US Federal News Bureau

US Treasury Department Thwarts $4 Billion in Fraud with AI — Here’s How

AI also helped the Treasury expand risk-based screening resulting in $500 million in prevention.

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Written by: CDO Magazine Bureau

Updated 4:30 PM UTC, Mon October 28, 2024

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The U.S. Department of the Treasury has recently announced that the use of AI in fraud and improper payment prevention has successfully prevented and recovered over $4 billion in FY 2024, a significant increase from $652.7 million in FY 2023.

Highlights include:

  • Expanding risk-based screening resulting in $500 million in prevention.

  • Identifying and prioritizing high-risk transactions resulting in $2.5 billion in prevention.   

  • Expediting the identification of Treasury check fraud with machine learning AI resulting in $1 billion in recovery. 

  • Implementing efficiencies in payment processing schedule resulting in $180 million in prevention. 

According to the department, this growth highlights the committed efforts of the Treasury’s Office of Payment Integrity (OPI) within the Bureau of the Fiscal Service to strengthen its fraud prevention capabilities and broaden its services to both new and existing customers.

“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. We’ve made significant progress during the past year in preventing over $4 billion in fraudulent and improper payments. We will continue to partner with others in the federal government to equip them with the necessary tools, data, and expertise they need to stop improper payments and fraud,” Deputy Secretary of the Treasury Wally Adeyemo said.

Previously, the department has said that cheque fraud has increased nationwide by 385% since the pandemic. To counter this, OPI has deployed an upgraded AI-powered process to combat the rise in fraud. 

This initiative aims to swiftly identify and mitigate check fraud in near real-time, enhancing the efficiency of recovering potentially fraudulent payments from financial institutions.

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