Overview: UPI & Cards Payment Fraud Detection System This project is a standalone, AI-powered fraud detection system specifically designed for UPI (Unified Payments Interface) and card-based payment transactions. It leverages high-performance machine learning and a rule-based validation engine to identify potential fraudulent activity in real-time. Key Features : Real-Time Standalone Inference: Runs entirely within the browser using Pyodide (WebAssembly). This eliminates the need for a backend API for predictions, ensuring low latency and maximum privacy. Dual-Model Ensemble: Uses a weighted ensemble of XGBoost and LightGBM models, achieving a high degree of accuracy by combining the strengths of different gradient boosting architectures. UPI Validation Engine: Includes a specialized rule engine to catch structurally invalid Virtual Payment Addresses (VPAs) and Unique Transaction Reference (UTR) numbers specific to apps like GPay, PhonePe, and Paytm. Privacy-First History: Automatically saves transaction history to a local SQLite3 database (sql.js) stored within the browser's IndexedDB. No sensitive transaction data ever leaves your device. Advanced Feature Engineering: Automatically transforms raw transaction data into 447 engineered features, capturing complex temporal and statistical patterns found in real-world fraud. Comprehensive Metrics: Provides instant feedback with fraud probability scores (0–100%), risk levels (Low, Medium, High), and detailed performance insights. Technical Specifications: Model Performance: 98.45% Accuracy and 0.965 ROC-AUC, trained on over 590,000 real-world transactions (IEEE-CIS dataset). Frontend Stack: Built with React.js, Vite, and Tailwind CSS for a modern, responsive, and "Cyber-Fintech" user experience. Machine Learning: XGBoost (Binary Logistic) + LightGBM (GBDT) with weighted ensemble logic. Deployment: Fully client-side; can be served as a static site while maintaining full AI capabilities.