AML Data with Python

Photo by New Material on Unsplash I still remember the night we detected a structuring ring that had been flying under the radar for months, moving about BDT 50 million through bKash accounts in increments just below the BDT 100,000 MFS threshold. It was a wake-up call - our rule-based system had failed us. So, what went wrong? The truth is, standard approaches often fail in Bangladesh due to the unique nature of our financial landscape. The sheer volume of small transactions, the preference for mobile financial services (MFS) like bKash and Nagad, and the cat-and-mouse game with money launderers mean that static rules just can't keep up . The Hidden Problem Rules are brittle. They're based on historical patterns and are quickly outdated by new money laundering tactics. In a country where the economy is largely cash-based and informally structured, machine learning (ML) offers a way out . By analyzing patterns in transaction data, ML can identify suspicious activity tha...