Analysis of machine learning techniques in detecting and preventing e-commerce fraud effectively

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.