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Online Payment Fraud Detection Using Machine Learning Techniques
Subject area: Science,Engineering and Technology · Area of research: Computer Science and Engineering
DOI: https://doi.org/10.64388/IREV9I6-1713148
Abstract
Online payment systems have become an integral part of modern financial transactions. However, the rapid growth of digital payments has also increased the risk of fraudulent activities, leading to financial losses. Traditional rule-based fraud detection methods are inefficient in detecting complex and evolving fraud patterns. This paper proposes an online payment fraud detection system using machine learning techniques to classify transactions as fraudulent or legitimate. The system involves data preprocessing, feature selection, and classification using machine learning algorithms such as Random Forest, Logistic Regression, and K-Nearest Neighbors. Experimental results indicate that machine learning-based approaches improve detection accuracy and reduce false positives. The proposed system provides an efficient and scalable solution for secure online payment transactions.
Keywords
Online Payment, Fraud Detection, Machine Learning, Random Forest, Financial Security
How to cite this paper
@article{1713148,
author = {Sonam, Ifra Mazhar , Ayesha Sulthana },
title = {Online Payment Fraud Detection Using Machine Learning Techniques},
journal = {Iconic Research And Engineering Journals},
year = {2025},
volume = {9},
number = {6},
pages = {1801-1804},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1713148.pdf},
abstract = {Online payment systems have become an integral part of modern financial transactions. However, the rapid
growth of digital payments has also increased the risk of fraudulent activities, leading to financial losses. Traditional rule-based fraud detection methods are inefficient in detecting complex and evolving fraud patterns. This paper proposes an online payment fraud detection system using machine learning techniques to classify transactions as fraudulent or legitimate. The system involves data preprocessing, feature selection, and classification using machine learning algorithms such as Random Forest, Logistic Regression, and K-Nearest Neighbors. Experimental results indicate that machine learning-based approaches improve detection accuracy and reduce false positives. The proposed system provides an efficient and scalable solution for secure online payment transactions.
},
keywords = {Online Payment, Fraud Detection, Machine Learning, Random Forest, Financial Security},
month = {December},
doi = {https://doi.org/10.64388/IREV9I6-1713148}
}