Fraud, waste, and abuse in government programs have long been a primary concern for state officials. Unemployment insurance (UI) is a program that has proven to be especially vulnerable and an easy target for fraud. The issue only worsened during the COVID-19 pandemic; in September 2022, the U.S. Department of Labor reported fraud estimates of about $45.6 billion since March 2020. The legacy detection strategies that states are using are no longer capable of stopping the barrage of fraudulent claims. To be able to combat fraud, waste, and abuse in this environment, states need to consider a multi-pronged approach.
For decades, UI programs have tried to address benefit fraud through detection and recovery. This strategy was driven by the main vehicle for UI fraud: individuals falsifying documents “proving” their eligibility. The UI system could identify the individuals responsible and recover the improperly paid benefits, as well as keeping track of repeat offenders.
With the rise of identity theft, the dynamic of UI fraud has changed. Now, most fraud is conducted using stolen identities, which makes it more difficult to identify fraudulent activity and recapture the stolen money. False claims with stolen identities are also much easier to submit, leading not only to increasing volumes of applications, but also a much larger population of fraudsters. Online communities have formed to share tips on identity theft and even sell stolen identities.
The shift in fraud tactics requires states to develop new strategies to combat theft. Fraud detection is still important, but the most effective way to counter fraud is prevention. The rising rate of fraud during the pandemic drove the adoption of various fraud prevention tools and solutions, such as identity validation and verification, but each comes with inherent limitations. “Some tools offered, while being accurate, are very costly, and in some instances do not provide fully automated methods of validating identity, creating an additional burden and delays for individuals filing claims,” shared Dale Smith, Director of Unemployment Insurance at TCS, in a recent blog post. “None have shown to be fully effective.”
From this, UI and fraud experts have come to understand that no single solution can offer UI programs full protection against fraud. A multi-pronged approach to fraud prevention, with different tools protecting different vulnerabilities in the system, is the best way to identify fraudulent claims before they are paid out. One of the most effective tools in fraud prevention is deep data analysis, which uses artificial intelligence (AI) and machine learning (ML) to investigate data, detect patterns, and address potential fraud cases. States are successfully combining advanced analytics with other security tools like identity verification and various state databases such as birth and death records, driver’s licenses and corrections, to verify legitimate claims at speed. By strategically deploying different solutions in various stages of the UI claim process, each tool can mitigate limitations in other parts of the system.
Emphasizing prevention doesn’t mean that agencies should abandon their fraud detection programs. Cybercriminals are working to identify and exploit new vulnerabilities as quickly as states can detect them, and there will be cases that slip through even the most robust fraud prevention methods. Fraud prevention programs need continued support from detection mechanisms to ensure up to date and optimal protection.
Predicting and responding to fraud is a necessary part of state UI programs’ ultimate goal of protecting and serving citizens with legitimate claims. A more effective fraud prevention strategy that combines multiple modern prevention tools with established detection solutions will enable states to focus attention and effort on improving constituents’ lives and supporting them in times of need.
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