Welcome to Workforce 3 One U.S. Department of Labor Employment and Training Administration Webinar Date: June 2, 2014 Presented by: Office of Unemployment Insurance U.S. Department of Labor Employment and Training Administration

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3# Moderator: Brad Wiggins Organization: Office of Unemployment Insurance, U.S. Department of Labor

Presenter: Paul Soczynski Organization: Accuity Solutions 4# Presenter: Jim Van Erden Organization: National Association of State Workforce Agencies Presenter: Tom McLaughlin Organization: Early Warning Services Presenter: Connie Reinhardt Organization: Early Warning Services Presenter: Janis Simm Organization: Early Warning Services

5# 1.Background on the Financial Data Pilot 2.Process and Matching Used in Phase I 3.Results from Phase I 4.Next Steps for Phase II

Presenter: Jim Van Erden Organization: National Association of State Workforce Agencies 6#

UI improper payments due to Benefit Year Earnings (BYE) represented nearly 30 percent of total overpayments. OUI asked ITSC to identify vendors who could access data from payroll firms and/or financial institutions to improve time to detection. ITSC issued an RFP in 2011, several firms inquired as to whether DOL/ETA and subsequently states had the authority to access the necessary records at financial institutions. While it was agreed federal authority existed, financial institutions, subject to the Fair Credit Reporting Act, required a informed consent agreement from UI claimants. None of the three pilot states had such an agreement in place. #7

The purpose of the project was to test the efficacy of the use of financial institution data. Sooooo It was decided to use historical data (18 months) available to one vendor and match that with similar data from the pilot states. This approach would not result in taking action against a claimant but would allow a determination of: –When the state detected a potential BYE vs. when the financial data detected a potential BYE. –How many potential BYEs were detected that the state had not found.. –Metrics defining the differences The results of the effort follow. #8

Presenter: Paul Soczynski Organization: Accuity Solutions 9# Presenter: Tom McLaughlin Organization: Early Warning Services Presenter: Connie Reinhardt Organization: Early Warning Services Presenter: Janis Simm Organization: Early Warning Services

10 Background: This effort was an OMB financed project via the DOL managed by CESER to determine if the matching of financial institution Account Owner Elements data (AOE) and Automated Clearing House/Direct Deposit (ACH) data can identify individuals who are receiving UI benefits and who are also employed or become employed without reporting their status in violation of the law. Focus: A three state pilot analyzing 15 months of state UI benefit data paid from Jan March 2012 was utilized to determine if Early Warning can identify and risk score individuals with wage related violations who did not report employment to state UI agencies during that period and to determine the timeliness of such identification. Primary data was received from the States and project analysis began in November, 2012 and was completed in March Key Objective: This project was intended to determine if the approach can provide a valuable tool to state UI agencies to improve early detection of potential wage related violations and identification of the paying employers for state investigation. It was designed to supplement, not replace current state fraud detection efforts.

11 1.Financial Institution contributed data coverage may be a factor in some states. 2.The availability of deposit account numbers is/will be an important factor in higher match rates. In some states, benefits are distributed solely by debit cards using a unique ID that is not identifiable in ACH and in a few other states, the account numbers are held by the UI debit payment processor. 3.Interpretation and production of planned standard File Data Elements for preparation of state data files for transmission to Early Warning was inconsistent creating delays and requiring follow up contacts and re-transmission of data. 4.Risk score definitions as initially envisioned required revision based on the availability of more specific individual data in the ACH database. 5.The UI claimant benefit payment cycles from state to state were more complex than originally envisioned including the rules regarding handling of severance and part time employment. 6.Adding informed consent and FCRA language to the state UI processes will be required in Phase II. Challenges

12 Match state data with Early Warning account owner data elements (by name and SSN) to obtain associated account numbers. Name and SSN Keep Name and SSN from AOE that match both Name and SSN from State Drop those whose names and SSN do not match (highlighted) Match

13 Match File 3 with ACH transaction data (by account number) to determine if any payroll transactions are seen for this claimant Match by Account

14 Look at the time of a claim and see if there is a payroll transaction in the same week or the week following. Look at these patterns at the claimant level (examining all accounts owned by the claimant) Sum the number of weeks there was a violation (4 in this example) Violation is defined as having both claims & payroll > $X / week for at least 2 weeks, X being the amount of the weekly earnings disregard defined by the state. Week Payroll0000$$$ ClaimXX √ √ √ √ √ EW ViolationNNNYYYY

15 Examples of claimants with multiple violation cycles.

16 Listing of state contributed “benefit weeks claimed”. Claims started Week 27 until week 42, then week 48 to 62.

17 Claimant has payroll transactions from week 1 to 33.

18 The Early Warning analysis found 44,311 potential violations/fraudulent receipt of benefits not detected by the three states with an estimated potential loss prevention of >$86 million dollars.

