Advanced Web Mining Techniques for Detecting and Predicting E-commerce Fraud

Abstract

E-commerce platforms have revolutionized the way consumers purchase goods and services, offering unparalleled convenience and global accessibility. However, as online marketplaces continue to expand, fraudulent activities have become increasingly sophisticated, leading to significant financial losses and eroded trust among consumers and merchants. Traditional fraud detection measures often rely on rule-based systems that become obsolete as attackers continually evolve their tactics. This paper presents a comprehensive overview of advanced web mining techniques employed to detect and predict e-commerce fraud. We explore state-of-the-art machine learning models, feature extraction methods, data preprocessing strategies, and scalable architectures that leverage big data infrastructures. The proposed methodologies include advanced anomaly detection approaches, hybrid feature engineering with natural language processing (NLP), and deep neural networks for user behavior modeling. Through systematic experimentation and performance evaluation, we demonstrate that our proposed framework offers improved detection accuracy, reduced false positives, and robust predictive capabilities. The findings suggest that advanced web mining techniques can serve as a pivotal component in building secure, trustworthy, and future-proof e-commerce environments.