Neural Network-Based Deep Learning for Online Payment Fraud Detection

Overview

This book explores deep learning as a next-generation approach to online payment fraud detection in the face of increasingly complex and adaptive threats. Traditional rule-based or shallow learning methods are no longer sufficient.

Content

Through ten focused chapters, this book tackles challenges such as behavioral modeling, spatiotemporal anomaly detection, class imbalance, behavior drift, and graph-based inference. It applies advanced neural architectures including LSTM, GRU, GANs, GNNs, and spatiotemporal transformers.

Structure and Application

With a problem-driven structure, each chapter links real-world fraud problems to tailored neural solutions, validated on large-scale transaction data. This book blends theory, practical design, and empirical rigor, offering researchers and practitioners a foundation for scalable, adaptive, and reliable fraud detection systems.