AML Data with Python

Photo by Orgalux on Unsplash I still remember the day our system flagged a massive structuring ring in bKash transactions - 10,000 suspicious transactions in a single day, with a total value of BDT 50 million. My team and I were on high alert, trying to make sense of the data and prevent a potential money laundering disaster. The Hidden Problem Standard approaches to detecting structuring patterns often fail in Bangladesh due to the unique characteristics of our mobile financial services (MFS) market. With bKash, Nagad, and Rocket dominating the scene, we have to deal with a huge volume of small transactions, making it difficult to identify legitimate activity from illicit one. BFIU guidelines require us to monitor transactions above BDT 100,000, but structuring involves breaking down large amounts into smaller, less suspicious transactions. Technical Breakdown & Logic Flow To tackle this problem, we need to analyze transaction patterns, looking for anomalies in user behavior. Ou...