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United Overseas Bank: Machine Learning-powered Alert Triaging for AML Transaction Monitoring and Name Screening

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Celent have reviewed this profile and believe it to be accurate.
8 March 2021
Arin Ray

Winner of Celent Model Risk Manager 2021 Award for Data, Analytics, and AI

Abstract

United Overseas Bank recognized the potential of artificial intelligence (AI) and machine learning (ML)–powered solutions to strengthen its control and streamline AML operations. Its co-development and implementation of Tookitaki’s AML Suite (AMLS) is a leading use case of AI application in AML, especially because the bank has applied it across both transaction monitoring and name screening processes at the same time.

The solution has resulted in impressive outcomes in high accuracy in identifying both true positives and false positives and enhanced efficiency and effectiveness of the bank’s AML program. It strengthens UOB’s financial-crime compliance operations by allowing the bank to draw out faster and more precise information to detect and prevent suspicious money-laundering activities.

Click on the video link below to watch a conversation between Victor Ngo, Head of Group Compliance at UOB, and Arin Ray, Senior Analyst with Celent's Risk practice; Celent Risk research members can download the PDF of a detailed case study.

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Video

Insight details

Insight Format
Reports
Geographic Focus
Asia-Pacific, EMEA, LATAM, North America