Unemployment Insurance Fraud in the Debit Card Market

Umang Khetan, Jetson Leder-Luis, Jialan Wang and Yunrong Zhou

No 32527, NBER Working Papers from National Bureau of Economic Research, Inc

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.

JEL-codes: G51 H53 J65 K42 (search for similar items in EconPapers)
Date: 2024-05
New Economics Papers: this item is included in nep-big, nep-cmp, nep-lab and nep-law
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