Analysis of machine learning techniques in detecting and preventing e-commerce fraud effectively
1 Independent Researcher, London, ON, Canada.
2 Zenith General Insurance Company, Limited, Nigeria.
3 KPMG, USA.
Research Article
International Journal of Frontiers in Science and Technology Research, 2024, 07(01), 025–034.
Article DOI: 10.53294/ijfstr.2024.7.1.0046
Publication history:
Received on 17 June 2024; revised on 30 July 2024; accepted on 02 August 2024
Abstract:
E-commerce fraud poses significant challenges to businesses and consumers, necessitating advanced detection and prevention methods. This paper provides a comprehensive analysis of the current landscape of e-commerce fraud and the application of machine learning techniques in combating it. We explore the types of e-commerce fraud, their impact, and traditional detection methods. The efficacy of various machine learning models, including supervised and unsupervised learning techniques, hybrid approaches, and ensemble methods, is evaluated based on accuracy, precision, recall, and F1 score. The paper discusses emerging trends such as AI, behavioral biometrics, and blockchain technology, along with potential advancements in machine learning techniques like deep learning, reinforcement learning, and federated learning. Ethical considerations and data privacy issues are highlighted, emphasizing the need for responsible use of these technologies. The findings demonstrate the significant role of machine learning in enhancing e-commerce fraud detection and prevention, underscoring the importance of continuous innovation and ethical practices.
Keywords:
E-commerce fraud; Machine learning; Fraud detection; Behavioral biometrics; Blockchain technology; Data privacy
Full text article in PDF:
Copyright information:
Copyright © 2024 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0