AML Data with Python

Photo by Md. Mehedi Hasan on Unsplash I still remember the night we discovered a massive structuring ring in our mobile banking system. It was a typical Monday evening when our team detected a pattern of transactions that seemed to be designed to evade our anti-money laundering (AML) checks. The total amount was staggering - over BDT 10 million in a single day, all below the BDT 100,000 threshold for monitoring. The Hidden Problem As we dug deeper, we realized that our AML rule engine had missed a critical technique used by money launderers: layering. It's a simple yet effective method where criminals break up large transactions into smaller, seemingly innocuous ones, making it difficult for our systems to detect. The problem is that standard approaches to AML often focus on individual transactions rather than the overall pattern of behavior. Our team was frustrated - we had implemented all the recommended rules and thresholds, but still, these transactions slipped through. It was...