AML Data with Python

Photo by Mohamed Nohassi on Unsplash I still remember the night we discovered a massive structuring ring in Nagad transaction data. It was a frantic call from our compliance officer - BDT 50 million in suspicious transactions over a single weekend. Our team sprang into action, but standard approaches weren't yielding results. That's when I turned to IsolationForest for anomaly detection. The Hidden Problem In Bangladesh, our Mobile Financial Services (MFS) like bKash and Nagad have a BDT 100,000 transaction threshold for monitoring. But when you're dealing with millions of transactions daily, even a small percentage of false positives can overwhelm your team. Standard machine learning models weren't effective in capturing the nuances of our local transactions. Technical Breakdown & Logic Flow IsolationForest works by identifying data points that are farthest from the rest - essentially, it's looking for outliers . The logic flow is as follows: Collect and pr...