Loading...
A. Kiruthika is an Assistant Professor in the Department of CSE (Artificial Intelligence and Machine Learning) at Velalar College of Engineering and Technology, India. Her research focuses on AI, Deep Learning, and intelligent computing.
The rapid expansion of digital financial services, internet banking, cloud computing, the Internet of Things (IoT), and e-commerce platforms has markedly heightened the frequency and intricacy of financial fraud. Conventional rule-based fraud detection systems frequently struggle to recognise advanced and changing fraudulent schemes because of their restricted adaptability and incapacity to handle extensive data sets. Machine Learning (ML) has surfaced as an effective method for recognising fraudulent transactions by autonomously discerning concealed patterns, recognising anomalies, and offering instantaneous decision assistance. This chapter offers an extensive summary of machine learning methodologies employed in fraud detection, encompassing supervised, unsupervised, semi-supervised, and reinforcement learning strategies. A range of methods, applications, obstacles, and prospective research avenues are examined within the framework of cloud-enabled IoT and e-commerce environments.