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E-commerce has witnessed exponential growth, creating opportunities for global trade, transforming the exchange of goods and services, and redefining customer engagement. According to eCommerce Statistics (SellersCommerce, 2025), there are approximately 2.77 billion online shoppers globally, accounting for about $6.86 trillion in sales in 2025. Consequently, e-commerce has witnessed a significant operational vulnerability, such as the increasingly sophisticated card-not-present (CNP) fraud. CNP online fraud is one of the most pervasive and damaging threats to global e-commerce, where goods and services are paid for without the physical presence of the card, typically using stolen payment credentials (Mekterović et al., 2021; Plateaux et al., 2018). MasterCard has projected a global loss of $48 billion annually due to CNP fraud.
Several efforts have been made by human resources (HR) management in e-commerce to establish training programs that combat CNP and related fraud. However, most of these trainings rely on static, policy-focused instruction; they are often limited to compliance checklists and generic fraud awareness sessions (Tunçsiper, 2025). Consequently, this approach lacks the practical, adaptive elements needed to equip employees for combating the evolving sophistication of CNP fraud (Chen et al., 2025), highlighting the need for a more technology-driven training.
Emerging technologies such as artificial intelligence (AI)-driven systems, alongside gamified immersive simulations, have the potential to detect CNP fraud patterns, enabling e-commerce employees to respond swiftly to suspicious online activities. For instance, AI-enabled technologies effectively detect complex fraud patterns in high-volume digital transactions through real-time transaction stream monitoring, purchase behavioral anomalies, and precision flagging of suspicious activities beyond human capacity (Papasavva et al., 2025). On the other hand, gamification mirrors real-world fraud scenarios to simulate environments that deepen understanding of fraud dynamics and enable engaging learning for informed decision making (Alothman, 2024). Thus, Rana and Chicone (2025) noted that the convergence of AI and gamification enables e-commerce employees to harness adaptive intelligence and immersive training to effectively detect CNP fraud in real-world digital transactions. Nevertheless, detecting CNP fraud is not solely a technical task; it also depends on human ability to interpret patterns and adequately respond to the threat based on ethical and professional judgment and behavioral insights.
Training bridges the gap between technology and its application by enabling staff to use AI-driven and gamified systems to enhance judgement, build confidence, improve skills, and reduce CNP fraud in real time (Su & Liu, 2023). Hence, there are increasing studies on HR training modules focused on employee upskilling to enhance online financial fraud detection.
Razaque et al. (2022) advanced the study by developing a CNP fraud detection and prevention method that leverages big data, regression learning, and Python for implementation. However, their work is not related to e-commerce. While Bodker et al. (2022) proposed a conventional crime script reading approach to identify CNP fraud patterns in e-commerce, their method remains limited and ineffective in addressing the increasingly sophisticated and adaptive nature of CNP fraud. In a more recent study, Ait Said et al. (2025) proposed a behavioral profiling framework that uses ISO8583 fields to identify transaction anomalies indicative of CNP fraudulent activities. However, their work is missing aspects of gamification and AI algorithms. While scholars like Chen et al. (2025) and Singh et al. (2025) have devised means of using AI in e-commerce, their studies focused on advertisement domain clicks and privacy concerns, respectively.