Infrastructure Summit

UOB @ SFF

Partnering with technology providers and regulators to build open, secure banking platforms

Advanced Analytics and Innovation in Financial Crime Compliance
– The Future is Now

The smarter use of AI to stop illicit money flows comes at a time1 when criminals are finding ways to evade detection by the traditional rules-based method for doing AML checks. For example, to circumvent traditional rules based on flow-through activity2 or static transaction size thresholds, criminals may transfer smaller amounts over a longer time.

UOB's new AI solution, underpinned by machine learning and greater computational power, complements the traditional rules-based method, which remains the first line of defence in identifying potentially high-risk customers and transactions. The solution will continue to sharpen its detection capabilities over time, as the model responds to changes in customer risk profiles, behaviours and transaction patterns. The AI solution also identifies links that were harder to spot in the past. For example, it can identify a customer's connection to a high-risk individual – such as a convicted drug trafficker – even if the customer were not considered high-risk.

Since its implementation, UOB's new AI solution has proven an overall true positive prediction rate of 96 per cent in the 'high priority' category. The highest of three priority tiers, the 'high priority' category contains transactions and accounts that are deemed most likely to be suspicious and are therefore subject to earlier and more thorough investigations. The AI solution also screens 60,000 account names monthly to determine if they belong to the individuals or entities on global regulatory watch lists.

The implementation journey on UOB's use of innovative solutions to combat financial crime, is documented in collaboration with Deloitte, in 3 whitepapers published in 2018, 2019 and latest volume launched this year as part of SFF. The whitepapers examine the journey of UOB's AI anti-money laundering solution, from proof of concept (POC) to production stage, explaining how it gradually calibrated models for integration into current banking operations. It outlines the justification for the Bank's investment in advanced analytics, AI/ML and robotics – noting how these have been instrumental in mitigating major disruptions.

Sharing UOB's transformation story – on its use of innovative technologies to combat financial crime – provides insight into the implementation process and relevant challenges. It sheds light on the governance of the technology, the engagement required with stakeholders to establish trust in the solutions, and how to integrate these into the operating environment. We hope that from reading the whitepapers, more FIs will be inspired to apply FCC technologies, reaping its benefits, while helping to expand FCC efforts in the industry.

  1. In a report published in June 2020, the intergovernmental group Financial Action Task Force (FATF) cited the growth in virtual assets globally as a factor that has given rise to “a more sophisticated disguise of the origins of funds” by money launderers. The FATF is a global money laundering and terrorist financing watchdog formed by G7 countries to review money laundering techniques globally and to make recommendations to governments.
  2. Flow-through activity refers to depositing money in an account before withdrawing it almost immediately.
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The third whitepaper examines the ongoing journey of UOB’s AI anti-money laundering solution, from proof of concept (POC) to production stage, explaining how it gradually calibrated models for integration into current banking operations. It outlines the justification for the Bank’s investment in advanced analytics, AI/ML and robotics – noting how these have been instrumental in mitigating major disruptions.
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The second whitepaper depicted the future-state of FCC that incorporates AI, ML, RPA and natural language processing (NLP) to manage evolving financial crime risks. It details what is involved to operationalise ML for FCC, taking reference from UOB’s successfully implemented ML model.
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The first whitepaper explains how financial institutions (FI) can leverage innovation to manage FCC effectively. It shared UOB’s case study in successfully piloting machine learning to identify suspicious accounts and transactions with greater accuracy.
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