and Call Center Issues
Prior to the pandemic, KDOL had few staff to answer calls because of low
unemployment and low federal funding levels.
•
KDOL customer service positions are federally funded. Federal funding is
based on prior year unemployment program expenditures. Generally, funding
increases and decreases with unemployment rates as the need for
unemployment insurance changes. Kansas’s unemployment rate steadily
declined from 2010 to 2019, reaching 3% in 2019; the lowest level since 1979.
Over the same time, federal funding for Kansas’s unemployment program
also declined.
•
KDOL reported that because unemployment program funding was low, they
only had 33 customer service representatives to answer phones in April 2020.
•
Total calls significantly outnumbered available staff at the beginning of the
pandemic. Figure 3 shows the total number of incoming calls and calls
answered during the pandemic. As the figure shows, KDOL reported a total of
12.5 million incoming calls in April 2020. That’s compared to just 33 fully
trained customer service representatives. During this time individuals called
multiple times a day because they couldn’t reach a customer service
representative. This contributed to the 12.5 million calls in April 2020.
3K
66K
87K
30K
71K
84K
310K
24K
31K
63K
29K
7K
Jan
20
Feb
20
Mar
20
Apr
20
May
20
Jun
20
Jul
20
Aug
20
Sep
20
Oct
20
Nov
20
Dec
20
Jan
21
Feb
21
Initial Claims - State Program (a)
Initial Claims - Federal Programs (a)
Kansas Legislative Division of Post Audit
Figure 2: Initial claims increased significantly during the pandemic,
especially before identify verification was established in February 2021.
Source: LPA Analysis of KDOL data from 2020-2021 (audited)
(a) All claims filed but not necessarily paid, including duplicate claims made in error or
with fraudulent intent.
-·-
-·-
10
•
According to KDOL, up to 120 staff from other divisions and agencies helped
answer calls during the spring of 2020. In total, KDOL reported answering only
about 70,000 of the 12.5 million calls (about 1%) in April 2020.
Despite additional staff, the number of calls answered did not improve
significantly during the pandemic, potentially leading to additional claims
delays.
•
Not being able to talk to a customer service representative likely caused
additional delays in claimants receiving unemployment benefits. Claimants
call KDOL for several reasons. In some cases, they’re calling because they need
help applying for benefits or to resolve problems with an existing claim. As
Figure 3: The total number of ca lls answered did not improve
significantly during the pandemic.
Total Incoming Calls
• 17.SM
3.4M
•----. 2.8M ~-
Apr May Jun
20
20
20
Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr
20
20
20
20
20
20
27
27
27
27
Total Calls Answered
• 70K
• 69K
,
~·
54K
56K~
54K
/\
"
49K
•-..
/•,,-
"-.-./
~
• 56K
•~--....,,,,,-
39K
•
Apr May Jun
Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr
20
20
20
20
20
20
20
20
20
27
27
27
27
Source: LPA Analysis of KDOLdata from 2020-2021 (unaudited)
Kansas Legislative Division of Post Audit
11
discussed above, many claimants called KDOL during the pandemic,
sometimes multiple times a day, without being able to reach a customer
service representative. Not being able to reach a customer service
representative likely resulted in additional delays for people needing
assistance with their claim.
•
We were unable to compare detailed staffing trends to calls answered during
the pandemic because of a lack of data. Beginning in July 2020, KDOL
contracted with Accenture to provide additional surge staff during the
pandemic. According to KDOL, Accenture staff helped answer phone calls,
made out-bound calls, and helped with other administrative duties. KDOL
gave us some staffing data, but we could not readily identify how many
Accenture staff were answering phones on a given day or month. As a result,
we were unable to compare detailed staffing levels to calls answered.
Generally, KDOL reported adding up to about 500 temporary Accenture surge
staff over the course of the pandemic. However, we could not verify those
numbers.
•
Despite added surge staff, the number of answered calls did not significantly
increase during the pandemic. As Figure 3 shows, there was no clear increase
in calls answered during the pandemic. That’s despite KDOL adding
additional Accenture surge staff. KDOL suggested call complexity increased
during the pandemic. It is possible more complex calls increased call times.
Longer calls could have resulted in fewer calls answered per day. KDOL also
reiterated that not all Accenture surge staff were answering phones, which
could have limited the number of calls answered per day.
Modernization Efforts
States with modern unemployment computer systems appeared better
equipped to handle the challenges of the pandemic.
•
According to a National Association of State Workforces report, 26 states
(including Kansas) used outdated unemployment computer systems as of
February 2021. Generally, an outdated system means it operates on an
antiquated mainframe computer system. Like Kansas, states with outdated
systems may depend on a combination of outdated and modern programs,
resulting in a piecemeal system. The remaining 24 states upgraded to
modern systems. Generally, modern systems do not rely on old mainframe
computers and are built using modern coding languages. As a result, the
necessary programs function more cohesively than the outdated, piecemeal
systems.
•
States with outdated unemployment systems appeared to encounter similar
challenges as Kansas during the pandemic. An October 2020 report from the
U.S. Department of Labor said outdated mainframe systems had compatibility
issues with the new programs (like PUA). An April 2020 U.S. Department of
Labor report found these compatibility issues likely resulted in delays
processing claims during the pandemic.
12
•
States with modern unemployment systems appeared better equipped to
handle the challenges of the pandemic. A May 2021 U.S. Department of Labor
report found that on average, states with modern systems implemented the
PUA and PEUC programs a week to two weeks before other states. This is
likely because it was easier to build the necessary pieces and make the
needed changes to accommodate the new federal requirements. We also
spoke to officials with the Nebraska and Idaho state labor departments.
Officials confirmed their modern systems gave them more flexibility to quickly
integrate new programs (like PUA) during the pandemic. Better integration
also means modern systems are more stable and able to handle higher claims
volume.
•
We were unable to compare Kansas’ claims processing times to other states.
