AML Data with Python

Photo by Joshua Bowers on Unsplash Last quarter, while reviewing a batch of 80,000 MFS transactions for a major fintech company in Bangladesh, I noticed that over 20% of the transactions had missing or incorrect customer information. This was a major concern, as accurate customer data is essential for effective Anti-Money Laundering (AML) analysis. When I was starting out as an AML analyst, I thought that cleaning transaction data was a simple matter of removing any obvious errors or inconsistencies. But I was wrong about this until I encountered a particularly tricky case involving a series of transactions that had been flagged as suspicious, only to discover that the issue was not with the transactions themselves, but with the poor quality of the data. The core problem most practitioners miss In my experience, many AML analysts miss the importance of thoroughly cleaning their transaction data before analysis. They may assume that the data is accurate, or that any errors will be caug...