19 Numbers above are summaries of the three pilot states. The potential loss prevention was calculated by examining each claimant, determining which weeks there was a violation (payroll & UI benefit claimed) and adding up the UI benefit claimed during those weeks. The total potential loss prevention calculated was >$86MM on 44,311 claimants, 26% of the total benefits paid to this group of claimants. Volume Benefit Amount Claimed Benefit Amount Claimed when Violation Detected Total # Weeks Claimed Violation Weeks found by EW #$$## Total44,311334,306,07786,275,0511,050,389336,341

20 Numbers above are summaries of the three pilot states. 60% (26,478 / 44,311) had ACH transactions that are categorized as “definitely payroll” meaning that the text displayed on the claimant’s bank statement is either “PAYROLL” or some variant of this word OR the text has been confirmed to be for payroll transactions. Volume Benefit Amount Claimed Benefit Amount Claimed when Violation Detected Total # Weeks Claimed Average Weeks per Claimant Violation Weeks found by EW #$$### Total26,478203,224,95859,834,371622, ,271

21 Numbers above are summaries of the three pilot states. 40% (17,833 / 44,311) had ACH transactions that are categorized as “probably payroll” meaning that the text displayed on the claimant’s bank statement is text that typically means that the transaction is payroll and where a payment is found more than once. Volume Benefit Amount Claimed Benefit Amount Claimed when Violation Detected Total # Weeks Claimed Average Weeks per Claimant Violation Weeks found by EW #$$### Total17,833131,081,11926,440,680427, ,070

22 Where the Early Warning analysis found the same violations as the states, in 90% of the cases, Early Warning found them from nine to thirty five weeks before the states did. The 44,311 additional potential violations were not identified by the states.

23 For investigative purposes, in the highest potential risk group, Early Warning identified 61% of the employer EINs associated with the potential violation. In over 95% of the instances that an EIN was not included, a company name was present.

24 Numbers above are summaries of the three states. In the ACH files, there is a field (Company Id) that contains the EIN, when the first byte of the field is a ‘1’. All violations detected were examined to determine if the EIN was present which was defined as the first byte is a ‘1’ AND the following 9 bytes were numbers. On average 61% of those in Risk Group 1 had an EIN and 25% of those in Risk Group 2 had an EIN. On average 95% of the transactions without an EIN had a Company Name present. Risk Group 1Risk Group 2 EIN Present Name Present EIN Present Name Present Total61%96%25%93%

25 Early Warning identified over 900 claimants receiving benefits in contiguous states. Analysis indicated that approximately 6% of these individuals received benefits during the same time from two of the states.

26 Claimants from all three states were compared (using SSN) to determine if any were claiming in multiple states. 60 of these claimants received benefits totaling $250K from multiple states at the same time.

27 Analysis of the benefit data provided by states produced significant findings related to potential changes in state investigative procedures and opportunities for better understanding ways to address improper payments. This project analysis also resulted in a number of potential areas of continuing research which could provide additional understanding of the role of ACH, Checks and other automated financial tools in the prevention of improper payments.

28 1.Consider implementation of multiple state production pilots based on these results to more quickly and efficiently identify potential additional wage violations. 2.Consider implementation of the pilots in states with high financial institution coverage for greater effectiveness. 3.Develop a standard Confidentiality Agreement for participating states patterned after the Agreement developed in Phase I. 4.Utilize a standard file layout and data formats for receipt of data from states. 5.Agree on a set of standard risk score definitions based on SSN, ACH status and the timing of employment. 6.Add informed consent and FCRA language to the state adverse action processes to comply with Federal law governing Early Warning’s use of data. 7.Have states require that their UI debit card providers contribute debit card data to Early Warning for extended matching. 8.Consider the use of Early Warning’s Data Analysis capabilities as an additional value add to further identify potential indicators of fraudulent behavior. 9.Examine the potential for automated notification by Early Warning of those individuals identified as high risk of the need for them to report to the local UI office.

29 1.Analysis of the impact of using check data with the ACH data. 2.Further analysis and exploration of the reasons that Early Warning and the States each found significant additional wage related violations and potential violations but did not find some of the same ones that the other discovered. 3.Additional analysis and interpretation of possible uses of the data provided by the States. 4.More understanding of the potential value of matching Early Warning’s Fraud and Abuse Data base to predict those individuals that may warrant additional up front investigation. 5.Discussion and understanding of possible approaches to enhance the use of the New Hire Database in conjunction with Early Warning data and/or a possible relationship with use of Equifax’s TALX data in a joint approach.

Presenter: Jim Van Erden Organization: National Association of State Workforce Agencies 30# Moderator: Brad Wiggins Organization: Office of Unemployment Insurance, U.S. Department of Labor

The live pilot option has been agreed to by OMB, OUI, and ITSC as a second and distinct phase in the work envisioned as a part of the original contract. OUI & ITSC will work with states to address issues: –Data matching agreement. –Informed consent provision. Informed consent language will be added to the states weekly claims form, failure to sign will result in that claim being removed from the test file. Data will be collected for up to 6 months and returned to states on a weekly basis for follow up action. #31

Seeking interested partner state(s). Administrative funding support is available for participating states Contact OUI if interested: #32

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