The widespread use of the Unified Payments Interface (UPI) has made digital payments faster and more accessible, while also increasing the need to examine reported fraud in a structured way. Much of the existing UPI fraud literature studies machine-learning or deep-learning techniques that classify or predict fraudulent transactions. This paper describes the UPI Fraud Intelligence Analytics Dashboard, a dataset-driven platform for analysing records that have already been labelled as fraudulent. The dashboard analyses records by state, bank, fraud type, transaction amount, and time. It also supports individual transaction checks, automatic statistical findings, and analysis of compatible uploaded files. The prototype uses Flask and Pandas on the backend and HTML, JavaScript, Bootstrap, and Chart.js on the frontend. Its findings are calculated from the loaded records; it does not generate a fraud probability or claim to identify fraud independently. The paper outlines the platform design, processing workflow, analytical features, and a functional evaluation using the supplied dataset.

Sayyed Alaa Mariya Irfan, Dr. Manoj Singh, "UPI Fraud Intelligence & Analytics Dashboard" International Journal of Scientific Research and Engineering Development, V9(5): Page(1298-1302) September - October 2026. ISSN: 2581-7175. www.ijsred.com. Published by Scientific and Academic Research Publishing.