Generative AI for Fraud Detection

From Theory to Enterprise Deployment
1. Auflage 2027
ISBN: 978-1-041-11119-1
Verlag: Taylor & Francis Ltd

Buch, Englisch, 408 Seiten, Format (B × H): 178 mm x 254 mm

With the growth of complex frauds, cyber threats, and financial crimes, standard detection approaches typically fall short. Generative AI, with its capacity to synthesize synthetic data, analyze anomalies, and improve predictive analytics, provides creative answers to these difficulties. This book addresses the revolutionary impact of generative AI in fraud detection, addressing the expanding complexity of fraudulent operations in numerous sectors. This book starts with core idea, providing readers to the foundations of fraud detection and generative AI technologies such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs). It then advances to key ideas, concentrating on synthetic data creation, anomaly detection, and the integration of machine learning models in fraud analytics. In this book, some topics like practical applications are also highlighted via case studies in financial services, healthcare, insurance, retail, and cybersecurity. This book illustrates how generative AI can detect credit card fraud, identify fake insurance claims, and safeguard e-commerce platforms against fraudulent transactions. On other side, emerging technologies like blockchain, IoT, and quantum AI are investigated as complementary methods for increasing fraud prevention frameworks. Also, some ethical aspects, including data privacy, AI biases, and regulatory compliance, are also highlighted to meet the problems of deploying generative AI ethically. With this book, readers will get insights on open-source technologies, industry-specific frameworks, and assessment criteria for installing fraud detection systems efficiently. We can assure to our readers that this book will provide a major setback to future researchers by addressing future trends, including real-time threat mitigation and policy implications for global fraud prevention.

Zielgruppe

Academic, Postgraduate, and Professional Reference

Autoren/Hrsg.

Tyagi, Amit Kumar

Tiwari, Shrikant

Fachgebiete

• Mathematik | Informatik EDV | Informatik Technische Informatik Computersicherheit Kryptographie, Datenverschlüsselung
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz
• Mathematik | Informatik EDV | Informatik Technische Informatik Computersicherheit Computer-Forensik
• Mathematik | Informatik EDV | Informatik Computerkommunikation & -vernetzung Netzwerksicherheit

Inhaltsverzeichnis

Preface. Acknowledgement. Chapter 1: The Genesis of Generative Artificial Intelligence in Fraud Detection. Chapter 2: Analytical Foundations and Machine Learning under Uncertainty. Chapter 3: Deep Neural and Generative Architectures: Frameworks and Design Paradigms. Chapter 4: Synthetic Intelligence: Data Creation, Fidelity, and Governance. Chapter 5: Multimodal Fraud Intelligence: Text, Image, Audio, and Video. Chapter 6: Generative AI in Financial Systems and Banking Forensics. Chapter 7: Healthcare and Insurance Fraud Analytics with Generative AI. Chapter 8: Cyber Intelligence, Blockchain Defenses, and Web3 Fraud Prevention. Chapter 9: Behavioral and Anomaly Analytics using Generative Models. Chapter 10: Ethics, Fairness, and Explainability in Generative Fraud Models. Chapter 11: Enterprise Deployment: Cloud, Edge, and Federated Learning Ecosystems. Chapter 12: Legal, Regulatory, and Societal Dimensions of Generative Fraud Analytics. Chapter 13: Evaluation Metrics, Stress Testing, and Benchmarking Standards. Chapter 14: Cross-Industry Case Studies and Applied Frameworks. Chapter 15: Conclusion to the book: Generative AI in Fraud Detection. Bibliography. Index.

Über Autor(innen)

Amit Kumar Tyagi is working as an Assistant Professor, at National Forensic Sciences University, Gandhinagar, Gujarat, India. He received his Ph.D. Degree (Full-Time) in 2018 from Pondicherry Central University, 605014, Puducherry, India. About his academic experience, he has worked as an assistant professor at several institutes like Lord Krishna College of Engineering (LKCE), Ghaziabad (for the periods of July 2009- July 2010, and October 2012- October 2013), Lingaya’s Vidyapeeth (formerly known as Lingaya’s University), Faridabad (September 2018- May 2019), VIT Chennai (June 2019- November 2022) and NIFT New Delhi (November 2022- September 2025). His supervision experience includes more than 10 Masters' dissertations and one PhD thesis. He has contributed to several projects such as “AARIN” and “P3- Block” to address some of the open issues related to the privacy breaches in Vehicular Applications (such as Parking) and Medical Cyber Physical Systems (MCPS). He has done more than 60 Edited and authored books and collaborated with eminent professors across the world from top QS ranked university. Also, He has published over 350 research articles in refereed high impact journals, conferences and books, and some of his articles has been awarded as best paper awards. Also, he has filed more than 15 patents (Nationally and Internationally) in the area of Deep Learning, Internet of Things, Cyber Physical Systems and Computer Vision. He is a Winner of Faculty Research Award for the Year of 2020, 2021 and 2022 (consecutive three years) given by Vellore Institute of Technology, Chennai, Tamilnadu, India. His current research focuses on Next Generation Machine Based Communications, Blockchain Technology, Smart and Secure Computing and Privacy. He is a senior member of IEEE.

Shrikant Tiwari (Senior Member, IEEE) was received his Ph.D. in the Department of Computer Science & Engineering at the Indian Institute of Technology (Banaras Hindu University), Varanasi, India, in 2012 and his M. Tech. in Computer Science and Technology from the University of Mysore, India, in 2009. Currently, he serves as a Professor in the School of Computing Science and Engineering at Galgotias University, Greater Noida, Gautam Buddha Nagar, Uttar Pradesh, India. Dr. Tiwari has published over 85+ papers in refereed high-impact journals, conferences and books, with several of his articles receiving best paper awards. He has also filed more than 10 patents, both nationally and internationally, in the areas of deep learning, the Internet of Things, cyber-physical systems and computer vision. Additionally, he has edited more than ten books for prestigious publishers such as IET, Elsevier, Springer and CRC Press. His research interests include machine learning, deep learning, computer vision, medical image analysis, pattern recognition and biometrics.