We attempted to use federal unemployment claims data to determine how
Kansas’ processing times compared to other states. However, the federal data
contained significant errors that prevented us from using it. This included
duplicated claims totals and inconsistencies between states. Additionally, that
data only measured time to payment after an application was submitted and
approved. It did not capture any delays in getting an application submitted.
KDOL is in the process of modernizing its unemployment computer system.
•
From 2005 to 2011 KDOL made efforts to modernize its unemployment
system. During this time, KDOL made improvements to its system, but never
fully modernized it. For example, KDOL added a new case management
system and a new online application site. However, KDOL did not replace the
outdated mainframe system before the modernization process was stopped
in 2011. As a result, KDOL still uses a piecemeal system, centered around an
outdated mainframe system. Current KDOL officials did not know why the
project was stopped or why it wasn’t restarted. They suggested a lack of
dedicated funding could have been a contributing factor.
•
As of July 2021, KDOL had restarted the process of modernizing its
unemployment computer system. Officials told us before the pandemic they
were gathering information from other states on modern unemployment
systems. However, the pandemic paused their efforts. In March 2021, KDOL
finalized a project modernization plan. Generally, the plan would replace
KDOL’s current piecemeal and outdated system with a more self-contained
system. The new system would also operate on modern computer code. In
April 2021, KDOL posted a request for proposal for its modernization project.
•
Passed in 2021, Kansas House Bill 2196 included several provisions for KDOL’s
modernization project. The bill required that the system be in place by
December 31, 2022. However, extensions to the deadline can be granted. The
governor’s fiscal year 2022 budget report proposed investing about $37.5
million on the modernization project. The report proposed those funds come
from federal and special revenue funds between fiscal years 2021 and 2022.
---
13
•
A new, modern system should be better suited to handle the unique
challenges of a pandemic, recession, or other major unemployment events.
Generally, a modern system can house all the necessary components to
process claims within one integrated system. This reduces the risk for system
failure during periods of high claims volume. The new system will also operate
on modern code, making it easier to quickly add new programs or
requirements should the need arise. Additionally, KDOL will have an
opportunity to fix its disorganized coding structure as it transitions away from
the mainframe computer code. It’s likely a modern system will also eliminate
the need for staff to navigate between multiple screens and hard code
information when processing a claim. A modern system should also improve
KDOL’s ability to run various metrics and reports on how its system is
operating.
Unemployment Fraud Update
In February 2021 we released a preliminary fraud estimate with the intent of
releasing an updated estimate in this report.
•
Fraud is a legal term used to describe specific criminal acts. Ultimately, only
courts can decide whether fraud occurred. In this audit, we do not use fraud
to refer to any legal determination. Rather, we use fraud to describe claims
that displayed suspicious characteristics indicative of imposter fraud.
•
Imposter fraud occurs when a fraudster uses stolen personal information to
apply for unemployment benefits in other people’s names. This normally
occurs in large quantities. We focused on imposter fraud because it appeared
to be the most widespread fraud during the pandemic. Other types of
improper payments or fraud, such as someone deliberately misrepresenting
their employment information to try to increase their benefit amount, were
not specifically accounted for in our fraud methodology. KDOL told us they
also saw an increase in these other types of fraud during the pandemic.
•
In February 2021 we released a preliminary fraud estimate showing that about
$600 million in unemployment fraud could have occurred in Kansas in
calendar year 2020. This estimate was based on three key numbers: The
number of claims KDOL reported stopping as potentially fraud from March
2020 through November 2020 (157,000), the total number of claims filed that
during that time (650,000), and the total benefit amount paid ($2.6 billion) in
2020.
•
As of February 2021, there was little information on how much fraud could
have occurred in Kansas during the pandemic. We thought it prudent to
release a preliminary estimate while we finished our detailed fraud analysis
using KDOL’s claims data. Since then, we completed our detailed fraud
estimate. That work is described below.
14
For this audit, we used an advanced computer model to create a more precise
estimate of unemployment fraud in Kansas.
•
We used a neural network to estimate unemployment fraud in Kansas. A
neural network is a form of machine learning used to replicate human
decision making. This helps automate and expedite time-consuming tasks.
•
Neural networks must first be trained to replicate human decisions. To
accomplish this, we manually reviewed a random sample of 1,000 unique
claims applications (out of about 737,000) for fraud. We looked for 26 things
that can be indicators of fraud. For example, we looked for:
o Duplicated passwords or e-mail address. We counted the number of times
the same password was used by different claimants. We reviewed
password complexity and duplicate counts to determine the likelihood of
fraud. We estimate it’s extremely unlikely (0.006%) that a six-character
password, with random characters, would have at least one duplicate by
chance out of 1 million claims. We also counted the number of times the
same or similar e-mail address (within 2 characters) was used across
claimants.
o The accuracy of state employee application information. Some fraudsters
targeted state employees because more of their information is publicly
available. We cross-checked social security numbers in the claims data
against Kansas state employee data to identify state employees. We then
confirmed the accuracy of the names and dates of birth for those claims.
Mismatches suggested imposter fraud.
o We were unable to review checking account numbers, bank routing
numbers, or IP addresses. Although this data exists in KDOL’s system,
successfully querying that data would have taken additional time, delaying
the release of the audit.
•
We used the results of our sample to train the neural network to identify fraud
in all remaining claims. In total, we identified possible fraud in 575 of 1,000
(58%) claims sampled. We trained the model using 700 claims randomly
selected from our sample of 1,000. We used the remaining 300 claims in the
sample to test and validate the network’s accuracy. Once fully trained, we ran
the neural network against all 1.08 million unemployment claims filed from
January 2020 to February 2021. Appendix B has more information on our
neural network methodology.
We estimate about $700 million in potentially fraudulent payments could have
been made in Kansas during the pandemic.
