UPI Fraud Detection Using Machine Learning

Abstract

UPI fraud has become a major challenge due to the rapid growth of digital payments. Fraudulent activities such as fake payment requests, phishing, identity theft, and unauthorized transactions can cause significant financial losses to users and banks. Therefore, detecting fraud at an early stage is important to ensure secure online transactions. This project develops a UPI Fraud Detection System using the Random Forest machine learning algorithm. The system is trained using a dataset that contains different transaction details such as transaction amount, transaction time, payment method, location, device information, and transaction history. The Random Forest algorithm is chosen because it provides high accuracy, handles large datasets effectively, and reduces the chances of incorrect predictions. The model analyses transaction patterns and classifies each transaction as either genuine or fraudulent. Its performance is evaluated using standard metrics such as accuracy, precision, recall, and F1-score. The results show that the Random Forest algorithm performs better than many traditional machine learning algorithms in detecting fraudulent transactions. The proposed system helps banks and digital payment platforms identify suspicious transactions quickly, reducing financial losses and improving user security. In the future, the system can be enhanced by integrating real-time transaction monitoring, user behaviour analysis, and advanced AI techniques to improve fraud detection accuracy and provide a safer digital payment experience