This review evaluates strategies for enhancing fraud detection and prevention in digital transactions, highlighting key techniques like machine learning and behavioral analytics.
This chapter reviews techniques for detecting and preventing card-not-present (CNP) fraud, which has escalated due to the rise in online transactions and the COVID-19 pandemic. It evaluates methods like machine learning, behavioral analytics and risk-scoring, highlighting their integration for effective fraud detection. Key findings indicate that traditional methods remain relevant, while advanced technologies, such as artificial intelligence, significantly enhance detection accuracy. The chapter emphasizes collaboration across sectors to combat CNP fraud and explores future research aimed at enhancing fraud detection, user experience, and ethical data usage, ultimately contributing to safer digital transactions.
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Owusu-Mensah et al. (2025) studied this question.
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