🛠 AI Fraud Detection & Transaction Monitoring

📌 Project Overview

Banks & e-commerce firms lose billions annually due to fraudulent payments.
This project builds an AI-powered fraud detection system that:

• Detects suspicious transactions in real time.
• Minimizes false positives.
• Provides SHAP-based explanations for compliance officers.

📂 Repository Structure

• report/ → Word/PDF project report.
• notebooks/ → Exploratory analysis + model building (HTML & Jupyter).
• src/ → Python scripts & FastAPI scoring service.
• data/ → Sample transactions (demo only).

⚙️ Tech Stack

• Python (pandas, scikit-learn, XGBoost, SHAP, FastAPI)
• SQL (PostgreSQL for ingestion & cleaning)
• Power BI (dashboard design – planned)

📊 Key Results

• XGBoost ROC-AUC: 0.98
• Recall (fraud detection rate): 95%
• False Positives reduced to <10% with SHAP interpretability.

🔮 Future Scope

• Deploy API on AWS Lambda/EC2.
• Live monitoring dashboards (Power BI/Tableau).
• Graph Neural Networks for fraud ring detection.