AML Data with Python

Photo by Mohamed Nohassi on Unsplash Last quarter, while reviewing a batch of 80,000 MFS transactions from Nagad, I noticed that about 2% of the transactions exceeded the BDT 100,000 threshold. When I was analyzing these high-value transactions, I realized that most of them were legitimate business payments. However, a few transactions seemed suspicious, with no clear pattern or connection to known customers. The Core Problem In my experience, most AML analysts focus on detecting known patterns of money laundering, such as structuring or smurfing. But what about unknown patterns? That's where anomaly detection comes in. I've found that many practitioners miss the importance of identifying unusual transactions that don't fit any known pattern. Short and sweet: anomaly detection is key. It helps us identify transactions that are unlikely to be legitimate, even if they don't match any known money laundering pattern. Technical Breakdown To detect anomalies in Nagad transac...