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Dr. S. Velayutham is a Professor in the Department of MBA at Bharath Institute of Science and Technology, India. His research interests include business analytics, digital marketing, e-commerce, and management information systems.
The swift growth of e-commerce has revolutionised the worldwide retail environment by allowing consumers and enterprises to perform transactions effortlessly via digital platforms. Nevertheless, the growing prevalence of e-commerce, electronic payment solutions, mobile commerce, cloud technology, and Internet of Things (IoT) services has heightened the threat of cyber fraud and financial offences. E-commerce platforms often fall victim to credit card fraud, account takeover incidents, identity theft, counterfeit merchant operations, refund fraud, misuse of promotions, phishing schemes, and automated bot attacks. Conventional rule-based security frameworks frequently struggle to identify intricate and advancing fraudulent behaviours. Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), Big Data Analytics, Behavioural Analytics, Blockchain, and Federated Learning have surfaced as formidable technologies for identifying and alleviating fraud in online transactions. This chapter delineates the principles, frameworks, methodologies, applications, obstacles, and prospective research avenues pertaining to e-commerce fraud detection and mitigation strategies for secure online business environments.