AML Data with Python

Photo by Marija Zaric on Unsplash Last quarter, while reviewing a batch of 80,000 MFS transactions, I noticed that our team was spending an inordinate amount of time manually filing Suspicious Transaction Reports (STRs) with the Bangladesh Financial Intelligence Unit (BFIU). The process was not only time-consuming but also prone to errors, with some reports being filed incorrectly or not at all. This experience made me realize the importance of automating the STR filing process, especially in the context of Bangladesh's rapidly growing fintech industry. The core problem most practitioners miss When I was starting out as an AML analyst, I was under the impression that automating the STR filing process would be a straightforward task. However, I soon learned that it was much more complex than I had anticipated. One of the main challenges was integrating our existing systems with the BFIU's reporting requirements, which seemed to change frequently. I was wrong about this until I ...

Photo by VD Photography on Unsplash Last quarter, while reviewing a batch of 80,000 MFS transactions for a leading BD fintech, I noticed that about 10% of the transactions exceeded the BDT 100,000 threshold, triggering alerts for potential money laundering. As I dug deeper, I realized that most of these alerts were false positives, wasting valuable time and resources for the compliance team. The core problem most practitioners miss When I was starting out as an AML analyst, I was wrong about this until I stumbled upon a crucial aspect of customer risk scoring models: they are not one-size-fits-all. In my experience, many practitioners miss this point, applying generic models that fail to account for the unique characteristics of their customer base. I recall a particularly frustrating instance where our team spent weeks fine-tuning a model, only to realize that it was not tailored to our specific use case. Background / why this matters in BD fintech context The BFIU guidelines emphasi...