UOB is hailing the accuracy of its new AI anti-money laundering technology in helping the Singaporean bank cut through large volumes of transactions to pinpoint suspicious activities.
The bank is using AI concurrently in two AML risk dimensions - transaction monitoring and name screening, helping it to pinpoint higher-priority cases from the more-than-5700 average monthly suspicious transaction alerts.
Once the AI system - which was built with regtech Tookitaki - flags suspicious activity, the bank’s compliance officers step in to conduct in-depth investigations and report to authorities.
UOB says the technology has proven an overall true positive prediction rate of 96%, in the ‘high priority’ category - those deemed most likely to be suspicious and therefore subject to earlier and more thorough investigations. The AI also screens 60,000 account names monthly to determine if they belong to the individuals or entities on global regulatory watch lists.
Victor Ngo, head, group compliance, UOB, says: "At UOB, we made early investments in artificial intelligence and began our AML proof of concept two years ago. Our AI solution works concurrently on two AML risk dimensions, which is technically more difficult, but also more fruitful as it helps us to pinpoint criminals trying to pose as customers."