Money launderers use complicated schemes to wash dirty money, and financial institutes need to fight back with advanced techniques. Using graph analytics, it becomes feasible to connect dots in very complicated schemes that traditional methods cannot handle. Nowadays, money laundering involves leveraging different types of financial instruments with more complicated schemes. Preventing money laundering and terrorist financing has become high priority for financial institutions.
Traditional methods of monitoring for AML typically involve static, rule-based alerts built from previous experience. The biggest challenge faced by traditional approach is that money laundering schemes continuously changing in a way that is difficult to detect. With the power of graph analytics, it becomes feasible to connect dots in complicated schemes that traditional methods cannot handle. In this talk, we will share our experience using advanced rules traversing hidden patterns in the data, creating graph-based features for machine learning and finding similar patterns using graph embeddings.
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