The rapid growth of the Unified Payments Interface (UPI) in India has transformed the digital payment landscape by enabling fast and user-friendly transactions.However, this rapid growth has also led to a significant rise in fraudulent activities, exposing the limitations of traditional rule-based fraud detection systems. To address this challenge, this project proposes a machine learning-based anomaly detection system designed specifically for UPI transactions. The system utilizes a stacking ensemble model that combines multiple classifiers—Support Vector Machine (SVM), Random Forest, K-Nearest Neighbors (KNN), and XGBoost—to enhance detection accuracy and reduce false positives. Feature selection is performed using the Chi-square test to isolate the most relevant transactional attributes. The solution is developed using Python in Visual Studio Code and deployed via a Flask web application with a MySQL database for data handling and real-time prediction. Experimental results demonstrate that the stacking model achieves high accuracy, outperforming individual models in terms of precision, recall, and F1-score. This project provides a scalable, real-time fraud detection framework capable of adapting to evolving financial threats in digital payment ecosystems. KEYWORDS: UPI, Fraud Detection, Machine Learning, Anomaly Detection, Stacking Ensemble Model, Chi-Square Feature Selection, Real-Time Prediction, Supervised Learning, Flask Web Application, Financial Security.
Rakesh et al. (Thu,) studied this question.