•
In total, Kansas paid about $2.8 billion in unemployment benefits from
January 2020 through February 2021. Of that total, we estimate about $700
million (about 25%) could have been fraudulent.
15
•
Our $700 million fraud estimate combined the results of our neural network
with claims KDOL already flagged as potentially fraud. In its data, KDOL
already flagged a significant number of fraudulent payments made during
the pandemic. We used our neural network to identify additional fraud KDOL
may have missed. Ultimately, we combined the results of KDOL’s work and
our neural network to arrive at our $700 million estimate. Figure 4
summarizes the components of our estimate. As the figure shows:
o LPA identified about $71 million in potentially fraudulent payments that
KDOL did not (high confidence). This only included claims our model was
at least 95% confident were fraud. For example, one claim we reviewed had
the exact same password (9 characters, upper and lower cases, contained
a special character and numbers), e-mail address, and residential address
as 20 other claimants. In this case, it was very likely one person applied for
benefits multiple times using other people’s information. Given the
network’s confidence, we also had high confidence of fraud in these cases.
o KDOL and LPA had consensus on $309 million in potentially fraudulent
payments (high confidence). Both KDOL and our neural network flagged
these payments as potential fraud, giving us high confidence of fraud in
these cases.
Figure 4: The $700 million f raud estimate is based on three components.
,:~-:-:-:-:-:-=-:-:-:-:-~:=:
:
::
·~-.
LPA:
KDOL& LPA: $309M
$71M
·=·
R::
~t
..
·-· ..
...
~:=.
:
The LPA neural network identified
$380M in potential fraud
including $71M not identified by
KOOL (high confidence)
--
KOOL identified $306 M in
potential fraud that our network
did not (low confidence)
I
KOOL: $306M
Source: LPA Analysis of KDOLdata from 2020-2021 (audited)
Kansas Legislative Division of Post Audit
16
o KDOL identified about $306 million in potentially fraudulent payments
that our model did not (low confidence). These are claims identified by
KDOL as fraud, but not by our neural network. This could be for two
reasons. First, KDOL officials told us they incorrectly flagged many
legitimate claims as fraud during the pandemic. Further, KDOL’s access to
other fraud detection methods could also explain why our network didn’t
flag some of these payments. However, because our neural network did
not flag these payments as fraud, KDOL officials were concerned much of
this $306 million were legitimate claims they incorrectly flagged as fraud.
Ultimately, there was no reliable way to determine how many of these
payments were legitimate. As such, we included this with the other
limitations to our estimate below.
•
Our final fraud estimate is subject to a few key assumptions and limitations.
o It is unlikely all the claims flagged in this estimate will end up being fraud.
This would overstate our estimate. As noted above, KDOL flagged
legitimate claims as fraud during the pandemic. These cases would
overstate our estimate.
o It is possible fraud occurred that neither our model nor KDOL identified.
This would understate our estimate.
o We were unable to use KDOL’s full 1099-G data as part of our estimate. A
1099-G form notifies individuals of taxes owed on state unemployment
benefits. Some victims of fraud received these forms for benefits they
never received. KDOL encouraged these individuals to contact them so
they could amend their tax form. KDOL compiled a list of fraudulent claims
based on the public’s feedback on 1099-G forms. This could have identified
additional fraudulent claims not identified by KDOL or our model. We
attempted to review that data to supplement our fraud estimate. However,
time restraints and data issues prevented a full analysis. The data we used
for our estimate contained some, but not all claims flagged as fraud from
KDOL’s 1099-G review. As a result, it is possible additional fraud exists that
was not captured in our estimate. This would understate our estimate.
o KDOL’s claims data was missing 9,000 application records that received
payment. We could not review those claims to determine fraud. This could
understate our estimate. However, given there were 1.08 million claims,
these missing applications likely have a minimal effect on our estimate.
Of the estimated $700 million in fraudulent benefit payments, about half ($343
million) came from federal funds and half ($344 million) from state funds.
•
Of the roughly $2.8 billion in benefits paid from January 2020 through
February 2021 (both fraudulent and valid), about $1.7 billion came from federal
funds for temporary pandemic programs. The other $1.1 billion came from
state funds for the state’s regular unemployment insurance program.
17
•
Of the estimated $700 million in fraudulent benefit payments, about half
came from federal funds and half from state funds.
o We estimated about $343 million in fraud from federal funds, mostly
occurring in the spring and summer of 2020. Figure 5 shows the
distribution of fraudulent payments during the pandemic, by state and
federal funding source. As the figure shows, most of the fraudulent
payments from federal programs occurred in the spring and summer of
2020. These fraudulent payments peaked in July 2020, before declining.
This coincides with the first iteration of the FPUC program expiring in July
2020. As federal programs, these fraudulent payments did not affect the
state’s unemployment trust fund.
o We estimated about $344 million in fraud from state funds, mostly
occurring at the end of 2020. As Figure 5 shows, most of the fraud to the
state’s regular unemployment program occurred in late 2020, peaking at
about $107 million in December 2020. These fraudulent payments did
impact the state’s unemployment trust fund. The balance of the trust fund
declined by about $711 million during this time. That means fraudulent
payments could have accounted for about 48% of the decline. Legitimate
payments likely accounted for the rest of the decline.
•
Fraudulent payments declined significantly in February 2021, likely because of
KDOL’s new identity verification process. In February 2021, KDOL
implemented a new identity verification system to help combat cases of
imposter fraud. Under the new system, all claimants must answer a series of
questions that only they should know before they are allowed to apply for
benefits. As Figure 5 shows, fraudulent payments from state funds decreased
from about $100 million in January 2021 to $4 million in February 2021, after
the identify verification system was implemented. That’s a decrease of about
96%.
•
Passed in 2021, Kansas House Bill 2196 included provisions to replenish the
state’s unemployment trust fund with emergency federal pandemic funding.
