- Tuvya
- Dhroova
- Ammar
- Swarnim
Built as part of a collaborative hackathon project.
FraudSense is a real-time fraud detection system designed to analyze transactions before completion and prevent high-risk activities using behavioral intelligence, machine learning, and rule-based logic.
🏆 HackUp 2026: Selected in the Top 45 teams out of 200 teams.
- ✅ Backend fully functional and tested
⚠️ Frontend under reconstruction (v2.0 UI in progress)- ✅ API accessible via
/docs
This version focuses on a complete redesign of the fraud detection engine with improved accuracy, stability, and system design.
- Amount deviation (z-score based)
- Transaction velocity tracking
- Time-based anomaly detection
- Category-based behavior analysis
- Random Forest model
- Feature-aligned input pipeline
- Integrated into real-time scoring
Final Score = 0.65 × ML Score + 0.35 × Rule Score
- APPROVE (low risk)
- MFA_HOLD (medium risk)
- BLOCK (high risk)
- Critical fraud patterns trigger forced BLOCK
- Velocity + anomaly triggers MFA
- Shared device detection
- Merchant spike analysis
- Fraud pattern linking
- Backend: FastAPI (Python)
- Frontend: React + Vite (Rebuilding UI v2.0)
- ML Engine: Scikit-learn (Random Forest)
- Database: SQLite
backend/ frontend/ models/ main.py requirements.txt README.md
pip install -r requirements.txt uvicorn main:app --reload --port 8000
cd frontend npm install npm run dev
Evaluates a transaction before execution.
Access interactive API via:
http://127.0.0.1:8000/docs
- Behavior-based fraud detection
- Hybrid ML + rule scoring
- Real-time decision engine
- System-level architecture focus
Contributions are welcome 🚀
- Improve detection logic
- Enhance frontend UI
- Fix bugs
- Improve documentation
© 2026 FraudSense Team