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{ "item_title" : "Neural Network-Based Deep Learning for Online Payment Fraud Detection", "item_author" : [" Yu Xie", "Yue Tian", "Jiamin Yao "], "item_description" : "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. 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. 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.", "item_img_path" : "https://covers1.booksamillion.com/covers/bam/9/81/958/512/9819585120_b.jpg", "price_data" : { "retail_price" : "129.99", "online_price" : "129.99", "our_price" : "129.99", "club_price" : "129.99", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : "" } }
Neural Network-Based Deep Learning for Online Payment Fraud Detection|Yu Xie

Neural Network-Based Deep Learning for Online Payment Fraud Detection

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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. 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. 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.

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Details

  • ISBN-13: 9789819585120
  • ISBN-10: 9819585120
  • Publisher: Springer
  • Publish Date: June 2026
  • Page Count: 185

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