That included an initial payment of $250 million in 2021. Additional payments
may be made pending the results of a future contracted audit required by the
bill. Among other things, the audit will estimate how much fraud occurred in
Kansas during the pandemic.
18
We estimate about $2 billion in potentially fraudulent payments were prevented
in Kansas during the pandemic.
•
There were a significant number of fraudulent attempts in Kansas that were
never paid. Figure 6 compares total claims filed, fraudulent attempts filed,
and fraudulent attempts paid during the pandemic. As the figure shows, a
little more than half of claims filed during the pandemic were cases of
attempted imposter fraud. However, as the figure also shows, not all these
attempts were paid. We estimate about 30% of fraudulent attempts were
paid, resulting in the estimated $700 million in fraud payments reported
above. The other claims were likely stopped by KDOL fraud staff, reported by
the public, or deemed ineligible for payment.
Figure 5: Fraudst ers attacked t he federal unemployment programs
before att acking the st at e 's program in lat e 2020.
Funds paid to
potentially
fraudulent
claims(a)
Jan Feb Mar
20
20
20
$89M
•
$42M
$707M
•~700M
•
State
Funds
$25M
•
OM
•
--~£~7~
~2M
$7M
~
t $4M
Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb
20
20
20
20
20
20
20
20
20
27
27
FPUC
KOOL
Expired
Identity
Verification
Started
Source: LPAAnalysis of KOOL data from 2020-2021 (audited)
(a) Payment date errors in KDOL's claims data could affect the d istribution of fraudu lent
payments.
Kansas Legislative Division of Post Audit
19
•
We estimate about $2 billion in potentially fraudulent payments were
prevented. It is difficult to know with certainty the value of the fraudulent
attempts that were never paid. That’s because we can’t be sure which
programs the fraudsters would have been eligible for, their weekly payment
amounts, or how long they’d receive benefits. However, we applied the
average unemployment benefit amount to the number of fraud attempts
prevented to estimate this amount.
KDOL officials reported working with federal organizations and banks to identify
and recover fraudulent payments, but no estimate on recovered funds was
available.
•
KDOL officials told us that the state’s two banks, Bank of America and U.S.
Bank, are the last line of defense in identifying and preventing potentially
fraudulent payments. The banks will suspend payment on accounts they
consider suspicious. In these cases, payments are held by the bank until it can
be returned to the state. KDOL reported about $7.4 million in potentially
fraudulent payments that could be recouped from Bank of America. KDOL
officials told us their initial review confirmed $3.9 million of that total as
fraudulent, but still need to review the remaining $3.5 million. KDOL officials
Figure 6: Our results showed that fraudulent claims comprised a large
proportion of total claims filed, but not all fraudulent claims filed were
paid(a).
300K
250K
200K
150K
lOOK
SOK
0
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb
20
20
20
20
20
20
20
20
20
20
20
20
27
27
Source: LPA Analysis of KDOLd ata from 20 20 -2021 (aud ited)
(a)Payment dat e errors in KDOL's c laims data could affect the d istribution of
fraudulent claims paid
Kansas Legislative Division of Post Audit
20
told us U.S. Bank did not have an estimate of fraudulent payment stopped at
the time of this audit.
•
KDOL officials told us they continue to work with federal investigators and law
enforcement to investigate potentially fraudulent claims. A recent report from
the U.S. Department of Labor estimated that nationally, about $87 billion in
unemployment benefits could have been made improperly, with a significant
portion attributed to fraud. To date, few cases, in Kansas or nationally, appear
to have been prosecuted.
Conclusion
We did not make any additional conclusions for this audit.
Recommendations
We did not make any recommendations for this audit because the Kansas
Department of Labor is already in the process of modernizing its unemployment
insurance system.
Agency Response
On August 6, 2021 we provided the draft audit report to the Kansas Department of
Labor (KDOL). We made some minor changes based on the department’s feedback.
KDOL’s response is included below. In its response KDOL suggested $306 million of
claims KDOL flagged as fraud should not be included in our $700 million fraud
estimate. The $306 million were claims KDOL flagged as fraud that our neural
network did not detect. In its response, KDOL officials were concerned much of this
$306 million may have been incorrectly flagged as fraud by KDOL. Ultimately, we did
not remove the $306 million from our estimate. That’s for two main reasons.
•
KDOL’s fraud detection process likely identified fraudulent claims that our
neural network didn’t. Our neural network was only trained to identify wide-
scale imposter fraud. KDOL’s process also focused on multiple types of fraud,
like individual fraud and false employer schemes. Further, KDOL had access
to additional data (bank account, routing numbers, and I.P. addresses),
different fraud detection tools, and relied on the public to report fraud.
•
There was no way to verify KDOL’s assertion that a significant amount of the
$306 million it flagged as fraud were legitimate claims. This will require
additional investigation by KDOL. We acknowledge it is possible our
21
estimate included some legitimate claims that were incorrectly flagged as
fraud by KDOL. This would overstate our estimate, which we noted in our
report.
Finally, although some of the $306 million KDOL flagged as fraud may have been
legitimate (which would overstate our estimate), it’s also likely that neither we nor
KDOL have identified all fraudulent claims (which would understate our estimate).
Consequently, we think our $700 million estimate is very reasonable.
