AI-Powered Real-Time Transaction Fraud Detection System
Identifying fraudulent financial transactions in real-time with 94.35% accuracy using ensemble ML.
The Friction
Fintech companies process millions of transactions daily, making them primary targets for complex fraud schemes. Legacy rules-based systems yield high false-positive rates, which block legitimate users, and fail to adapt to new fraud patterns, causing severe revenue leakage and user friction.
The Neural Architecture
We architected and deployed a containerized real-time Fraud Detection System. Built on FastAPI and dockerized for enterprise scale, the system runs an advanced machine learning ensemble model (Random Forest, Gradient Boosting, and Neural Networks) engineering 48+ features per transaction. It evaluates and scores transaction risk on a scale of 0-100 in under 250ms, triggering automated alerts with reasoning for critical risk events.
Tech Stack Deployed
Impact Report
- Achieved a verified 94.35% prediction accuracy with an AUC-ROC score of 92.34%.
- Reduced transaction scoring latency to under 0.25 seconds, enabling in-flight authorization blocks.
- Designed a secure REST API with comprehensive logging, metrics tracking, and SQLite database storage.
- Constructed an interactive executive analytics dashboard visualizing real-time threat maps and alert triggers.