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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 Nagad Transaction Anomaly Using IsolationForest

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Photo by Mohamed Nohassi on Unsplash I still remember the night we discovered a massive structuring ring in Nagad transaction data. It was a frantic call from our compliance officer - BDT 50 million in suspicious transactions over a single weekend. Our team sprang into action, but standard approaches weren't yielding results. That's when I turned to IsolationForest for anomaly detection. The Hidden Problem In Bangladesh, our Mobile Financial Services (MFS) like bKash and Nagad have a BDT 100,000 transaction threshold for monitoring. But when you're dealing with millions of transactions daily, even a small percentage of false positives can overwhelm your team. Standard machine learning models weren't effective in capturing the nuances of our local transactions. Technical Breakdown & Logic Flow IsolationForest works by identifying data points that are farthest from the rest - essentially, it's looking for outliers . The logic flow is as follows: Collect and pr...
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