The U.S. Government Accountability Office (GAO) is urging federal agencies to strengthen data quality and workforce expertise as they deploy artificial intelligence (AI) to combat fraud and improper payments, which have cost taxpayers an estimated $2.8 trillion since 2003.
In testimony before the Joint Economic Committee, GAO Chief Scientist Dr. Sterling Thomas outlined the benefits and risks of using AI tools, such as machine learning, to identify fraudulent activity in federal programs. However, he warned that without “ground truth” data and skilled human oversight, these technologies could backfire, creating costly errors and eroding public trust.
“AI does not replace the professional judgment of experienced staff,” Thomas emphasized, adding that models trained on flawed or mislabeled data could miss real fraud (false negatives) or falsely flag legitimate payments (false positives). Such errors, he said, could delay critical support to vulnerable populations.
GAO recommended several steps to improve anti-fraud capabilities. These include making permanent the Social Security Administration’s authority to share death data with the Treasury Department’s Do Not Pay system—a move that recovered $31 million in just five months. GAO also reiterated its call to establish a permanent analytics center of excellence, like the Pandemic Analytics Center of Excellence (PACE), which played a critical role in recent fraud investigations.
A major barrier to effective AI deployment remains the shortage of qualified personnel. GAO noted ongoing federal workforce gaps in AI expertise and recommended considering the creation of a “digital services academy,” akin to military academies, to build a pipeline of skilled talent.
“AI can dramatically improve fraud detection, but it is not a silver bullet,” Thomas said. “Agencies need reliable data, robust oversight frameworks, and people who understand both the technology and the mission.”
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