FRAUD DETECTION IN UPI TRANSCATIONS
DOI: 
SHARE THIS ARTICLE
DOWNLOAD ARTICLE
REVIEW REPORT
CITE THIS ARTICLE
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.
Keywords
Article Information
Article Metrics
HOW TO CITE
Copy
J, M. (2026). Fraud Detection in UPI Transcations. International Journal of Science, Strategic Management and Technology, 02(03). https://doi.org/10.55041/ijsmt.v2i3.065
J, Mohanapriya. "Fraud Detection in UPI Transcations." International Journal of Science, Strategic Management and Technology, vol. 02, no. 03, 2026, pp. . doi:https://doi.org/10.55041/ijsmt.v2i3.065.
J, Mohanapriya. "Fraud Detection in UPI Transcations." International Journal of Science, Strategic Management and Technology 02, no. 03 (2026). https://doi.org/https://doi.org/10.55041/ijsmt.v2i3.065.
References
[2] R. Sadaf & R. Manivannan, “Enhanced Detection of Fraud in Unified Payments Interface (UPI) Transactions Using Gradient Boosting Method,” International Journal of Interpreting Enigma Engineers, 2025
[3] J. Sindhu & V.S. Swarupa, “UPI Fraud Detection Using Machine Learning Algorithms,” International Journal of Engineers Research and Science & Technology, 2024; vol. 20 (4): 57-67.
[4] “UPI Fraud Transaction Detection Using Machine Learning,” International Journal of Engineers Research and Science & Technology, 2025; vol. 21 (4): 281-285.
[5] R. Chaudhary, S. Singh, R. Singh, H. Zaidi & K. Jain, “Fraud Detection in UPI Payments Using Tabular Machine Learning Models,” International Journal for Research in Applied Science & Engineering Technology, 2025.
[6] V. Bhaskar, Abhishek, Hritik, Manjunath, and Sukanya, (2026) "UPI Fraud Detection Using Machine Learning" in International Journal of Science, Engineering and Technology.
[7] J. Kavitha, G. Indira, A. Anil Kumar, A. Shrinita, and D. Bappan, (2024) "Fraud Detection in UPI Transactions Using Machine Learning" in EPRA International Journal of Research and Development, Vol. 9, No. 4.
[8]D. Dahiphale et al. (2024) "Enhancing Trust and Safety in Digital Payments: An LLM-Powered Approach" on arXiV.
[9] S. Bhattacharyya, S. Jha, K. Tharakunnel, and J. C. Westland, (2011) "Data Mining for Credit Card Fraud: A Comparative Study" in Decision Support Systems, Vol. 50, No. 3, 602-613.
[10] F. Carcillo, A. Dal Pozzolo, Y. Le Borgne, O. Caelen, and G. Bontempi, (2017) "SCARFF: A Scalable Framework for Streaming Credit Card Fraud Detection with Spark" in IEEE Big Data Research.
Ethics and Compliance
Indexed In
Similar Articles
Previous Article