22
Kansas Department of Labor
401 SW Topeka Boulevard
Topeka, KS 66603-3182
Arn ber Shultz, Secretary
August 23, 2021
Matt Etzel
Legislative Division of Post Audit
800 SW Jackson St., Suite 1200
Topeka, KS 66612
Department of Labor
Office of the Secretary
CONFIDENTIAL
Phone (785) 296-5000
Fax (785) 368-6294
dol.ks.gov
Laura Kelly, Governor
RE:
Response to Part 2 of LP A's Evaluation of the Kansas Department of Labor's Response to
COVID-19 Unemployment Claims
Dear Mr. Etzel:
The Kansas Department of Labor (KDOL) recognizes the time the Legislative Division of Post
Audit (LP A) spent understanding and analyzing the unemployment insurance (UI) and Lost Wages
Assistance (LW A) benefit programs ( collectively "benefit programs") in the context of the
COVID-19 pandemic. The agency largely agrees with points LP A has raised about the difficulties
KDOL has faced by implementing and in some cases creating these programs with extremely
antiquated technology, ever-changing statutory requirements, federal guidance, and inadequate
staffing, resulting in part from low federal funding that at the time the pandemic began was tied to
Kansas's previous year of extremely low unemployment. The unemployment system and related
policies are complex, and KDOL appreciates the difficulty of the task LP A had, to learn about and
acknowledge that unlike traditional periods of higher unemployment where the unemployment
rate rises over several months, here, the "immediacy of the pandemic caused unemployment rates
to surge very quickly," to a rate that is unprecedented in modem times and far exceeds what the
state saw during the Great Recession in 2009 and 2010 .1
Moreover, KDOL recognizes LP A's efforts to fulfill its legislative direction to further estimate the
amount of fraud that occurred in the benefit programs administered by KDOL during the
pandemic. LP A and KDOL appear to have consensus on an estimate of $309 million; LP A has
high confidence in another $71 million; any amount beyond that, LP A and KDOL agree is low
confidence. T11erefore, KDOL reads this audit to state that fraud identified by LP A's methodology
is $380 million, which we believe is a reasonable estimate. The additional amount of $306 million
provided by KDOL but not identified as fraud by LP A, which LP A includes in its total, includes
a high number of claim payments that KDOL has not yet reconciled. Therefore, the $306 million
that LP A includes in its estimate should be rejected until it can be further investigated and
accounted for.
1 LPA Audit, Evaluating the Kansas Department of Labor's Response to CO VID-19 Unemployment Claims (Part 2),
August 2021, page 5.
23
Legislative Post Audit
August 20, 2021
Page2 of7
The low confidence of additional estimated amounts shows that at this time there is not a reliable
fraud measurement for the benefit claims paid out during the pandemic. It further supports the
need for the upcoming audit, which if properly conducted, KDOL hopes will yield a more
definitive estimate of the amount of benefit fraud during the pandemic.
KDOL's response below provides some additional clarity and context on the benefit programs and
issue pertaining to fraud in those programs. As noted below, USDOL-Office of Inspector General
(OIG) reported at one point that nearly $89 billion in UI fraud occurred. Compare that with a
possible estimate of more than $400 billion that was more recently reported by at least one industry
professional whose organization testified before the Unemployment Compensation Modernization
and Improvement Council in July of this year. 2 Clearly, the scope of fraud is not yet known and
cannot possibly be known at this time.
A spokesperson for the Texas Workforce Commission was quoted as saying: "Fundamentally, the
[UI] system is trying to do two things simultaneously that are at odds with one another: ensure
qui ck payments to individuals and prevent fraud. "3 (Emphasis added.) Kansas also experienced
these competing priorities, and not only does federal law require prompt payment, but during the
pandemic there was overwhelming constituent and legislative pressure to pay benefits to distressed
Kansans as quickly as possible. Unfortunately, fraudsters - likely both foreign and domestic - took
advantage of this situation. The simple truth is that Kansas's and other states' antiquated
technology was likely no match for sophisticated criminal syndicates. Compounding the problem
was the lack of a timely and cohesive response from the national level on how USDOL would use
national resources in preventing UI fraud.
I.
KDOL does not disagree with LPA's background of the benefit programs, but
believes it is oversimplified and KDOL would like to provide additional information
to clarify the complexity ofCOVID-19's impact on existing and new UI programs as
well as the new L WA program.
LPA identified that at the beginning of the COVID-19 pandemic, Congress passed the CARES Act to
help individuals whose employment was negatively impacted. Generally speaking, states implemented
quarantine measures the week of March 15, 2020, and the CARES Act passed through both chambers
of Congress and was signed into law on March 27, 2020. This was an unbelievably fast time frame for
legislation that fundamentally changed the way UI benefits were paid out to claimants and who were
2 Salmon, Felix, Half of the pandemic's unemployment money may have been stolen, Axios, issued June 10, 2021,
available
at
https ://www .axios.com/pandemic-unemployment-fraud-benefits-stolen-a937ad9d-0973-4aad-8 l 4 f-
4ca47b72f67f.html ("Blake Hall, CEO of ID.me, a service that tries to prevent [ unemployment insurance] fraud, tells
Axios that America has lost more than $400 billion to fraudulent claims."); ID.me testimony before Unemployment
Compensation
Modernization
and
Improvement
Council,
July
13,
2021,
available
at
http//www.kslegislature.org/li/b202l 22/committees/ctte ot unemployment compensation modernization 1/docu
ments/testimony/20210713 01.pdf.
3 How Unemp/oymentlnsurance Fraud Exploded During the Pandemic, Mother Jones, James Bernsen, Texas Workforce
Commission,
quoted
Ill
article
dated
August
4,
2021,
issued
August
4,
2021,
available
at
https://www.motherjones.com/politics/2021/08/how-unemployment-insurance-fraud-exploded-during-the-pandemic/.
24
Legislative Post Audit
August 20, 2021
Page3 of7
eligible to receive UI benefits. All of this change happened simultaneously while Kansas experienced
the highest unemployment rate, from 3.2% to 12.6% almost overnight, since at least 1976.4
LPA mentioned that the CARES Act created three brand new UI programs: Pandemic Emergency
Unemployment Compensation (PEUC), Federal Pandemic Unemployment Compensation
(FPUC), and Pandemic Unemployment Assistance (PUA). When the FPUC program expired in July
2020, the President created the LW A Program, which provided an additional $300 supplemental
payment on top of a claimant's UI benefits for six weeks, which LP A also includes in its report. 5 Not
since the creation of the unemployment insurance system has there been this type of authorization of a
completely new program
or in the case of PUA, a completely new system. KDOL created,
implemented, and operated these programs in addition to the Regular UI and Extended Benefits (EB)
programs.
