Why Most Customer Risk Scoring Models Fail in BD Fintechs: A 8-Year Veteran's Guide
Photo by Brett Jordan on Unsplash It's 3 AM, and my phone is blowing up. Our system has flagged a massive structuring ring, with over 500 suspicious transactions in the last hour alone. The total amount? A whopping BDT 50 million. I'm talking bKash, Nagad, DBBL - all the major players are involved. This is not a drill. I've spent the last 8 years building and refining customer risk scoring models for BD fintechs. And let me tell you, it's a daunting task. The standard approaches just don't cut it here. So, what's the hidden problem? The Hidden Problem In Bangladesh, we have a unique set of challenges. For one, the BDT 100,000 MFS threshold monitoring is a major pain point. We need to flag any transaction above this amount, but the false positives are through the roof. And then there's the STR/SAR bottlenecks - our systems are overwhelmed with suspicious activity reports, and it's hard to separate the wheat from the chaff. So, how do we build a custome...