U.S. Treasury Utilizes AI to Recover $4 Billion in Fraud

The U.S. Department of the Treasury announced it recovered over $4 billion in fraud and improper payments in the 2024 fiscal year thanks to machine learning artificial intelligence (AI).  This amount is a big increase from the $652.7 million recovered last year. The Treasury started using AI in late 2022 through its Office of Payment Integrity (OPI) to detect fraud.

According to the report shared, AI helped identify high-risk transactions, leading to a recovery of $2.5 billion, while the Treasury’s check fraud detection systems also helped in recovering $1 billion. The Treasury was also able to prevent an extra $500 million in possible fraud by improving its risk-based screening methods.

In a statement, Deputy Secretary of the Treasury Wally Adeyemo said, “Treasury takes seriously our responsibility to serve as effective stewards of taxpayer money. Helping ensure that agencies pay the right person, in the right amount, at the right time is central to our efforts.” 

Moreover, the Treasury handles around 1.4 billion payments each year, totalling more than $6.9 trillion, to over 100 million people. This includes payments for Social Security, Medicaid, and federal workers’ salaries, making it a target for fraud.

In May 2024, the Treasury partnered with the Department of Labor to share AI-powered fraud prevention tools with state unemployment programs. This partnership allows state unemployment agencies to access the Do Not Pay Working System, which helps confirm who is eligible to receive funds. According to the press release, online payment fraud is expected to reach $362 billion by 2028, making the need for these efforts even more important.

Related posts

Crypto Exchange Woo X Adds AI-Powered Trader ‘George AI’ to Its Copy Trade App

AI Agents Can Help Crypto Become the Currency of AI

Robert Downey Jr. Refuses to Let Hollywood Create His AI Replica

This website uses cookies to improve your experience. To read more or opt here visit the privacy policy. Read More