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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 in AML: Why Machine Learning Beats Rules in Bangladesh

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Photo by New Material on Unsplash I still remember the night we detected a structuring ring that had been flying under the radar for months, moving about BDT 50 million through bKash accounts in increments just below the BDT 100,000 MFS threshold. It was a wake-up call - our rule-based system had failed us. So, what went wrong? The truth is, standard approaches often fail in Bangladesh due to the unique nature of our financial landscape. The sheer volume of small transactions, the preference for mobile financial services (MFS) like bKash and Nagad, and the cat-and-mouse game with money launderers mean that static rules just can't keep up . The Hidden Problem Rules are brittle. They're based on historical patterns and are quickly outdated by new money laundering tactics. In a country where the economy is largely cash-based and informally structured, machine learning (ML) offers a way out . By analyzing patterns in transaction data, ML can identify suspicious activity tha...
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