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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 Chasing Shadows: How I Uncovered the Hidden Patterns of Agent Banking Fraud in Bangladesh

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Photo by Anas on Unsplash I still remember the night we detected a massive structuring ring that had been flying under the radar for months. It was a $1 million transaction, split into tiny chunks of BDT 100,000, all going through different agents across the country. My team and I were ecstatic, but also terrified - how did this happen under our noses? The Hidden Problem As I dug deeper, I realized that standard AML approaches just weren't cutting it in Bangladesh's unique DFS ecosystem. The BFIU guidelines are clear: monitor transactions above the BDT 100,000 MFS threshold, but the reality is that most fraud happens in smaller, more frequent transactions. It's like looking for a needle in a haystack, but the haystack is on fire. Why Standard Approaches Fail First , most systems rely on simple rule-based models that can't keep up with the sophistication of modern fraudsters. Second , the sheer volume of transactions in Bangladesh's MFS space makes it impossible to ...
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