Agent-Based Artificial Intelligence in Fraud Detection: Strategies for Building Secure Self-Governing Systems

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.