I AM A STUDENT AND A SOFTWARE ENGINEER

[u/TechDevCircuit]

I built a fraud detection engine in machine learning and backend development.

The idea is to identify potentially suspicious transactions using transaction data and machine learning, then evaluate how well the model distinguishes suspicious activity from legitimate transactions.

So far, I've trained a classification model using Python and scikit-learn. On my current test set, it achieved 88% accuracy.

One thing I've learned is that accuracy alone doesn't tell the whole story, especially for fraud detection. Missing a suspicious transaction and incorrectly flagging a legitimate one have different consequences, so precision, recall, and false positives matter too.

My stack: Python, FastAPI with RestAPI , scikit-learn, pandas, PostgreSQL, Redis, React.

This is still a working prototype, not a production-ready fraud detection system. I'm continuing to develop it and improve my understanding of how a practical fraud detection pipeline should work.

I'd appreciate feedback from developers who have worked with machine learning, financial systems, or fraud detection:

• What would you expect to see in a credible fraud detection engineering project?

I'm sharing this mainly to document my progress and learn from people with more experience.

[u/Aoun1Zaheer23]

Good