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K. Sunanda is an Assistant Professor in the Department of CSE (AI & ML) at Narsimha Reddy Engineering College, India. Her research interests include Machine Learning, cybersecurity, and intelligent computing.
The swift growth of digital banking, mobile payment systems, cloud computing, and e-commerce platforms has markedly elevated the quantity and intricacy of financial transactions. While these innovations have enhanced efficiency and accessibility, they have simultaneously created new avenues for financial fraud, cyberattacks, money laundering, and unauthorised transactions. Conventional rule-based fraud detection systems frequently struggle to recognise new and complex attack patterns because they cannot adjust to changing threats. Anomaly Detection has become an essential method for recognising atypical patterns and dubious behaviours in banking and payment systems. Utilising Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), and Big Data Analytics, anomaly detection systems can recognise irregularities in standard transaction patterns and offer immediate fraud prevention solutions. This chapter examines the fundamentals, methodologies, frameworks, applications, obstacles, and prospective advancements of anomaly detection within contemporary banking and payment systems.