EB, though it was an existing federal-state partnership program, was triggered in Kansas during the
pandemic, where it hadn't been in place since 2012. Additionally, the levels of unemployment in the
state during the pandemic resulted in a High Unemployment Period, which triggered High Extended
Benefits (HEB), essentially a new program that Kansas had never had to implement. LP A does not
include EB or HEB in its description of pandemic unemployment programs, despite the impact that
programming both EB and HEB had on KDOL's ability to quickly and accurately pay claims during
the pandemic.
Not only do all of these programs interact with one another, nearly all of these programs were subject
to statutory amendments and ever-changing USDOL guidance. This resulted in claimants being moved
back and forth between programs throughout the pandemic. Furthermore, KDOL had to work with the
Governor's office and the Legislature to execute Executive Orders, amend Kansas statutes, and modify
existing policies to secure as much federal funding/reimbursement for the state of Kansas.
In addition to the expiration ofFPUC and LW A, PUA and PEUC were set to expire in late December.
At the eleventh hour, Congress passed and the President signed the Continued Assistance to
Unemployed Workers Act (Continued Assistance Act) of 2020. The states did not have adequate
time to prepare for or anticipate the changes that came with the Continued Assistance Act. The
Continued Assistance Act not only extended the length and maximum amount of benefits available
under PEUC, FPUC, and PUA, but it also required the states to change how FPUC, PEUC, and PUA
operated. 6 These statutory changes were in addition to changes USDOL required the states to make in
4 U.S. Bureau of Labor Statistics, Current Unemployment Rates for States and Historical Highs/Low, last updated
July 16, 2021, available at https://www.bls.gov/web/laus/lauhsthl.htm (Can only be backdated to January 1976
because that is when the series began.).
'L WA is not a UI program. Rather, it is a grant administered by the Federal Emergency Management Agency
(FEMA). The funds used for the L WA program are normally used for disasters like hurricanes, tornados, etc. FEMA
has had challenges administering the LWA program through the states' UI programs. This required KDOL to seek
guidance from FEMA about UI matters it was unfamiliar and had not interacted with.
6 In KDOL's response to LPA's audit on February 19, 2021, KDOL mentioned how the Continue Assistance Act
changed the eligibility requirements for the PUA program to require claimants to substantiate their employment with
documentation. See pages 6-8. See also Sec. 241 of the Continued Assistance Act. However, the Continued Assistance
Act also changed, among other things, how PEUC operated and interacted with EB, new phase-out periods for PUA
and PEUC, and limitations on backdating PUA claims.
25
Legislative Post Audit
August 20, 2021
Page 4of 7
the guidance that followed. For example, USDOL created three new COVID-19 eligibility provisions
that claimants can self-certify under to qualify for PU A. 7 The expiration date for these UI programs was
extended to March 14, 2021.
Congress once again at the eleventh hour extended and maximized the amount of benefits claimants
could receive under these programs when it passed the Ameiican Rescue Plan Act (ARPA) of2021
on March 11, 2021. With another stimulus package came more statutory changes and USDO L guidance
that required KOOL to change how it operated PEUC, FPUC, and PUA.8 In total, between the CARES
Act, the Continued Assistance Act, and the ARP A:
•
PEUC - 53 weeks of benefits available for weeks of unemployment through the week
ending on or before September 6, 2021 (these benefits are available after 26 weeks of
regular UI are exhausted).
•
FPUC - Reauthorized to provide $300 in supplemental benefits per week through the week
ending on or before September 6, 2021.
•
PUA - 79 weeks of benefits available for weeks of unemployment through the week ending
on or before September 6, 2021.
With the expiration of PEUC, FPUC, and PUA quickly approaching, USDOL is releasing more
guidance to the states on how to close out these programs. But, KDOL's obligation to administer these
programs does not end simply because these UI programs expire. For years to come, KDOL will have
to continue to hear claim appeals, recover overpayments, and investigate fraud in these benefit
programs.
Each time Congress passed a new stimulus package or USDOL released new guidance on the UI
programs, hundreds, if not thousands, of hours of staff time was needed to make the necessary and
required changes. Figure 1 is incomplete because LP A indicates that there were merely nine federal
program revisions that "required KOOL to modify its UI system." Since March 12, 2020, when USDOL
released it first piece of guidance to the states on COVID-19 and its impact on the regular UI program,
USDOL-Employment and Training Administration (ETA) has released at least 47 pieces of formal
guidance to the states regarding the federal stimulus packages, combatting fraud, recovering
overpayments, and permissible state flexibilities in the benefit programs in light ofCOVID-19.9 Much
of the guidance required KDOL to modify previously established eligibility requirements, reevaluate
whether a claimant was receiving benefits from the proper program, in addition to editing underlying
code and testing changes before development.
7 See UIPL 16-20, Change 5, Expanded Eligibility Provisions for the Pandemic Unemployment Assistance (PUA)
Program, available at https://wdr.doleta.gov/directives/attach/UIPL/UIPL 16-20 Change 5.pdf
8 For example, USDOL previously advised the states that they were prohibited from imposing any monetary penalty
on CARES Act overpayments. However, USDOL issued subsequent guidance that the states were required to impose
a monetary penalty on all fraudulent CARES Act overpayments, effective on different dates depending on the benefit
program. UIPL 20-21, State Instructions for Assessing Fraud Penalties and Processing Overpayment Waivers under
the
Coronavirus Aid,
Relief
and Economic Security
(CARES)
Act
as
Amended,
available
at
https:/ /weir. doleta.gov /directives/attach/UIPL/UIPL 20-21 .pdf
9 USDOL-ET A, available at https://wdr.doleta.gov/directives/search-new.cfm?type~l . This number does not include
additional guidance KDOL received from FEMA on the LWA program, USDOL-OIG, or informally from the
USDOL-ETA Regional and National offices.
