As part of the initiative, UOB is augmenting Tookitakis 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. 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.
Tookitaki is a graduate of The FinLabs second accelerator programme in 2017, a joint venture between UOB and SGInnovate, a Singapore government-owned deep technology development company.
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