Identifying High-Risk Transactions on Grab: A Data- Driven Approach to Anti-Money Laundering (AML)

Grab’s existing process to mitigate & detect ML transactions is labour intensive. The team was tasked to develop an automated solution that minimizes manual involvement and enhances effectiveness of anomaly detection. They adopted a data-driven approach to identify transactions with the highest risk of ML activity.