Book

With the rapid expansion of digital commerce and electronic financial transactions, fraudulent activities have grown increasingly sophisticated, causing significant financial damage worldwide. This book presents a comprehensive comparative analysis of modern machine learning techniques applied to credit card fraud detection. Focusing on overcoming real-world obstacles—such as extreme class imbalance and real-time inference requirements—the work evaluates and compares key predictive models, feature engineering strategies, and algorithmic performance metrics. Designed as a practical and academic reference, this volume provides clear insights into selecting, implementing, and optimizing intelligent fraud mitigation systems in contemporary financial environments.