UPI Fraud Detection Using Ensemble Approach

Abstract

Unified Payment Interface (UPI) has paved the way for convenient digital transaction and has enhanced the speed of digital transaction. Users can simply use the technology to make online payments at an accurate speed through mobile number, Virtual Payment Address (VPA) or QR code. But with the evolving technologies and advanced system, there has been cases where fraudulent activities are encountered leading to financial risk and losses. In order to tackle this problem, this research develops an ensemble RNN and XGBoost system for detecting UPI fraud by examining the patterns to recognize and alert on potentially fraudulent activities. It also encompasses data cleaning and preparation, and training of predictive models. Dataset from open source has been trained and tested against the Ensemble RNNBoost (Ensemble RNN and XGBoost) model and has been compared with various ML models. The model’s performance was assessed using various evaluation metrics, including F1-score, precision, recall, and accuracy. The main reason to combine XGBoost and RNN is to exploit sequential patterns and tabular feature interactions simultaneously, leading to overall performance and more robust predictions. As a result, RNNBoost model produce the maximum accuracy rate of 96.44% in comparison with other traditional models like DT (95.00%), RF (95.88%), LR (80.25%), KNN (83.00%), SVM (83.00%), NB (83.00%) and RNN (81.25%).