AI-Enabled Fraud Detection in E-commerce

Abstract

The rapid growth of e-commerce has revolutionized the way businesses operate, providing unprecedented convenience for consumers and opening new avenues for commerce. However, this growth has also attracted the attention of fraudsters, who exploit vulnerabilities in online payment systems to perpetrate fraudulent activities. Traditional rule-based fraud detection systems have proven insufficient in combating the evolving tactics of these fraudsters. As a result, the integration of Artificial Intelligence (AI) has become a critical component of modern fraud detection mechanisms in the e-commerce industry.

This paper explores the application of AI in fraud detection within the e-commerce domain. It delves into the various techniques and algorithms that leverage machine learning and data analysis to identify fraudulent transactions. By examining historical transaction data, AI models can identify anomalous patterns, detect unauthorized access, and flag potentially fraudulent activities in real-time. The adoption of AI-driven fraud detection not only enhances the security of e-commerce platforms but also minimizes false positives, ensuring a smoother experience for legitimate customers.

In this paper, we provide a comprehensive overview of AI-based fraud detection methodologies, including supervised and unsupervised learning approaches, neural network models, and ensemble techniques. We also discuss the advantages and limitations of AI-enabled fraud detection and the importance of continuous model adaptation to keep up with the evolving nature of online fraud. Furthermore, we address the ethical and privacy concerns associated with AI-powered fraud detection and suggest strategies for responsible implementation.