Fear of regulatory disapproval and data integrity concerns have kept some U.S. financial institutions from more fully incorporating machine learning and other artificial intelligence-based monitoring tools into their anti-money laundering programs, say sources.
The vast majority of transfers flagged as suspicious by transactional monitoring systems are eventually cleared as false positives, racking up compliance costs for financial institutions and undermining their ability to identify illegal money flows, say sources.
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