UPI Fraud Detection

Abstract

With the rapid growth of digital transactions, the Unified Payments Interface (UPI) has emerged as a popular and convenient method for financial transactions in the modern era. However, the increasing reliance on digital platforms has also led to a rise in fraudulent activities. This paper proposes a robust UPI fraud detection system employing advanced machine learning techniques to enhance the security of digital transactions. The proposed system leverages a diverse set of features, including transactional patterns, user behaviour, and device information, to create a comprehensive model for fraud detection. Machine learning algorithms, such as supervised learning classifiers and anomaly detection techniques, are employed to analyse historical transaction data and identify patterns indicative of fraudulent activities. The model is trained on a labelled dataset that includes both genuine and fraudulent transactions, ensuring its ability to distinguish between normal and suspicious behaviour