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UOB, Tookitaki team up on machine learning to fight money laundering

UNITED Overseas Bank (UOB) and Singapore-based regulatory technology startup Tookitaki Holding are partnering to apply machine learning technology toward anti-money laundering efforts.

As part of the initiative, UOB is enhancing Tookitaki's Anti-Money Laundering Suite (AMLS) with co-created machine-learning features that will allow the AMLS product to conduct deeper and broader analyses of any set of data for greater accuracy beyond the existing rules-based systems, UOB said. The bank said that the integrated solution will allow it to better detect high-risk individuals and companies and suspicious activities.

UOB is currently using the system for name screening and transaction monitoring, two of four key processes within its own anti-money laundering framework. A six-month pilot showed promise, and over the next six months the bank will continue to optimise the machine learning algorithms with new transactional data, UOB head of group compliance Victor Ngo said.

Tookitaki is a graduate of The FinLab's second accelerator programme in 2017, a joint venture between UOB and SGInnovate, a Singapore government-owned deep technology development firm.