Mr. Chua Kim Leng, AMD, MAS also talked about using techniques like machine learning for identifying unusual patterns of transactions.
In a speech delivered at the Association of Banks in Singapore (ABS) Financial Crime Seminar on July 2017, Mr. Chua Kim Leng, Assistant Managing Director, Monetary Authority of Singapore (MAS) talked about how banks can work smarter on the AML/CFT (anti-money laundering / countering the financing of terrorism) front.
The subject of the speech was to strengthen the financial system’s resilience to financial crimes, focusing on money laundering and terrorism financing risks or ML/TF risks. Methods of detecting and preventing the abuse of the financial system cannot remain static, as criminals are constantly finding more creative ways to perpetrate crimes.
Mr. Leng highlighted onboarding and transaction monitoring as two areas, where the financial system could benefit from better use of technology.
During the process of onboarding or adding a new client to the bank’s systems, banks are supposed to subject the new customer to KYC (Know your customer) procedures. This is a critical step to counter ML/ FT. For instance, shell companies with no apparent economic purpose are often used for such activities. This would be detectable through KYC procedures.
Mr. Leng said that a number of banks in Singapore have come together to build a joint utility for KYC processes.
He said, “Robust KYC processes are the front line of our defences, and they are by nature resource-intensive. MAS is working closely with these banks on the project and I am excited about its potential.”
This utility can be a platform for raising the benchmark for KYC processes across participating banks. It can help strengthen the adoption of best practices for screening and on-boarding.
It can also free up resources and allow banks to focus on the more complex aspects of customer due diligence and on-going monitoring, including monitoring and investigating unusual and suspicious transactions. If well designed and well executed, the utility could also potentially offer efficiencies of scale and reduce the need for customers to provide the same information to multiple institutions.
Mr. Leng said that there is room for improvement in the area of transactions monitoring through the use of techniques such as machine learning.
Current systems usually flag out transactions based on a set of pre-defined rules, thresholds and scenarios. Though these rules are calibrated periodically, there continues to be a high rate of false positives. Extensive human effort is required to review these alerts.
Instead of adding more people for monitoring, which is not a long-term sustainable solution, using next generation surveillance systems, which utilise sophisticated techniques, such as machine learning, can help identify unusual patterns of transactions across a network of entities and across time. These systems could succeed in picking out suspicious activities that are impossible for a human to detect today. (Also, such a system would improve over time, the more data it processes, unlike the traditional systems with a set of rules.)
“Understanding how these complex and sometimes proprietary algorithms work is a challenge. Our responsibility, as professionals in this field, is to learn to “unpack the black box”, before we base our decisions on them. In this regard, I am glad that a number of financial institutions have started pilot programmes with data analytics providers for AML/CFT purposes,” Mr. Leng said.
Read the transcript of the speech here.
Featured image: MAS building (on left)/ Credit: Terence Ong/ CC BY-SA 3.0
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