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)

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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AML Resources

AML Resources — Free Tools & Templates

A curated collection of free resources for AML analysts and fintech compliance professionals in Bangladesh and South Asia.

📊 Excel Templates

  • Transaction Monitoring Log Template
  • KYC Customer Risk Scoring Sheet
  • SAR/STR Filing Checklist
  • Daily Cash Transaction Report (CTR) Template

🐍 Python Scripts (Coming Soon)

  • Anomaly Detection in Transaction Data
  • Customer Risk Scoring with Pandas
  • Automated STR Report Generator

📚 Recommended Reading

  • FATF 40 Recommendations (Official)
  • Bangladesh Bank AML Circular
  • BFIU Guidelines for Reporting Entities

🔗 Useful Links

  • Bangladesh Financial Intelligence Unit (BFIU): bfiu.org.bd
  • FATF Official Site: fatf-gafi.org
  • Asia/Pacific Group on Money Laundering: apgml.org

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