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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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The Hidden Problem in Cleaning Dirty Transaction Data for AML Analysis in Python

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Photo by Joshua Bowers on Unsplash Last quarter, while reviewing a batch of 80,000 MFS transactions for a major fintech company in Bangladesh, I noticed that over 20% of the transactions had missing or incorrect customer information. This was a major concern, as accurate customer data is essential for effective Anti-Money Laundering (AML) analysis. When I was starting out as an AML analyst, I thought that cleaning transaction data was a simple matter of removing any obvious errors or inconsistencies. But I was wrong about this until I encountered a particularly tricky case involving a series of transactions that had been flagged as suspicious, only to discover that the issue was not with the transactions themselves, but with the poor quality of the data. The core problem most practitioners miss In my experience, many AML analysts miss the importance of thoroughly cleaning their transaction data before analysis. They may assume that the data is accurate, or that any errors will be caug...
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