Generative AI for Fraud Detection: From Theory to Enterprise Deployment (en Inglés) - Amit Kumar Tyagi

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.