AML Data with Python

AI-generated illustration High‑Stakes Hook It was 02:17 am on a humid Tuesday. My phone buzzed – the BFIU had just emailed a provisional SAR on a single transaction that moved BDT 3,120,000 from a Dhaka‑based corporate account to a shell company in the UAE. The red‑flag came from our sanctions‑screening engine, but the alert had been dismissed two weeks earlier as a false positive. The regulator was now demanding a full audit trail, and my team was scrambling to prove we hadn’t missed a sanctioned party. The Hidden Problem Most banks in Bangladesh treat sanctions screening like a checkbox. They load a static OFAC list, run a fuzzy match, and hope the result is clean. In reality the list changes daily, local counterparties have multiple spellings, and the BFIU expects a documented risk‑based approach. The mistake that cost us the $3 million alert was not the list itself – it was how we integrated it. Technical Breakdown & Logic Flow We broke the process into four logical layers: Dat...