Leveraging Data Analytics and Machine Learning for AML/CFT

This technical report discusses the advancements in data analytics and machine learning methods applied by financial institutions (FIs) in Singapore to combat financial crime, specifically in the areas of anti-money laundering (AML) and countering the financing of terrorism (CFT). It outlines the evolution of these practices since 2018, highlighting the increasing investments made by FIs to enhance their capabilities in detecting and preventing financial crime. The report details the foundational pillars necessary for effective implementation, including organizational structure, skillsets, and data infrastructure. It also presents various use cases of data analytics, such as multi-layer risk surveillance and specific financial crime types like sanctions evasion and trade fraud. The document aims to provide FIs with insights and benchmarks for improving their analytics capabilities, while also addressing the challenges faced in the current landscape. The report concludes with a look at future trends and considerations for advancing data analytics in the fight against financial crime.