AML/CFT Industry Partnership Publishes Industry Perspectives on Best Practices – Leveraging on Data Analytics and Machine Learning Methods for AML/CFT

The AML/CFT Industry Partnership, led by the Association of Banks in Singapore (“ABS“), has published the Industry Perspectives on Best Practices – Leveraging on Data Analytics and Machine Learning Methods for AML/CFT (“Paper”). The Paper is a follow-up to the 2018 Data Analytics Industry Perspectives Paper, which provided an industry overview of the data analytics solutions adopted by Financial Institutions (“FIs“). This new Paper updates the industry on the successful implementations of data analytics solutions and aims to inspire FIs into expanding their usage of such solutions. 

The Paper discusses how FIs based in Singapore have adopted data analytics to improve their control effectiveness and operational efficiency in detecting and preventing financial crime. It reviews enhancements since the publication of the first paper and discusses emerging techniques and capabilities that are being developed. The Paper shares insights and experiences of member banks, seeking to help FIs gain a broader perspective of the future directions in fighting financial crime. The Paper does not seek to set recommended minimum standards, but to provide awareness and possible directions for FIs to consider in the adoption of data analytics.

The key features of the Paper are as follows:

(a)        Current landscape. The Paper covers the current landscape of financial crime and shares insights on the core foundational pillars supporting the successful implementation of data analytics solutions. This is followed by a section on how FIs have leveraged data analytics at a macro and network level, converging into the outcomes of customer and transaction multi-level risk surveillance. The Paper then delves into the use of technology and data analytics to combat six different prioritised financial crime risk types identified for Singapore.

(b)        Looking ahead. The Paper discusses upcoming trends and the considerations for the next generation’s data infrastructure, exploring the advanced analytics deployed by member banks in different areas to stay ahead of financial crime. Finally, the Paper discusses Collaborative Sharing of Money Laundering/Terrorism Financing Information and Cases (COSMIC), a digital information-sharing platform for anti-money laundering and countering the financing of terrorism (AML/CFT) which is being developed by the Monetary Authority of Singapore.

Click on the following link for more information:

• Industry Perspectives on Best Practices – Leveraging on Data Analytics and Machine Learning Methods for AML/CFT (available on the ABS website at www.abs.org.sg)