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I Built a BFIU-Compliant AML Detection System in Python (Here's Why the Kaggle Approach Doesn't Work

I Built a BFIU-Compliant AML Detection System in Python (Here's Why the Kaggle Approach Doesn't Work)

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Most AML tutorials end with a confusion matrix and a 99% accuracy score. Here's why that doesn't work — and what I built instead. I've been working in fintech compliance data for a while. The one thing I kept noticing: every "fraud detection project" on GitHub or Kaggle uses the same dataset — the UCI credit card fraud dataset from 2013. It has 284,000 rows, 30 features labeled V1-V28, and approximately zero explanatory value for anyone who wants to understand how financial crime actually works. So I built something different. The problem with the standard approach Real transaction monitoring engines don't work like Kaggle competitions. They don't take a CSV, train a model, and output a probability score. They work like this: A rule engine runs first — deterministic, auditable, regulatory-cited rules that generate alerts Those alerts get scored and triaged by risk tier An ML layer reduces false positives among the high-risk alerts ...
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How I Caught a Massive Money Laundering Ring in Bangladesh Using Graph Analysis

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Photo by Sasun Bughdaryan on Unsplash Sitting at my desk, sipping coffee, I stared at the screen in front of me - 10,000 suspicious transactions in one day, all passing the BDT 100,000 threshold. I was working as an AML compliance analyst at a leading fintech in Bangladesh, and this was not what I wanted to see on a Monday morning. The transactions were spread across various mobile financial services (MFS) like bKash, Nagad, and Rocket. We had to report these to the Bangladesh Financial Intelligence Unit (BFIU) as soon as possible. But how do we connect the dots and figure out if these are part of a larger money laundering network? The Hidden Problem Standard approaches to detecting money laundering often rely on simple threshold monitoring and rule-based systems. But these can generate a huge number of false positives, overwhelming the compliance team. In Bangladesh, the MFS threshold monitoring is a critical component of AML regulations, but it's not enough on its own. That'...
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