Online Payment Fraud Detection Using Machine Learning Techniques

• Author(s): Sonam; Ifra Mazhar ; Ayesha Sulthana
• Paper ID: 1713148
• Page: 1801-1804
• Published Date: 24-12-2025
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 9 Issue 6 December-2025

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

Citations

IRE Journals:
Sonam, Ifra Mazhar , Ayesha Sulthana "Online Payment Fraud Detection Using Machine Learning Techniques" Iconic Research And Engineering Journals Volume 9 Issue 6 2025 Page 1801-1804 https://doi.org/10.64388/IREV9I6-1713148

IEEE:
Sonam, Ifra Mazhar , Ayesha Sulthana "Online Payment Fraud Detection Using Machine Learning Techniques" Iconic Research And Engineering Journals, 9(6) https://doi.org/10.64388/IREV9I6-1713148