AML Data with Python

Photo by Roger Starnes Sr on Unsplash I still remember the day our team detected a massive layering scheme in our mobile banking system. It was a typical Monday morning when our alert system started buzzing with unusual transaction patterns. The numbers were staggering - over 10,000 transactions in a single day, all below the BDT 100,000 threshold, and all of them were layered in a way that seemed almost impossible to detect. The Hidden Problem As I dug deeper, I realized that our AML rule engine was missing a critical aspect of layering detection. The engine was designed to catch obvious structuring attempts, but it was not sophisticated enough to identify complex layering schemes. This was a major concern, as layering is a common technique used by money launderers to evade detection. According to the BFIU guidelines, layering is defined as the process of moving funds through multiple transactions to disguise the origin of the money. In mobile banking, layering can be particularly ch...