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Intelligent Fraud Detection Systems Using AI, Machine Learning, and IoT: A Behavioral and Psychological Analytics Approach

AVINASH REDDY AITHA, Dr. K. PRABHA, Dr. L. VIJAYAKUMAR, S. GOKUL

Indexed In: Google scholar

Release Date: To be updated | Copyright:©2026 | Pages: 739

DOI: To be updated Cite

ISBN10: 0 | ISBN13: 0

Intelligent Fraud Detection Systems Using AI, Machine Learning, and IoT: A Behavioral and Psychological Analytics Approach explores advanced technologies and interdisciplinary methodologies for identifying, predicting, and preventing fraudulent activities across digital ecosystems. The book integrates artificial intelligence, machine learning, Internet of Things (IoT), behavioral analytics, and psychological profiling to develop intelligent fraud detection frameworks. It examines real-world applications in banking, healthcare, e-commerce, insurance, cybersecurity, and smart environments. Through case studies, predictive models, and emerging research trends, the book highlights innovative strategies for risk assessment and anomaly detection. It serves as a valuable resource for researchers, practitioners, policymakers, and students.

This book covers the foundations and applications of intelligent fraud detection using Artificial Intelligence (AI), Machine Learning (ML), and the Internet of Things (IoT). It explores behavioral analytics, psychological profiling, anomaly detection, predictive modeling, risk assessment, and real-time fraud monitoring. Topics include deep learning, explainable AI, data mining, sensor-based fraud detection, cybersecurity threats, financial fraud, healthcare fraud, insurance fraud, e-commerce fraud, identity theft, and insider threats. The book also addresses ethical considerations, privacy preservation, data governance, and regulatory compliance. Case studies, practical frameworks, and emerging research directions provide comprehensive insights into modern fraud prevention and decision-support systems.

Table Of Contents

Detailed Table Of Contents


Chapter 1

Introduction to Artificial Intelligence, Machine Learning, and IoT in Intelligent System Development

Akana Chandra Mouli Venkata Srinivas, R Suganya

(Pages:37)

Chapter 2

Fundamental Concepts and Evolution of Fraud Detection Systems in Digital Environments

Tayyaba Tabassum, R Sakthidevi

(Pages:35)

Chapter 3

Data Science Foundations for AI-Driven Fraud Detection and Behavioral Analyticss

Kharmega Sundararaj G, R. Sivakumar

(Pages:32)

Chapter 4

Ethical, Privacy, and Security Challenges in AI-Based Behavioral and Fraud Detection Systems

Poornima Jogi, A. Shravani

(Pages:33)

Chapter 5

Supervised and Unsupervised Machine Learning Techniques for Fraud Detection Applications

Deepika Upadhyay, Sri Sathya K B

(Pages:31)

Chapter 6

Security Vulnerabilities and Risk Management in IoT-Enabled Fraud Detection Systems

C. Sriram, B Anbarasu

(Pages:38)

Chapter 7

Cloud and IoT Integration for Scalable and Distributed Fraud Detection Applications

R Jayamala, P. Buvaneswari

(Pages:35)

Chapter 8

Human Behavior Modeling Using AI and Machine Learning Techniques

Deepika Dubey, Indrani Merugu

(Pages:39)

Chapter 9

Psychological Profiling and Its Role in Intelligent Fraud Detection Systems

Anil Pandurang Gaikwad, M. Keerthi Priya

(Pages:37)

Chapter 10

Anomaly Detection Based on Behavioral Patterns Using AI and Statistical Models

MOHAN PRABHU S, R. Varadharajan

(Pages:37)

Chapter 11

AI-Driven Fraud Detection in Digital Banking and Financial Transaction Systems

P. Boopathimaharaja, M. Athiththachozhan

(Pages:33)

Chapter 12

Machine Learning Approaches for Credit Card Fraud Detection and Risk Assessment

Amreen Anjum, Vadla Anuja

(Pages:31)

