Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{179808,
        author = {M. SABARI RAMACHANDRAN and P. KISHORE KUMAR},
        title = {FEDERATED TRANSFORMER DISTILLATION FOR SCALABLE FINANCIAL FRAUD DETECTION},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {8030-8034},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=179808},
        abstract = {The proposed UPI Fraud Detection system is 
designed to strengthen the safety and trustworthiness of 
digital financial transactions by utilizing advanced 
technologies such as machine learning (ML), artificial 
intelligence (AI), and data analytics. By examining 
transaction behaviours and spotting irregularities, the 
system can identify and prevent a variety of fraudulent 
activities, 
including phishing attacks, identity 
impersonation, and unauthorized access. It features a 
real-time surveillance mechanism to swiftly detect 
suspicious transactions and generate alerts for 
immediate intervention.  
As UPI-based payments 
continue to grow rapidly, there is a pressing demand for 
effective fraud prevention strategies. This research 
introduces a scalable solution capable of analysing large 
datasets, detecting fraudulent patterns, and learning 
from new data to enhance its predictive accuracy. 
Techniques such as Random Forest, Support Vector 
Machine (SVM), and Neural Networks will be used to 
distinguish between legitimate and fraudulent 
transactions. Real-time detection not only reduces 
monetary losses but also boosts user confidence in 
digital transactions, thereby contributing to a more 
secure UPI framework.},
        keywords = {},
        month = {May},
        }