26
Legislative Post Audit
August 20, 2021
Page 5 of7
Each additional piece of guidance from USDOL required states to coordinate additional questions with
the regional office and wait for response from the national office. KDOL has submitted over 130
questions to the USDOL regional office. LP A cites to the May 2021 USDOL-OIG audit that found that
on average, states with modem systems implemented the PUA and PEUC programs earlier than states
without. But, LP A did not mention that in the same audit, USDOL-OIG also found that states struggled
to implement the CARES Act programs, regardless of how modernized their system was, because
"guidance from ETA was untimely and unclear."1° KDOL, at times, had to wait months for answers
to questions, which sometimes conflicted with answers other states received when they asked the same
questions. This was especially evident in the context of what KDOL could do to combat fraud. 11 This
was also contrary to what USDOL-O IG has recommended to USDOL-ET A throughout the pandemic,
which was to provide guidance to the states on how to deploy "resources efficiently and expeditiously"
and "timely oversight to ensure states are effectively carrying out" the critical responsibility to combat
improper paymcnts.12
The complexity of the unemployment system, recognized technological inadequacy of the UI
mainframe, COVID-19 stimulus packages, and USDOL guidance that KDOL is required to comply
with cannot be understated or overlooked. Congress's continued amendments to the CARES Act
programs and USDOL's untimely, unclear, and changing guidance significantly inhibited KDOL's
ability to manage, and sometimes to create, the different benefit programs that all hinge upon one
another.
II.
KDOL agrees with LP A regarding the difficulty in estimating fraud and
acknowledges a more accurate assessment is forthcoming.
As LP A recognized in Part 1 of this Audit, fraudsters nationwide successfully targeted UI programs
during the pandemic.13 LP A did consider the impact of sophisticated international criminal syndicates,
which have been recognized as being heavily involved in committing fraud in these benefit programs.
In Part 2 of this Audit, LP A now recognizes about 59% of Kansas claim applications could have been
10 USDOL-OIG-Office of Audit, Reporl to !he Employment and Training Administration COVID-19: States
Struggled to Implement CARES Act Unemployment Insurance Programs, issued May 28, 2021, pages 18-19, available
at https://www.oig.dol.gov/public/reports/oa/2021/19-21 -004-03-315.pdf.
11 USDOL-OIG- Office of Audit, Reporl to the Employment and Training Administration COVID-19: States Cite
Vulnerabilities in Detecting Fraud While Complying wilh the CARES Act UI Program Self-Certification Requirement,
issued October 21, 2020, available at https://www.oig.dol.gov/public/reports/oa/2021/19-21-001-03-31 5.pdf. LPA
cites USDOL-OIG's October 2020 report to mention how outdated mainframe systems had compatibility issues with
the PUA program. However, that report found that states struggled with the PUA program even more than their
outdated mainframe systems because of (1) a shortage of resources to handle the volume of claims, and (2) ET A
guidance was untimely and unclear. See page 6.
12 USDOL-OIG-Office of Audit, Alert to Significant Concerns !hat Cause USDOL to be at Particular Risk of Fraud,
Mismanagement,
Waste,
Deficiencies,
or
Abuse,
issued
March
31,
2021,
available
at
https :/ /www.oig.do I.gov /significant concems.htm.
13 LP A Audit, Evaluating !he Kansas Deparlment of Labor's Response to CO VID-19 Unemployment Claims (P arl 1 ),
August 2021, available at https://www.kslpa.org/audit-report-librmy/evaluating-the-kansas-department-of-labors-response-
to-cov id-19-unemploym ent-claims-part-1/.
27
Legislative Post Audit
August 20, 2021
Page 6 of7
fraudulent attempts.14 USDOL-OIG explains that identity thieves and organized criminal groups have
found ways to exploit UI weaknesses, and these issues were exacerbated by the significant funding
increase in response to the CO VID-19 pandemic.15
In March 2021, USDOL-OIG estimated more than $89 billion CARES Act program-related UI benefit
payments could have been paid improperly, with a significant portion attributable to fraud. 16 This is
reflected in the reporting of other states. In Oklahoma, a state audit found that $1.04 billion went to
fraudulent claims.17 In California, at least $31 billion were paid out to fraudulent claims.18
In Kansas, LP A and KDOL both agree on a high confidence estimate of $380M as identified by the
neural network tool. KDOL believes that the remainder of the LP A estimate of fraudulent claims cannot
be considered accurate without further review, and that a more accurate estimate will be determined
through the audit conducted by the independent firm selected by the Unemployment Compensation
Modernization and Improvement Council established in 2021 Senate Substitute for Substitute for House
Bill No. 2196. As stated by the LPA in Part 2 of this Audit, "[i]t is unlikely all the claims flagged in this
estimate will end up being fraud. This would overstate our estimate. "1 9
Additionally, LP A focuses on fraudulent payments due to identity theft in its report, while KDOL staff
are focused on multiple types of fraud, including identity theft, wage/earnings reporting fraud (when an
individual becomes reemployed but continues to file for unemployment benefits) and fictitious
employer schemes (where an individual uses a fake employer to gain eligibility for unemployment
benefits).
LP A recognizes that KOOL prevented about $2 billion in potentially fraudulent payments during the
pandemic. 2° KDOL believes that a more accurate estimate of the fraud prevented by the agency is more
than $20 billion when the potential maximum payout of fraudulent claims is considered. This number
14 LPA Audit, Evaluating the Kansas Department of Labor's Response to COVID-19 Unemployment Claims (Part
2), August 2021, page 8.