Chapter 13

Intelligent Fraud Detection in E-Commerce Platforms Using Behavioral Analytics

Ruchi Pandey, Kathiravan Ravichandran

(Pages:36)

Chapter 14

IoT-Enabled Fraud Detection in Smart Retail and Supply Chain Management Systems

Anmol Narayan, Kanchan Kamlesh Ingale

(Pages:31)

Chapter 15

Fraud Detection in Insurance Claim Processing Using Predictive Analytics and AI Models

Neha Nivrutti Jamdar, A. Devi

(Pages:38)

Chapter 16

Cybersecurity and Fraud Prevention in Online Payment Gateways Using Machine Learning Techniques

V. Balaraju, R. Janarthanan

(Pages:39)

Chapter 17

Real-Time Fraud Detection in Mobile and UPI-Based Payment Systems Using AI

V. Balaraju, M. Vidya

(Pages:39)

Chapter 18

Healthcare Fraud Detection Using Data Mining and Machine Learning Techniques

Krutika Balram Kakpure, M. Chiranjivi

(Pages:35)

Chapter 19

Social Media Fraud and Fake Profile Detection Using AI and Behavioral Analysis

Shubhangi Abhijit Solanke, Saraswathi Natarajan

(Pages:32)

Chapter 20

Fraud Detection in Smart Cities Using IoT, AI, and Big Data Analytics

K. Suresh, M. Keerthi Priya

(Pages:38)


Contributions


Avinash Reddy Aitha is a highly accomplished Principal QA Engineer, researcher, and innovator with over nine years of experience driving digital transformation, automation, and AI powered solutions across insurance, hospitality, broadcasting, and telecom domains. His career reflects a unique blend of technical depth, leadership, and visionary research, making him a recognized contributor to the fields of Generative AI, Agentic AI, Deep Learning, and Cloud Native DevOps. Avinash has spent the last several years pioneering AI driven digital transformation within the insurance sector, delivering innovative frameworks that enhance operational efficiency, customer experience, and risk management. At State Compensation Insurance Fund, he has led initiatives focused on workers’ compensation claims automation, fraud detection, and intelligent premium modelling, leveraging cutting edge technologies such as Generative AI, Agentic AI, Deep Learning, and Multi Agent Systems. His contributions include architecting cloud native

Dr. K. Prabha is an accomplished Professor of Computer Science with over 17 years of teaching and research experience. She currently serves at the Periyar University PG Extension and Research Centre, Dharmapuri. She holds a Ph.D. in Computer Science and specializes in Data Mining, Artificial Intelligence, and Wireless Communication. Dr. Prabha has published numerous research papers in SCI (18), Scopus (26), and UGC Care (12) journals. She is also a reviewer for reputed international journals including Elsevier and Springer publications. Her research interests include Big Data Analytics, Image Processing, and Social Network Analysis. She has authored multiple academic books (04) and holds an Indian patent in image processing applications. She is a recipient of the Best Faculty Award and actively contributes as a resource person in 26 national and international academic events.

Dr. L. Vijayakumar is an accomplished academician currently serving as an Associate Professor in the Department of Commerce at Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Avadi. With extensive experience in teaching and research, he has actively contributed to the academic community by participating in more than 21 national and international conferences. His research work has been widely recognized, with numerous papers published in international conferences held in Malaysia and Thailand, as well as in reputed journals with strong impact factors.

In addition to his academic achievements, Dr. Vijayakumar is a prolific author and has received several awards from various institutions in recognition of his contributions to education and research. Beyond academia, he is also socially committed and runs a trust in Pondicherry, through which he engages in catering services and other initiatives aimed at serving society across multiple domains.

S. Gokul is an Assistant Professor in the Department of Computer Science and Engineering (Internet of Things) at KSR College of Engineering, Tamil Nadu, India. He holds a postgraduate degree in Computer Science and Engineering and is actively engaged in teaching and academic mentoring. His areas of interest include Internet of Things (IoT), Cloud Computing, Software Engineering, and emerging computing technologies. He is involved in academic research, curriculum development, and continuous professional development through workshops and faculty development programs.



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