UOB enhances anti-money laundering surveillance with some help from machine learning

UOB enhances anti-money laundering surveillance with some help from machine learning

Michelle Zhu
24/08/18, 04:32 pm

SINGAPORE (Aug 24): United Overseas Bank (UOB) says it has enhanced its anti-money laundering (AML) surveillance abilities with an integrated solution that enables the bank’s existing AML systems to make sharper, smarter and swifter detection of high-risk individuals & companies and suspicious activities.

This is essential given the volume, value and velocity of transactions that flow through UOB’s systems, says the bank in a press release on Friday.

The bank’s enhancement to its AML systems comes as a result of a recent collaboration with local regulatory technology (regtech) company Tookitaki Holding, says UOB, which saw the co-creation of a number of machine learning features for Tookitaki’s solution, Anti-Money Laundering Suite (AMLS).

Tookitaki is a graduate of the second accelerator programme of The FinLab, a joint venture between UOB and SGInnovate.  

After testing the effectiveness of AMLS over a six-month pilot, the bank has subsequently applied the solution for two of the four main processes within the AML framework, specifically ‘name screening’ and ‘transaction monitoring’.

In ‘name screening’, the bank identifies high-risk individuals and entities based on internal and external watch lists, enabling it to assess more accurately the risks involved to bank them, and to remain vigilant against AML activities by existing customers.

On the other hand, ‘transaction monitoring’ involves the identification and reporting of suspicious transactions of investigation.

UOB says it intends to progressively roll out AMLS to enhance the other two AML processes, namely ‘customer risk assessment’ and ‘sanctions screening’.

According to the bank, these new features enable AMLS – which is unique to the market as it can be applied to all processes within the AML framework – to conduct deeper and broader analyses of any set of data for greater accuracy.

When it spots a pattern of suspicious activity, the AMLS 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 the solution will continue to filter the number of false positives and hence contribute to more accurate tracking over time, all while boosting the productivity of UOB employees by enabling them to focus on other related tasks.

“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,” says Victor Ngo, head of group compliance, UOB.

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