Fraud  Detection in UPI Transcations

J, Mohanapriya

doi:https://doi.org/10.55041/ijsmt.v2i3.065

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FRAUD DETECTION IN UPI TRANSCATIONS

AUTHORS:

Mohanapriya J

Mentor

Dr.B.Leelavathi

Affiliation

Department Of computer Technology, Dr.N.G.P. Arts and Science College, Coimbatore, Tamil Nadu, India

DOI: 10.55041/ijsmt.v2i3.065

CC BY 4.0 License:

This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Abstract

The digital payment landscape in India has undergone a paradigm shift with the advent and widespread adoption of the Unified Payments Interface (UPI). While UPI has democratized access to cashless financial services, its exponential growth has been accompanied by a surge in sophisticated fraudulent activities, ranging from high-value transaction manipulation to unauthorized device usage. Traditional rule-based detection mechanisms often fail to adapt to these evolving threat vectors, resulting in high rates of false negatives. This paper proposes a robust machine learning framework leveraging the XGBoost classification algorithm to detect fraudulent UPI transactions in real time. The system integrates diverse attributes, including transaction velocity, device novelty, and merchant risk profiles, and utilizes Scikit-learn’s Standard Scaler for feature normalization. Deployed via a Flask-based web application, the model provides instantaneous fraud probability scores. Theoretical analysis and methodological design suggest that this approach offers a scalable, high-confidence solution for securing the modern digital payment ecosystem against dynamic financial threats.

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This article has undergone plagiarism screening and double-blind peer review. Editorial policies have been followed. Authors retain copyright under CC BY-NC 4.0 license. The research complies with ethical standards and institutional guidelines.

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