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House Social Security subcommittee chair presses GAO on AI, data tools to curb improper federal payments
Summary
At a congressional hearing, the chair of the House Social Security subcommittee pressed witnesses on using machine learning and data analytics to detect improper federal payments; a GAO official described agency tools and a fraud‑risk framework, and another witness stressed avoiding false positives.
The chair of the House Social Security Subcommittee pressed witnesses on using artificial intelligence and data analytics to detect improper federal payments and reduce taxpayer losses, citing GAO estimates of large annual fraud ranges.
She told the panel that, "based on data from 2018 to 2022, GAO estimates that aggregate fraud was between $233,000,000,000 to $521,000,000,000 each year," a range she said implies losses of more than $1 trillion to as much as $2.6 trillion over five years. She asked what the Government Accountability Office and other…
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