A Machine Learning-Based UPI Security and Fraud Detection

Abstract

Unified Payments Interface (UPI) has revolutionized digital payments in India, enabling seamless, real-time money transfers between accounts. However, its growing popularity has made it increasingly susceptible to fraudulent activities such as phishing, account takeovers, and transaction manipulation. This study introduces a hybrid fraud detection system integrating Convolutional Neural Networks (CNN) with Autoencoder, Local Outlier Factor (LOF), and K-Means Clustering to detect anomalous UPI transactions efficiently. The system utilizes anonymized UPI transaction data including transaction amount, time, device identifiers, and geolocation. Preprocessing involved encoding categorical features, normalizing numerical variables, and addressing missing data. The proposed hybrid approach was evaluated using accuracy, precision, recall, F1-score, and AUC-ROC metrics, achieving higher accuracy and fewer false positives compared to traditional methods. The findings highlight that deep learning combined with unsupervised techniques offers a robust solution for ensuring secure and reliable UPI payment operations.