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Dr. G. Jagadeeswar Reddy is Professor and Head of the Department of Electronics and Communication Engineering at Mahatma Gandhi Institute of Technology, India. His research interests include communication systems, IoT, embedded systems, and intelligent technologies.
Automated Teller Machines (ATMs) have emerged as a prevalent banking medium for cash withdrawals, fund transfers, balance checks, and various financial services. Notwithstanding their ease of use and availability, ATMs continue to be a primary focus for hackers because of their direct linkage to financial systems and consumer accounts. Fraudulent methods including card skimming, card trapping, PIN theft, malware assaults, cash trapping, card cloning, jackpotting, and social engineering persist in jeopardising ATM security globally. Conventional security measures frequently fall short in addressing advanced and swiftly changing fraudulent methods. Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), biometric verification, behavioural analysis, and blockchain innovations have surfaced as potent measures for improving ATM security and mitigating fraud. This chapter offers an extensive examination of ATM card fraud detection and prevention strategies, encompassing fraud categories, security frameworks, advanced detection methodologies, novel technologies, obstacles, and prospective research avenues.