UPI Fraud Detection Using Machine Learning

Authors

  • MD.Nazmoddin , Mitta Swetha , Gattu Yashwanthi , Yalangi Divyasree

Keywords:

UPI fraud detection, machine learning, anomaly detection, deep learning, financial security, transaction analysis, real-time fraud prevention.

Abstract

With the rapid adoption of Unified Payments Interface (UPI), digital transactions have significantly increased, leading to a rise in fraudulent activities. Traditional rule-based fraud detection methods often fail to adapt to evolving fraud patterns. This study proposes a machine learning-based UPI fraud detection system that leverages supervised and unsupervised learning techniques to identify suspicious transactions in real-time.

References

Gupta, A., & Sharma, R. (2023). "Cybersecurity Challenges in Digital Payments: A Case Study on UPI Fraud". International Journal of Cyber Research, 12(3), 45-58. [2] Patel, S., & Verma, K. (2022). "Machine Learning Approaches for Detecting Financial Fraud in Real-Time Transactions". IEEE Transactions on Financial Technology, 29(4), 112-126.

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Published

2024-09-10

How to Cite

MD.Nazmoddin , Mitta Swetha , Gattu Yashwanthi , Yalangi Divyasree. (2024). UPI Fraud Detection Using Machine Learning . Journal of Computational Analysis and Applications (JoCAAA), 33(05), 1192–1200. Retrieved from https://eudoxuspress.com/index.php/pub/article/view/1875

Issue

Vol. 33 No. 05 (2024): JOCAAA

Section

Articles

License

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.