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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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8 Years of AML in Bangladesh: Cracking the Code on FATF vs BFIU Gaps

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I still remember the day our team detected a massive structuring ring in a local mobile financial service (MFS) provider, with transactions totaling over BDT 10 million in a single week. What was more alarming was how this had slipped through our standard monitoring systems, highlighting the critical gaps between FATF recommendations and Bangladesh's BFIU guidelines. The Hidden Problem As an AML compliance analyst, I've found that standard approaches often fail in Bangladesh due to the unique nature of our financial landscape. The BDT 100,000 MFS threshold monitoring, for instance, can be easily circumvented by structuring transactions just below this limit. Moreover, the sheer volume of transactions in platforms like bKash and Nagad makes manual monitoring nearly impossible. Technical Breakdown & Logic Flow To tackle this, our team developed a more nuanced system. First, we collected and preprocessed transaction data, focusing on patterns that might indicate structuring o...
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