Advanced ML Techniques for Fraud Detection: Case Studies in Retail and E-Commerce Applications

Abstract

Fraud detection in retail and e-commerce has evolved significantly with the advent of advanced machine learning (ML) techniques. This paper investigates the application of these techniques in identifying and mitigating fraudulent activities within these sectors. Machine learning algorithms, particularly supervised and unsupervised models, have demonstrated their potential in recognizing anomalies and fraudulent patterns. Through case studies in retail and e-commerce, this research explores how different ML models, including decision trees, support vector machines, neural networks, and ensemble methods, have been effectively implemented to improve fraud detection systems. The paper also discusses the challenges, benefits, and future directions of ML in fraud detection, offering a comprehensive view of its impact on both business and security.