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UOB, Intel partner for cross-border AML checks

UOB, Intel partner for cross-border AML checks

Singapore lender United Overseas Bank (UOB) has successfully tested the use of federated data analytics for conducting cross-border anti-money laundering (AML) checks.

The project, done in collaboration with technology giant Intel, leveraged advanced data analytics to track the extent of transactions made across countries and entities by a single client.

UOB created scenarios and datasets to simulate transactions by a customer with bank accounts in Singapore and Thailand. The Monetary Authority of Singapore (MAS) and the Bank of Thailand were independent observers for the project.

An Intel spokesperson told Citywire Asia that while UOB’s pilot test covered only corporate banking, the solution can cover both corporate and private banking activities.

As cross-border transaction data sits in multiple localities, maintaining data sovereignty and determining the risk of money laundering across geographies can be difficult and complex, UOB explained in a statement.

Federated data analytics can address this issue as it reads data from multiple sources without the need for information to sit in a single repository.

The analytics model facilitates the sharing of algorithms across different geographical data sites without the need to share the data itself. These algorithms are designed to draw out specific insights, indicators and patterns to identify money laundering activities within the global banking system.

This in turn can enable financial institutions to have an expanded view of a customer’s banking activities across borders without breaching privacy and data protection regulations.

The joint project between UOB and Intel was co-funded by MAS’ artificial intelligence and data analytics grant under the financial sector technology and innovation scheme.

UOB has made other investments into AML technologies this year. In August, the bank announced that it has teamed up with regulatory technology firm Tookitaki for improved money laundering detection capabilities.

The bank co-created machine learning features for Tookitaki’s anti-money laundering software to better analyse and track high-risk individuals and companies as well as suspicious activities.

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