15 USDOL-OIG-Office of Audit, Alert to Significant Concerns that Cause USDOL to be at Particular Risk of Fraud,
Mismanagement,
Waste,
Deficiencies,
or
Abuse,
issued
March
31,
2021,
available
at
https://www.oig.dol.gov/significant concerns.htm.
16 USDOL-OIG- Office of Audit, Alert to Significant Concerns that Cause USDOL to be at Particular Risk of Fraud,
Mismanagement,
Waste,
Deficiencies,
or
Abuse,
issued
March
31,
2021,
available
at
https://www.oig.dol.gov/significant concerns.htm; See also Tergesen, Anne, Unemployment-Benefits Fraud Has
Soared in the Pandemic. Here's What to Do., The Wall Street Journal, issued April 29, 2021, available at
https :/ /www. ws1 . com/ articles/unem p loym ent-bene fits-fraud-has-soared-in-the-pandemic-heres-what-to-do-
1161968860 l.
17 Toolis, Brittany, State Auditor Thinks Almost 2(!'/o of Unemployment Claims Were Fraudulent, News 9, available
at
https://www.news9.com/story/6108377 l 54dd3b l 4d698d520/state-auditor-thinks-almost-20-of-unemployment-
claims-were-fraudulent (Reporting that the Oklahoma's State Auditor estimates that nearly 20% of the unemployment
payments went to fraudulent claims).
18 CA EDD admits paying as much as $31 billion in unemployment funds to criminals, ABC? News, issued January
25, 2021, available at https://abc7news.com/california-edd-unemployrnent-fraud-ca-scam-insurance/1001 l 810/.
19 LPA Audit, Evaluating the Kansas Department of Labor's Response to COVID-19 Unemployment Claims (Part
2), August 2021, page 14.
20 LPA Audit, Evaluating the Kansas Department of Labor's Response to COVID-19 Unemployment Claims (Part
2), August 2021, page 15.
28
Legislative Post Audit
August 20, 2021
Page 7 of7
is expected to increase as KOOL continues to review claims and those that may be revealed as
fraudulent in the subsequent audit.
KDOL continues to work with federal and local law enforcement to criminally investigate identified
fraudulent claims. These cases will continue to be referred to the United States Attorney's Office and
local district attorneys' offices for criminal prosecution and recovery of fraudulent payments.
Thank you for LP A's work on this Audit, and KDOL appreciates the opportunity to submit this
response.
Respectfully,
Amber Shultz
Secretary of Labor
Kansas Department of Labor
29
Appendix A – Cited References
This appendix lists the major publications we relied on for this report.
1. COVID-19: States Cite Vulnerabilities in Detecting Fraud While Complying with
the CARES Act UI Program Self-Certification Requirement (October, 2020).
U.S. Department of Labor Office of the Inspector General.
2. COVID-19: States Struggled to Implement CARES Act Unemployment
Insurance Programs (May, 2021). U.S. Department of Labor Office of the
Inspector General.
3. Status of State UI IT Modernization Projects (February, 2020). National
Association of State Workforces Agencies.
30
Appendix B – Fraud Analysis
This appendix contains further information about the methodology and results of
the fraud analysis presented in this report.
We used a neural network model to help us find fraud using risk indicators.
•
A neural network is a type of advanced machine learning model. Models like
this are used to map the complex relationships that exist between a series of
input variables and output variables. In this audit, the input variables were the
fraud risk indicators (along with some basic application information), and the
output variable was a binary determination of “fraud” or “not fraud”.
•
Machine learning models like a neural network train themselves on a set of
complete data before they can be used to predict or classify new data.
o Complete data is data for which the outcome is already known. In this
case, the outcome is whether a claim is fraudulent.
o Training consists of running the model through repeated iterations of a
learning algorithm, during which time the model teaches itself to
minimize error in its predictions.
o Once trained, the model can predict the outcomes for data that does not
yet have an outcome.
We reviewed 1,000 sampled claims applications to generate a dataset to train
the neural network.
•
We chose a random sample of 1,000 applications from a universe of about
737,000 unique claims applications to review. Later, KDOL sent an additional
343,000 claims applications, increasing total claims to about 1.08 million. All
claims data contained the same fields. Drawing a sample from the roughly
737,000 claims wasn’t problematic for two reasons. First, there was no
evidence of significant differences in claims characteristics between the two
data sets. Second, our final fraud estimate did not rely on the proportion of
fraud found in the sample. That’s because we used a neural network, which
searches for fraud on a claim-by-claim basis.
o Using 26 fraud indicators we reviewed each application in the sample to
determine the possibility of fraud. Fraud indicators included duplicate
passwords, security words, and e-mail addresses. We also reviewed
residential, mailing, and employer addresses for suspicious information.
We used those and other indicators to help us assess fraud.
o For each determination, we marked our conclusion (fraud or not fraud)
with a confidence level of low, medium, or high.
31
o We also used a formula backup to help designate fraud for ambiguous
cases. The formula assigned a numerical weight to each fraud indicator,
giving each claim a risk score. We relied on the score to help determine
fraud when we could not reach consensus or had low confidence.
Various neural networks were trained and tested on the applications we
reviewed.
•
The 1,000 reviewed applications were randomly split into 3 distinct sets: the
training set (700), the validation set (100), and the test set (200).
o The neural network model reviews the training set while iterating through
the training algorithm.
o The validation set was used to compare models once they were trained.
Ultimately, it’s how the preferred model was chosen. When validating, it
was important to use data the models had not seen. This helped assess the
models’ accuracy in predicting outcomes of new, unseen data. The final
model was selected for its high accuracy and low degree of bias.
o We then tested the validity of the final model. Once the preferred model
was selected, we tested it one final time. To do this we used the final set of
200 applications the model had not seen. Thus, a model’s performance on
the test set is a strong indicator of the model’s performance. The test
showed the model predicted the correct outcome 183 of 200 times (91.5%).