Singapore deploys AI to combat financial scams

In its statement, the Monetary Authority of Singapore (MAS) said as part of broader efforts to harness AI/ML for industry-wide use cases, it is conducting a Proof-of-Value (POV) to explore AI/ML techniques for pre-emptive scam detection.

Hanoi (VNA) – The Monetary Authority of Singapore (MAS) on May 4 announced that it is working with partners in the banking industry, the Government Technology Agency of Singapore, and the Singapore police force to combat financial crime, using artificial intelligence and machine learning (AI/ML) techniques to enhance scam detection capabilities.

In its statement, MAS said as part of broader efforts to harness AI/ML for industry-wide use cases, it is conducting a Proof-of-Value (POV) to explore AI/ML techniques for pre-emptive scam detection.

By bringing together data from across five banks, the POV aims to build more robust and accurate AI/ML models that help identify higher-risk transactions and accounts. Prompt identification could enable timely assessment, intervention and reduction of customer losses to scams.

To support the POV, MAS has provided industry partners with a secure data sharing environment governed by policies and protocols to safeguard customer information. Data used in the POV will remain confidential and protected with cryptographic techniques.

Bank account numbers used will be hashed, ensuring that only the contributing bank can identify actual account numbers. Data access will be restricted to authorised personnel within a controlled setting that will be continuously monitored throughout the POV. All data used will be deleted at the end of the POV.

According to MAS, the current POV lays the groundwork for deeper industry collaboration, harnessing AI/ML to enhance and complement individual financial institution’s existing efforts in preventing and countering financial crime.

After assessing its effectiveness and learning from it, MAS may expand the scope and sophistication of AI/ML models used – incorporating broader datasets, and a wider set of use cases to further fortify our financial system’s defences against criminal activity./.

VNA

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