United Overseas Bank has teamed up with Singapore-based Tookitaki to bolster its fight against anti-money laundering.
Following a successful six-month pilot trial, the bank will apply machine learning technology from the RegTech startup and accelerator graduate across its entire AML framework.
While industry norms require banks to use multiple systems to analyse subsets of the same data of the same set of data for each process, Tookitaki’s Anti-Money Laundering Suite (AMLS) can be applied as a single rounded solution. The solution offers separate modules for the four key processes within the AML framework.
To enhance its AML surveillance, UOB co-created a number of machine learning features for Tookitaki’s solution, Anti-Money Laundering Suite (AMLS).
During the pilot, the bank applied the system to name screening and transaction monitoring, experiencing a 60% and 50% reduction in false positives for individual names and corporate names respectively. Transaction monitoring saw a five per cent increase in true positives and 40% drop in false positives.
When it spots a pattern of suspicious activity, the AMLS also creates a smart rule and adds it to the AML typology library, thus enabling the machine learning models to detect similar patterns for future alerts. This means that over time, the solution will continue to filter the number of false positives and enable more accurate tracking.
Victor Ngo, head of group compliance, UOB, said, “UOB is committed to upholding a strong risk-focused culture in which regulatory compliance is a key pillar to maintaining the trust our stakeholders place in us.
“The area of AML requires constant vigilance and continual enhancement to ensure that we stay on top of preventive, detective and enforcement measures. The use of RegTech such as Tookitaki’s AMLS enables us to augment our ability to identify actionable alerts and to minimise false positives. These sharpen the accuracy and effectiveness of our AML risk management.”
UOB said it will roll out AMLS to enhance the other two AML processes, namely customer risk assessment and sanctions screening, progressively.
Earlier this year, Mindtree partnered with Tookitaki to use AI and Machine Learning to help banks across reduce risk and improve compliance. To address the challenges facing financial institutions, the pair are now offering two new services.
Smart Alert Management uses artificial intelligence and machine learning technology to detect suspicious cases, while Smart Reconciliation Management provides an end-to-end automated approach to reconciliation management across business functions.
Copyright © 2018 RegTech Analyst
Copyright © 2018 RegTech Analyst
