International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

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Volume 8 Issue 2
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Online Payment Fraud Detection Using ML

Author(s) Ms. Juweria Ibrahim Petkar, Shakila Siddavatam
Country India
Abstract The rapid growth of digital payment systems—such as credit cards, mobile wallets, and online banking—has transformed the way people and businesses handle financial transactions. While these technologies have improved convenience and accessibility, they have also created new opportunities for fraud, including identity theft, phishing, and unauthorized payments. Traditional fraud detection methods, which rely on fixed rules and thresholds, often fail to keep pace with the evolving tactics of cybercriminals.
Machine learning (ML) provides a more adaptive and intelligent solution by analyzing large volumes of transaction data, identifying hidden patterns, and detecting anomalies in real time. Unlike static rule-based systems, ML models continuously improve as they learn from new data, making them more effective against emerging fraud strategies.
This research builds on existing studies that highlight the limitations of conventional fraud detection and the potential of ML approaches. It explores supervised, unsupervised, and deep learning techniques, while addressing challenges such as data imbalance, evolving fraud patterns, and the need for rapid detection. The goal is to demonstrate how machine learning can strengthen online payment security, minimize financial losses, and enhance customer trust in digital transactions.
Field Computer
Published In Volume 8, Issue 2, March-April 2026
Published On 2026-03-07
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E-ISSN 2582-2160
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