An Intelligent Machine Learning-Based Frame-work for Secure UPI Fraud Detection Using En-semble Classification Models
Authors-S.Venkateswara Rao, Mullamuri yamini
Keyword-UPI Fraud Detection, Machine Learning, Ensemble Learning, XGBoost, Voting Classifier, Stacking Classifier, Digital Payments, Financial Fraud Detection, Flask, SQLite Authentication.
The rapid adoption of the Unified Payments Interface (UPI) has transformed digital payment services by enabling instant, convenient, and cashless financial transactions. However, the wide-spread usage of UPI has simultaneously increased the occurrence of fraudulent activities, makingThe proposed framework utilizes Logistic for transaction classification. Furthermore, Voting and Stacking ensemble classifiers are employed to enhance prediction performance by combining the strengths of multiple learning models. The dataset undergoes extensive prepro-cessing, including missing value treatment, feature selection, normalization, and exploratory data analysis to improve learning efficiency. Aenables secure real-time fraud prediction for authorized users. Experimental evaluation demonstrates that ensemble learning significantly improves fraud detection performance, with the Stacking Classifier achieving the highest prediction accuracy while maintaining the same implementation strategy and evaluation methodology as the original study. The proposed framework provides a scalable, intelligent, and reliable solution for protect-ing UPI transactions against fraudulent financial activities while strengthening
Doi-[https://doi.org/10.5281/zenodo.21735199]