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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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Why Most AML Models Fail in Bangladesh: 5 Steps to Improve Detection

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Photo by Mauro Sbicego on Unsplash Last quarter, while reviewing a batch of 80,000 MFS transactions, I noticed that our rule-based AML system had flagged over 10,000 transactions as suspicious, but upon manual review, only about 100 actually required reporting to the BFIU. This experience made me realize that traditional rule-based systems can be overly broad and inefficient, leading to unnecessary false positives. In my experience, many AML analysts and compliance officers in Bangladesh face similar challenges. We spend too much time reviewing false positives, which takes away from our ability to focus on high-risk transactions. I was determined to find a better approach. The core problem most practitioners miss Most AML systems rely on predefined rules to identify suspicious transactions. However, these rules often fail to account for complex patterns and anomalies in real-world data. This leads to a high number of false positives, which can overwhelm compliance teams. I noticed tha...
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