Joint Economic Committee hears bipartisan push to use AI to reduce federal waste, fraud and abuse

Chairman Schweickart, chair of the Joint Economic Committee, opened a hearing where witnesses from the Government Accountability Office, academia and the inspectors general community urged Congress and federal agencies to use data analytics and artificial intelligence to reduce improper payments and fraud across federal programs.

The issue is large in scale: “The federal government reported an estimated $162,000,000,000 in payment errors or improper payments during fiscal year 2024, and that is almost certainly an underestimate,” said Dr. Sterling Thomas, chief scientist at the Government Accountability Office (GAO), during his opening testimony.

Why it matters: witnesses told members that improper payments and fraud appear most concentrated in large benefit and health programs and that established analytics techniques used in the private sector — anomaly detection, natural language processing, graph analytics and robotic process automation — could be applied to speed service to legitimate beneficiaries and flag suspicious activity earlier.

Testimony and proposals

Dr. Thomas said the GAO supports a cautious, staged approach: improve traditional anti‑fraud methods now, apply AI where data are reliable, and build workforce capacity. “Before pouring data science on a problem, we…