Real-Time Fraud Detection Engine for Fintech

A fintech platform processing thousands of daily transactions needed a way to detect fraudulent activity in real time. Their rule-based system was catching less than 40% of fraud cases and generating too many false positives.

I built a machine learning-powered fraud detection engine that analyzes every transaction in real time and flags suspicious activity before it completes.

Every transaction hits the FastAPI endpoint

The system extracts 50+ features (amount patterns, device fingerprints, geolocation, velocity checks)

A trained scikit-learn ensemble model scores the transaction in under 100ms

High-risk transactions are held for review; medium-risk get additional verification

Results feed back into the model for continuous improvement

Real-time scoring under 100ms per transaction

Ensemble model combining Random Forest, Gradient Boosting, and anomaly detection

Adaptive thresholds that adjust based on transaction volume and patterns

React dashboard for the fraud team to review flagged transactions

PostgreSQL-backed audit trail for compliance

ML: scikit-learn, Python

Backend: FastAPI

Database: PostgreSQL

Frontend: React

Infrastructure: Containerized deployment with auto-scaling

Fraud detection rate improved from 40% to 98%

False positive rate reduced by 75%

Processing latency under 100ms per transaction

Saved the platform an estimated $2M+ annually in fraud losses