Why Most Feature‑Engineering Tricks Miss the Real Bangladeshi Money‑Flow Signals
AI-generated illustration Bangladesh, 02:45 am. My phone buzzes. A senior manager at bKash shouts, “We just got a 12‑minute spike: 1,200 transactions, BDT 100 k each, all from rural agents in Sylhet!” My heart jumps. The alert system is choking on false positives, the audit team is breathing down my neck, and the regulator is about to ask for the next STR. The Hidden Problem: Why the Same Old Features Fail Here We all start with the textbook features: transaction amount, velocity, time‑of‑day, and a simple count of distinct counterparties. In Dhaka they work okay for classic structuring, but in the field they miss a whole class of behavior unique to Bangladeshi MFS. Two things make the difference: Agent‑banking cascades: A single agent can route money for dozens of micro‑merchants, each with their own pattern. Cash‑in‑cash‑out loops: Rural users often move cash from a mobile wallet to a local shop, then back via a different agent, staying under the BFIU BDT 100 k threshold. When I fi...