UPI Fraud Detection Using Machine Learning

The project “UPI Fraud Detection Using Machine Learning” aims to provide an intelligent, real-time security layer for UPI-based digital payments by automatically identifying suspicious transactions before they are completed. An ensemble of machine learning models (including Random Forest, XG Boost, Light GBM and Gradient Boosting) is trained on a balanced fraud-non-fraud dataset, with standardized features and careful handling of class imbalance to improve recall on rare fraudulent cases while maintaining high precision. The system is deployed as a Flask-based UPI transaction portal, where users can register, log in, initiate payments, and receive instant feedback on the fraud risk for each transaction. For every payment request, the model outputs a fraud probability; high-risk transactions are automatically blocked, logged into the user’s history, and accompanied by email alerts and downloadable CSV/PDF reports for audit and analysis.