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width=\"896\" height=\"459\" alt=\"image\" src=\"https://github.com/user-attachments/assets/3e8a8080-c004-4468-92cb-1731fc1520db\" /\u003e\n\u003cimg width=\"1389\" height=\"529\" alt=\"image\" src=\"https://github.com/user-attachments/assets/003be8b2-2493-4a06-a678-05eb50e33ed8\" /\u003e\n\u003cimg width=\"603\" height=\"293\" alt=\"image\" src=\"https://github.com/user-attachments/assets/9cd9b9bd-ae5e-43bb-991d-27980efa52a4\" /\u003e\n\u003cimg width=\"189\" height=\"173\" alt=\"image\" src=\"https://github.com/user-attachments/assets/16eea3e3-f2bc-4509-8595-0ed48d8f1818\" /\u003e\n\n\n\n# 🛠 AI Fraud Detection \u0026 Transaction Monitoring  \n\n## 📌 Project Overview  \nBanks \u0026 e-commerce firms lose billions annually due to fraudulent payments.  \nThis project builds an AI-powered fraud detection system that:  \n- Detects suspicious transactions in real time.  \n- Minimizes false positives.  \n- Provides SHAP-based explanations for compliance officers.  \n\n## 📂 Repository Structure  \n- **report/** → Word/PDF project report.  \n- **notebooks/** → Exploratory analysis + model building (HTML \u0026 Jupyter).  \n- **src/** → Python scripts \u0026 FastAPI scoring service.  \n- **data/** → Sample transactions (demo only).  \n\n## ⚙️ Tech Stack  \n- Python (pandas, scikit-learn, XGBoost, SHAP, FastAPI)  \n- SQL (PostgreSQL for ingestion \u0026 cleaning)  \n- Power BI (dashboard design – planned)  \n\n## 📊 Key Results  \n- XGBoost ROC-AUC: **0.98**  \n- Recall (fraud detection rate): **95%**  \n- False Positives reduced to \u003c10% with SHAP interpretability.  \n\n## 🔮 Future Scope  \n- Deploy API on AWS Lambda/EC2.  \n- Live monitoring dashboards (Power BI/Tableau).  \n- Graph Neural Networks for fraud ring detection.  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