Authors : Dr. R. Nagarajan; S. Jayasurya; S. Kavin balaji

Volume/Issue : Volume 11 - 2026, Issue 3 - March

Google Scholar : https://tinyurl.com/mryd27be

Scribd : https://tinyurl.com/33e3dfmu

DOI : https://doi.org/10.38124/ijisrt/26mar371

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : The rapid growth of digital payment technologies has transformed financial transactions by enabling instant, convenient, and secure money transfers. In India, the Unified Payments Interface (UPI) has emerged as one of the most widely used real-time payment systems for both personal and business transactions. However, the increasing popularity of UPI has also led to a rise in fraudulent activities such as fake UPI IDs, unauthorized transactions, OTP and PIN theft, and social engineering attacks. Traditional fraud detection methods based on fixed rules often struggle to identify complex and evolving fraud patterns. This study proposes an Advanced UPI Transaction Fraud Detection System that combines rulebased validation with machine learning techniques to detect suspicious activities. The system analyzes multiple transaction features including payment amount, transaction frequency, login failures, device changes, and location variations. Implemented using Python and Streamlit, the system enables real-time transaction monitoring. Experimental results demonstrate that the model effectively identifies fraudulent behavior while reducing false alarms, improving the security and reliability of digital payment systems.

Keywords : UPI Fraud Detection, Machine Learning, Random Forest Algorithm, Digital Payment Security, Transaction Monitoring, OTP/PIN Attack Detection, Anomaly Detection, Cyber Fraud Prevention.

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