Neural network-based deep learning for online payment fraud detection /
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 b...
| Main Authors: | , , , |
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| Format: | eBook |
| Language: | English |
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Singapore : [Shanghai] :
Springer ; Tongji University Press,
[2026]
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| Subjects: |
| Summary: | 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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| Physical Description: | 1 online resource (xii, 185 pages) : illustrations (chiefly color) |
| Bibliography: | Includes bibliographical references. |
| ISBN: | 9789819585137 (electronic bk.) 9819585139 |