Machine Learning Is Helping the US Treasury Identify Fraud

What is the best contemporary use for AI technology? While generative AI — and the often-absurd amounts of power it uses — has been getting the bulk of press lately, the real breakthroughs might come more from the machine learning side of the equation. The field has already seen promising benefits in terms of AI’s applications in reviewing massive amounts of medical records to better understand health risks.

But this isn’t the only way machine learning might be leveraged for use as a social good. The U.S. Treasury Department recently announced that it was using machine learning to detect “fraud and improper payments” and that it had prevented and recovered $4 billion worth of such payments in the latest fiscal year. Out of this total amount, approximately $1 billion was recovered as a result of “[e]xpediting the identification of Treasury check fraud.”

“We’ve made significant progress during the past year in preventing over $4 billion in fraudulent and improper payments,” said the agency’s deputy secretary, Wally Adeyemo, in a statement. “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.”

In analysis of the news, Engadget’s Sarah Fielding pointed out one apparent paradox of this announcement — namely, that AI is also frequently used to carry out fraud. Still, the category of technology is so broad at this point that it isn’t hard to see how different aspects of it can be used both for enforcing and breaking the law.

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The Treasury Department has touted its use of machine learning technology in a few different settings. Earlier this year, it announced a partnership with the U.S. Department of Labor that provided anti-fraud technology to the state agencies responsible for issuing unemployment payments. If these programs can keep the government running more efficiently, it sounds like a win for all involved. Except, possibly, for fraudsters.