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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About Me

Monsur Habib
AML Data Analyst



## Hello, I'm Monsur Habib. Welcome to my corner of the web!

I'm an AML Data Analyst based in Dhaka, Bangladesh.


I specialize in:

- Anti-Money Laundering (AML) data analysis

- Transaction monitoring with Python

- Financial fraud detection

- KYC data validation

- Fintech compliance reporting


Tools I use: Python, Pandas, SQL, Excel, Power BI


I work with fintech companies, compliance teams, and financial institutions to build data-driven AML solutions.


📩 Want to work together?

👉 Fiverr: https://www.fiverr.com/s/gD7QZzb

👉 WhatsApp: +880 1675-115659



### Let's Connect

I am always eager to connect with fellow data enthusiasts, mentors, and industry professionals. If you'd like to discuss technology, share a project idea, or just say hello, please don't hesitate to reach out.


**[Button: Connect on LinkedIn]** **[Button: View my GitHub Profile]**

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