Advanced Credit Card Fraud Detection with Hybrid Optimization and Deep Learning

In today's fast-paced digital economy credit card fraud poses one of the greatest challenges to financial security. Advanced Credit Card Fraud Detection with Hybrid Optimization and Deep Learning bridges the gap between cutting-edge research and practical implementation offering a comprehensive guide for students researchers and professionals. This book explores the integration of hybrid optimization techniques with deep recurrent neural networks (RNNs) to create high-performance fraud detection systems. Through clear explanations mathematical foundations and real-world case studies it demonstrates how combining feature selection model tuning and sequential deep learning can dramatically improve detection accuracy while reducing false alarms. Readers will learn:The fundamentals of credit card fraud patterns and detection challengesHybrid optimization algorithms for feature engineering and model enhancementDeep RNN architectures including LSTM and GRU for sequential data analysisEnd-to-end implementation strategies using real datasetsPerformance evaluation and deployment in real-world financial systemsWhether you are an academic exploring AI in finance a data scientist building detection models or a security professional safeguarding transactions this book provides the tools and knowledge to stay ahead in the evolving battle against financial fraud.