UPI Fraud Detection Using Machine Learning

Abstract: Digital transactions have been growing rapidly, leading to a parallel increase in online payment fraud. According to the Reserve Bank of India, digital payments saw a 216% rise in volume and a 10% increase in value between March 2019 and March 2022. As more people embrace digital payment methods, security challenges and a lack of awareness about online payments persist. A few years ago, online payments were rare, but now UPI payment QR codes can be found at nearly every doorstep. This has opened the door for scammers and fraudsters to exploit these systems, deceiving people and carrying out fraudulent transactions. However, online transactions are monitored, which allows for the application of advanced tools to analyze them. This system aims to develop a machine learning model to detect fraudulent activities within transaction datasets.

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Publication Details

Journal
International Journal for Research in Applied Science and Engineering Technology
Published
2025-01-27
DOI
https://doi.org/10.22214/ijraset.2025.66643
Primary Topic
Imbalanced Data Classification Techniques
Type
article
Field-Weighted Citation Impact
0.00
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article

UPI Fraud Detection Using Machine Learning

A Nithin, Mohammad Yasir, N Naveen Kumar Reddy, N. R.
International Journal for Research in Applied Science and Engineering Technology
Imbalanced Data Classification Techniques
article

UPI Fraud Detection Using Machine Learning

A Nithin, Mohammad Yasir, N Naveen Kumar Reddy, N. R.
article en

Abstract

Abstract: Digital transactions have been growing rapidly, leading to a parallel increase in online payment fraud. According to the Reserve Bank of India, digital payments saw a 216% rise in volume and a 10% increase in value between March 2019 and March 2022. As more people embrace digital payment methods, security challenges and a lack of awareness about online payments persist. A few years ago, online payments were rare, but now UPI payment QR codes can be found at nearly every doorstep. This has opened the door for scammers and fraudsters to exploit these systems, deceiving people and carrying out fraudulent transactions. However, online transactions are monitored, which allows for the application of advanced tools to analyze them. This system aims to develop a machine learning model to detect fraudulent activities within transaction datasets.

International Journal for Research in Applied Science and Engineering TechnologyVol. 13(1)
Openalex Percentile: Top 99%
Imbalanced Data Classification Techniques
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