UPI FRUAD DETECTION USING MACHINE LEARNING

Abstract: With the rapid growth of digital payment systems in India, the Unified Payments Interface (UPI) has become one of the most widely used platforms for real-time financial transactions. While UPI offers convenience, speed, and accessibility, it has also become a target for fraudulent activities such as unauthorized transactions, phishing attacks, identity theft, and account takeovers. These increasing fraud incidents highlight the need for an intelligent and automated fraud detection system to ensure secure digital transactions. This project presents a UPI Fraud Detection System using Machine Learning techniques to identify and prevent fraudulent transactions in real time. The proposed system analyzes historical transaction data and user behavior patterns to distinguish between legitimate and fraudulent activities. Key features such as transaction amount, transaction frequency, time of transaction, location variance, device usage, and transaction velocity are extracted and processed for model training. Machine learning algorithms such as Logistic Regression, Decision Tree, Random Forest, and Support Vector Machine (SVM) are applied to classify transactions as either genuine or fraudulent.

Keywords: UPI FRUAD , Machine learning , historical transaction,random forest, fraudulent