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Unemployment Insurance Fraud in the Debit Card Market

Author

Listed:
  • Umang Khetan
  • Jetson Leder-Luis
  • Jialan Wang
  • Yunrong Zhou

Abstract

We study fraud in the unemployment insurance (UI) system using a dataset of 35 million debit card transactions. We apply machine learning techniques to cluster cards corresponding to varying levels of suspicious or potentially fraudulent activity. We then conduct a difference-in-differences analysis based on the staggered adoption of state-level identity verification systems between 2020 and 2021 to assess the effectiveness of screening for reducing fraud. Our findings suggest that identity verification reduced payouts to suspicious cards by 27%, while non-suspicious cards were largely unaffected by these technologies. Our results indicate that identity screening may be an effective mechanism for mitigating fraud in the UI system and for benefits programs more broadly.

Suggested Citation

  • Umang Khetan & Jetson Leder-Luis & Jialan Wang & Yunrong Zhou, 2024. "Unemployment Insurance Fraud in the Debit Card Market," NBER Working Papers 32527, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:32527
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    More about this item

    JEL classification:

    • G51 - Financial Economics - - Household Finance - - - Household Savings, Borrowing, Debt, and Wealth
    • H53 - Public Economics - - National Government Expenditures and Related Policies - - - Government Expenditures and Welfare Programs
    • J65 - Labor and Demographic Economics - - Mobility, Unemployment, Vacancies, and Immigrant Workers - - - Unemployment Insurance; Severance Pay; Plant Closings
    • K42 - Law and Economics - - Legal Procedure, the Legal System, and Illegal Behavior - - - Illegal Behavior and the Enforcement of Law

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