Transforming Legacy Compliance system for a Universal Bank
A leading Singapore-based Universal Bank
United Overseas Bank (UOB) is a leading bank in Asia with a global network of more than 500 offices and territories in Asia Pacific, Europe and North America.
UOB has a strong risk-focused culture using next-generation technologies to stay vigilant in an ever-changing financial crime landscape. With the commitment to enhance its AML surveillance, UOB saw a significant opportunity to tap into machine learning to augment and enhance its existing systems to spot and prevent illicit money flows.
Implementing a True Risk-based Approach and Reducing the Rising Cost of Compliance
UOB faced a strategic imperative to optimise its alert management and address the rising cost of compliance. With the volume and velocity of transactions flowing through the bank, there was a pressing need to reduce "false positives" and efficiently close alerts.
UOB was also keen to acquire better insights from the transactions and activities of high-risk individuals and companies and suspicious activities to remain vigilant against any potential money laundering activities. Having experimented with multiple systems, UOB faced challenges in finding a sustainable solution.
High Transaction Volume
Coping with the sheer volume and velocity of transactions.
False Alert Challenges
Overcoming a staggering 90-95% false alert rate across transaction monitoring and customer name screening.
Missed High-Priority Cases
Ensuring that no high-priority cases were overlooked in the deluge of alerts.
A Future-ready ‘Self-learning’ Machine Learning Solution
UOB embarked on a transformative journey by collaborating with Tookitaki, to integrate machine learning (ML) into its anti-money laundering program. This partnership aimed to propel UOB into a future-ready 'Community-driven compliance model.' At the core of this initiative was the deployment of Tookitaki's Anti-Money Laundering Suite (AMLS) to transform the transaction monitoring and name-screening process.
Tookitaki AMLS Smart Alert Management
Tookitaki deployed its proven Smart Alert Management solutions to transform the current system for transaction monitoring and name screening. AMLS Smart Alert Management (SAM) leverages a combined supervised and unsupervised ML techniques, to swiftly detect suspicious activities and pinpoint high-risk clients with enhanced accuracy. The key components of this solution included:
- Seamless Integration: AMLS employs standardized data schema and adapters to integrate with the legacy systems
- Risk Classification: AMLS excelled in classifying AML risk, demonstrating precision through L1-L3 buckets, and maintaining an accuracy rate exceeding 85%.
- Adapting to Skewed Data Sets: During COVID-19, the alert data showed skewness due to high defensive reporting. However, AMLS showcased resilience, by adapting to the skewness and delivering consistent results.
- Reduction in False Positives: SAM showed increased effectiveness in identifying suspicious patterns and reduced false positives by 50%-70%
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Automated Model Management
The Automated Model Management framework automatically constructs a Machine Learning model trained on client data, delivering a bespoke model, instead a generic industry ML solution.
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Alert Prioritization Engine
The Smart Alert Management (SAM) Risk scoring engine employs a dual approach of supervised learning and unsupervised learning to ensure precise alert prioritization.
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Champion Challenger Framework
Tookitaki’s machine learning platform features a Champion–Challenger Framework which is a self-learning system designed to automatically adapt to changing customer data and deliver consistent performance.
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Explainable AI Framework
Cutting-edge Explainable AI (XAI) Framework, currently under patent review, pioneers a 'glass-box' solution for enhanced transparency. It provides the output of the ML model in simple human-readable language for quicker investigation and audit.
The Results
Focused on optimizing the detection of new and unknown suspicious patterns and prioritizing known alerts, UOB witnessed a significant advancement in its transaction monitoring and name screening modules.
5 %
70 %
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