AML Data with Python

Photo by ThisisEngineering on Unsplash I still remember the night we discovered a massive structuring ring at one of the largest mobile financial services (MFS) providers in Bangladesh. It was a BDT 100 million case, with thousands of transactions flying under the radar. Our team had to act fast, but standard approaches weren't cutting it. We needed something more... sophisticated. The Hidden Problem As AML analysts, we're no strangers to false positives and noisy data. But in Bangladesh, the problem is exacerbated by the sheer volume of transactions. With bKash, Nagad, and Rocket leading the MFS charge, we're talking millions of transactions daily. The BFIU guidelines are clear: monitor transactions above BDT 100,000 , but that's just the tip of the iceberg. Why Standard Approaches Fail Traditional rule-based systems are too simplistic for our needs. They can't keep up with the complexity of modern money laundering networks. We need to think like the criminals...