UPI Fraud Detection using Machine Learning

: Because of its user-friendliness and real-time transactions, the Unified Payments Interface (UPI) has completely transformed digital payments. On the other hand, fraud has increased as a result of this quick acceptance. It offers a fresh method for identifying UPI fraud by utilizing behavioural analytics and cutting-edge machine learning algorithms. We create a strong detection system that accurately detects suspicious activity by utilizing a wide range of data, including transaction patterns, user behaviours, and past fraud incidents. To improve the security of UPI transactions, our method incorporates features including anomaly detection and pattern recognition. The usefulness of our model in reducing monetary losses and preserving user confidence in digital payment systems is demonstrated by experimental results that show a notable improvement in fraud detection rates when compared to conventional methods.

Authors

Publication Details

Journal
International Journal For Multidisciplinary Research
Published
2025-04-16
DOI
https://doi.org/10.36948/ijfmr.2025.v07i02.42152
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

Barupati Akhila, Putluru Bhavana -, C. Anitha, Balasundar I. Raju et al.
International Journal For Multidisciplinary Research
Imbalanced Data Classification Techniques
article

UPI Fraud Detection using Machine Learning

Barupati Akhila, Putluru Bhavana -, C. Anitha, Balasundar I. Raju, Mr Reddy
article en

Abstract

: Because of its user-friendliness and real-time transactions, the Unified Payments Interface (UPI) has completely transformed digital payments. On the other hand, fraud has increased as a result of this quick acceptance. It offers a fresh method for identifying UPI fraud by utilizing behavioural analytics and cutting-edge machine learning algorithms. We create a strong detection system that accurately detects suspicious activity by utilizing a wide range of data, including transaction patterns, user behaviours, and past fraud incidents. To improve the security of UPI transactions, our method incorporates features including anomaly detection and pattern recognition. The usefulness of our model in reducing monetary losses and preserving user confidence in digital payment systems is demonstrated by experimental results that show a notable improvement in fraud detection rates when compared to conventional methods.

International Journal For Multidisciplinary ResearchVol. 7(2)
Openalex Percentile: Top 96%
Imbalanced Data Classification Techniques
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