Machine Learning-Based UPI Fraud Detection: A Comprehensive Approach Using Random Forest

DOI

10.2991/978-94-6463-852-3_29How to use a DOI?

Keywords

UPI Fraud Detection; Machine Learning; Random Forest; Digital Payments; Transaction Security; Anomaly Detection; Financial Fraud; Feature Engineering; Real-time Fraud Detection; Supervised Learning; Cybersecurity; Imbalanced Data Handling; Transaction Monitoring

Abstract

As adoption of UPI for online transactions is on the rise, fraud has also been rising sharply. This project applies machine learning to identify UPI fraud in real time with the help of Random Forest algorithm. The model was trained using a dataset that includes transaction information such as amount, time, sender and receiver activity, device data. Data preprocessing steps such as dealing with missing values, feature scaling, encoding the categorical variables, feature selection were utilized to enhance the performance of the model. Random Forest algorithm which is strong and can deal with big data creates an ensemble of many decision trees and aggregates their prediction to achieve higher accuracy of 96% and lower overfitting. The model achieved a precision of 97%, recall of 91%, and an F1-score of 93%, indicating its strong capability to accurately detect fraudulent transactions while minimizing false positives and false negatives. Results of experiments reveal that the model is capable of identifying fraudulent transactions with high accuracy and recall. The system includes user friendly interface that gives real time fraud detection, transactional monitoring and fraud alerts. With the use of machine learning in digital payment security this project offers a trustworthy means to prevent UPI fraud and enjoy secure financial transactions.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Bhramanand Sethi
AU  - Sarvednya Mhatre
AU  - Sachin Yadav
AU  - Siuli Das
AU  - Vaishali Jadhav
PY  - 2025
DA  - 2025/10/07
TI  - Machine Learning-Based UPI Fraud Detection: A Comprehensive Approach Using Random Forest
BT  - Proceedings of the MULTINOVA: First International Conference on Artificial Intelligence in Engineering, Healthcare and Sciences (ICAIEHS- 2025)
PB  - Atlantis Press
SP  - 462
EP  - 470
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-852-3_29
DO  - 10.2991/978-94-6463-852-3_29
ID  - Sethi2025
ER  -