How I Improved Threshold Monitoring for BDT 100,000 MFS Transactions with Python
Photo by Sajad Nori on Unsplash Last quarter, while reviewing a batch of 80,000 MFS transactions, I noticed that approximately 10% of them exceeded the BDT 100,000 threshold, which is a critical limit for reporting suspicious transactions to the Bangladesh Financial Intelligence Unit (BFIU). This experience made me realize the importance of accurate and efficient threshold monitoring in Anti-Money Laundering (AML) compliance. The core problem most practitioners miss When I was new to AML compliance, I was wrong about this until I faced a situation where a significant number of transactions were just below the threshold, raising suspicions of structuring. In my experience, many analysts miss the fact that threshold monitoring is not just about identifying transactions above the limit but also about detecting patterns and anomalies that could indicate money laundering or terrorist financing. Background / why this matters in BD fintech context The Bangladeshi fintech sector, including mo...