AI-Powered Financial Fraud Detection & Risk Intelligence System

Financial fraud has become one of the most critical challenges for banks, payment gateways, and digital financial platforms. As transaction volumes continue to grow, traditional rule-based systems struggle to detect evolving fraud patterns while keeping false alerts under control.

This project presents an end-to-end Explainable Machine Learning pipeline for detecting fraudulent financial transactions using XGBoost, Hyperparameter Tuning, Threshold Optimization, and SHAP Explainability.

Instead of focusing only on prediction accuracy, the project emphasizes building a practical fraud detection solution that balances predictive performance, business impact, and model interpretability.