Agent-Based Artificial Intelligence in Fraud Detection explores advanced AI-driven approaches for identifying and preventing fraud in modern digital ecosystems. The book presents agent-based artificial intelligence, multi-agent systems, blockchain integration, and deep reinforcement learning as powerful tools for adaptive and real-time fraud detection. It covers financial fraud, public sector applications, cloud security, currency recognition, and distributed decision-making systems.

Through case studies and practical frameworks, it highlights scalability, interoperability, ethics, and regulatory challenges in deployment. The work provides a multidisciplinary roadmap for researchers, policymakers, and cybersecurity professionals to build secure, transparent, and resilient digital financial systems in an evolving technological landscape.

Dr. C. Kishor Kumar Reddy is a seasoned academician and researcher with over 13 years of experience in computer science and engineering. Currently serving at Stanley College of Engineering and Technology for Women, Hyderabad, he holds a Ph.D. in Computer Science and Engineering and a Postdoctoral Fellowship from Universiti Kebangsaan Malaysia, Malaysia. Dr. C. Kishor Kumar Reddy has made significant contributions in areas such as Artificial Intelligence, Machine Learning, Deep Learning, Federated Learning, Cybersecurity, Healthcare 6.0, and Disaster Management. He has authored and co-authored 230+ research articles in reputed SCI/Scopus-indexed journals, presented in international conferences, and contributed to numerous book chapters with leading publishers like Springer, CRC Press, Wiley-IEEE, IGI Global, and Cambridge Scholars Publishing. He also holds several published patents and serves as an editor for multiple scholarly books on emerging technologies. Dr. C. Kishor Kumar Reddy is an active member of professional bodies such as the Indian Society for Technical Education, Computer Society of India, and the International Association of Engineers, among others.

Anindya Nag obtained an M.Sc. in Computer Science and Engineering from Khulna University, Khulna, Bangladesh, and a B.Tech. in Computer Science and Engineering from Adamas University, Kolkata, India. He is working as a lecturer in the Department of Computer Science and Engineering at the Northern University of Business and Technology, Khulna, Bangladesh. Anindya Nag’s research focuses on health informatics, medical Internet of Things, neuroscience, and machine learning. He serves as a reviewer for numerous prestigious journals and international conferences. He has authored and co-authored about 84 publications, including journal articles, conference papers, and book chapters, and has co-edited ten books. He is currently pursuing his PhD in Computer Science at Ca’ Foscari University of Venice, Italy.

Dr Sarika S is Professor and Head of the Department of Artificial Intelligence and Data Science at Adi Shankara Institute of Engineering and Technology, Kalady, India, under APJ Abdul Kalam Technological University, India. Dr Sarika S is a patent holder and has authored three books and eight book chapters. She has also served as a reviewer for leading international journals, contributing to academic quality and research advancement in the field of Artificial Intelligence and Data Science.

Chapter 1 Foundations and Principles of Agent-Based Artificial Intelligence for Enhancing Fraud Detection Mechanisms in Complex Financial Ecosystem

Chapter 2 Comprehensive Analysis of Fraud Typologies & Behavioural Patterns in Financial, E-Commerce, and Digital Identity Domains

Chapter 3 Design and Implementation of Multi-Agent Architectures for Scalable and Adaptive Data Fraud Detection Systems

Chapter 4 Integrating Blockchain Technology with Agent-Based AI to Ensure Data Integrity, Transparency, and Trust in Fraud Prevention Systems

Chapter 5 Applications of Agent-Based AI in Public Sector Fraud Detection: Enhancing Transparency and Accountability in Government Services

Chapter 6 Real-Time Detection of Transactional Fraud in Digital Payment Platforms through Intelligent Multi-Agent Systems

Chapter 7 Deep Learning Framework with Agent-Based Currency Recognition in Fraud Detection to Empower the Visually Impaired

Chapter 8 Applying Deep Reinforcement Learning to Develop Autonomous Agents Capable of Continuous Adaptation in Dynamic Fraud Environments

Chapter 9 Advanced Coordination, Negotiation, and Conflict Resolution Techniques Among Autonomous Agents in Distributed Fraud Detection Networks

Chapter 10 AI in Cloud Security: Threat Detection, Compliance Monitoring, and Intelligent Response Automation

Chapter 11 Addressing Scalability, Latency, and Interoperability Challenges in Real-World Deployments of Agent-Based Fraud Detection Systems

Chapter 12 Navigating Ethical, Legal, and Regulatory Challenges in the Deployment of Agent-Based AI Systems for Fraud Prevention in Financial Services

Chapter 13 Challenges, Opportunities, and a Research Roadmap for the Next Generation of Agent-Based AI Solutions in Fraud Prevention