DETECTING FRAUD IN UPI TRANSACTIONS: A STUDY USING RANDOM FOREST AND XGBOOST

The widespread adoption of the Unified Payments Interface (UPI) has simplified digital transactions, enabling fast and easy payments. However, with its increasing usage comes a surge in fraudulent activities. This project presents a cutting-edge method for detecting UPI fraud by leveraging machine learning models and analyzing user behavior patterns. Utilizing a rich dataset that combines transaction records, behavioral data, and prior fraud cases, we develop a highly effective fraud detection system. The model uses techniques such as anomaly detection and pattern recognition to bolster the security of UPI transactions. Our experimental findings show a substantial enhancement in detecting fraudulent activities when compared to traditional approaches, underscoring the system’s potential to mitigate financial losses and build trust in digital payment systems.