How analysis of cross-border payments busted a child trafficking ring

In this week’s episode of the Buzz, Yaron Hazan, vice president of regulatory affairs for cybercrime and big data analytics vendor ThetaRay, breaks down for Bank Automation News how the tool’s machine learning and AI discovered an unusual pattern in cross-border payments processed by a bank. The transactions were labeled as medical tourism but hid a much darker secret - child trafficking.

Artificial intelligence can pick up on patterns humans would ignore — patterns that can indicate crime in cross border payments, for instance. 

In this week’s episode of the Buzz, Yaron Hazan, vice president of regulatory affairs for cybercrime and big data analytics vendor ThetaRay, breaks down for Bank Automation News how the tool’s machine learning and AI discovered an unusual pattern in cross-border payments processed by a bank. The transactions were labeled as medical tourism but hid a much darker secret - child trafficking.  

Twenty-seven percent of human trafficking victims are children, according to the United Nations. By leveraging machine learning and AI technologies, financial institutions can help stop this and other horrific crime rings that he struggled to stop during his time in the Israeli military and law enforcement, Hazan says.   

“After 25 years of career of chasing bad guys, feeling that I'm one step ahead of them is a very good feeling,” Hazan says. “In terms of the industry, and even humanity, when we think about child trafficking, terrorist funding, and all these worst phenomena that we think that are impossible to manage to detect to fight against, I say it’s possible.”

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