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Singapore Banks Embracing Machine Learning to Prevent Financial Crime

In a bid to stay ahead in the fight against financial crime, banks in Singapore are increasingly turning to machine learning (ML) technology. According to industry insiders, this move is driven by the need for greater efficiency and accuracy in detecting suspicious transactions.

The Rise of AI-Powered Transaction Monitoring

Recently, OCBC Bank announced that it has partnered with fintech company ThetaRay to implement AI-powered transaction monitoring. The bank’s Fintech unit, The Open Vault at OCBC, conducted a proof-of-concept exercise earlier this year, which showed promising results. OCBC is now in the extended proof-of-concept and pre-implementation phase, involving advanced testing with additional data sets to verify the efficacy of the solution.

• Benefits of AI-powered transaction monitoring:
  • Speed: allows for faster detection of suspicious transactions
  • Efficiency: reduces manual review time and increases accuracy
  • Ability to cover vast volumes of transactions

Intel’s Saffron AML Advisor Program

Intel also announced that Bank of New Zealand (BNZ) has joined its Saffron Anti-Money Laundering Advisor program. The AI-powered solution uses associative memory to detect financial crime by analyzing vast amounts of data and identifying patterns that may indicate suspicious activity.

• Key features of Intel’s Saffron AML Advisor:
  • Uses associative memory to detect complex patterns
  • Analyzes vast amounts of data to identify suspicious activity
  • Provides unprecedented speed and efficiency in detecting money launderers

OCBC’s Fintech Solution

OCBC’s Fintech solution uses an algorithm that can detect anomalies in transaction behavior by assessing broad parameters, including products, customers, risks, and diverse data sources. This approach allows for better precision in flagging suspicious transactions and discovering new patterns for smarter future detection.

• Results of OCBC’s proof-of-concept exercise:
  • Reduced number of alerts not requiring further review by 35%
  • Increased accuracy rate of identifying suspicious transactions by more than four times

Industry Experts Weigh In

Industry experts agree that machine learning is a game-changer in the fight against financial crime. Gayle Sheppard, vice president and general manager of Saffron AI Group at Intel, notes that the amount of data banks and insurers collect is growing at massive scale.

• Quotes from industry experts:
  • “Machine learning is a game-changer in the fight against financial crime.” - Gayle Sheppard
  • “Financial crimes are evolving in complexity and sophistication. This is why we strongly believe in embracing technology and tools that will increase our proficiency in transaction monitoring.” - Loretta Yuen, OCBC Bank’s Head of Group Legal and Regulatory Compliance

Conclusion

By supplementing traditional review of transactional and customer data with machine learning solutions, financial institutions can greatly enhance their reputational, operational, and legal risk exposure. Qantaverse, a finance data science solution provider, advocates for the use of AI applications and machine learning solutions to complement existing transaction monitoring systems (TMS) in use by financial institutions.