How I Uncovered the Dark Side of AML Models: The Urgent Need for Explainability
Photo by Juno Jo on Unsplash I still remember the day our AML system flagged over 10,000 transactions as suspicious, only to find out that 90% of them were false positives. The regulator was breathing down our necks, and our team was under immense pressure to explain each and every one of those flags. That's when I realized the harsh truth: our AML models, despite being highly accurate on paper, were black boxes that nobody could interpret. The Hidden Problem In Bangladesh, where the BFIU guidelines are strict and the MFS threshold monitoring is a challenge, AML models need to be more than just accurate. They need to be explainable. But standard approaches often fall short. I've seen it time and time again: a model is trained on a dataset, it performs well on the test set, and then it's deployed. But when it comes to explaining why a particular transaction was flagged, the model falls silent. Technical Breakdown & Logic Flow To tackle this problem, I decided to take ...