AML Data with Python

AI-generated illustration High‑Stakes Hook It was 02:17 am on a humid Thursday in Dhaka. My phone buzzed—another alert from the BFIU’s real‑time watchlist. The flag read: Potential Trade‑Based Money Laundering (TBML) – BDT 12,000,000 . The transaction? A single export invoice from Chattogram to a shell company in the Maldives, declared value BDT 950 million, but the shipping manifest showed only 120 tons of jute. My heart raced. The senior manager on the call asked, “What did we miss?” I had to answer before the compliance window closed at midnight. The Hidden Problem Most of us in Bangladesh lean on the BFIU’s standard checklist: verify HS codes, match invoice amounts, cross‑check counterparties. It works for simple over‑/under‑invoicing, but TBML is a chameleon. Criminals layer trade documents, split shipments, and use “price‑adjustment” clauses that slip past a static rule set. In my first year I trusted a spreadsheet that only looked at price per unit . It never caught the Maldive...