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As more people use UPI for digital payments, stopping fraud has become very important. This project creates a tool that uses Machine Learning to spot fake UPI transactions.
The system is trained on a past dataset of transactions. It learns from details like the transaction amount, time, and recipient to understand the pattern of a fraudulent payment. Once trained, a user can enter the details of a transaction. The model then quickly predicts if the transaction is safe or fraudulent. Additionally, the system offers comprehensive analytics, displaying fraud distribution across categories and overall model performance.
This smart tool is better than old-fashioned methods and helps protect users from financial loss, making digital payments safer for everyone.
Keywords:
UPI Fraud Detection, Machine Learning, Random Forest, Digital Payment Security, Transaction Analysis, Financial Crime Prevention, Predictive Analytics, Ensemble Learning
Cite Article:
"UPI Fraud Detection Using Machine Learning", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 10, page no.a399-a404, October-2025, Available :http://www.ijrti.org/papers/IJRTI2510035.pdf
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000151
ISSN:
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator