Anti Fraud Engineering for Digital Finance: Behavioral Modeling Paradigm - Rilegato

Wang, Cheng

Rilegato

ISBN 10: 9819952565 ISBN 13: 9789819952564

Casa editrice: Springer-Nature New York Inc, 2023

Riassunto

This book offers an introduction to the topic of anti-fraud in digital finance based on the behavioral modeling paradigm. It deals with the insufficiency and low-quality of behavior data and presents a unified perspective to combine technology, scenarios, and data for better anti-fraud performance. The goal of this book is to provide a non-intrusive second security line, rather than replaced with existing solutions, for anti-fraud in digital finance. By studying common weaknesses in typical fields, it can support the behavioral modeling paradigm across a wide array of applications. It covers the latest theoretical and experimental progress and offers important information that is just as relevant for researchers as for professionals.

Su questo libro

Informazioni sull'autore

Cheng Wang received the M.S. degree from the Department of Applied Mathematics, Tongji University, in 2006 and the Ph.D. degree from the Department of Computer Science, Tongji University, in 2011. He is currently Professor with the Department of Computer Science, Tongji University. His research interests include cyberspace security and intelligent information services.

Dalla quarta di copertina

This book offers an introduction to the topic of anti-fraud in digital finance based on the behavioral modeling paradigm. It deals with the insufficiency and low-quality of behavior data and presents a unified perspective to combine technology, scenarios, and data for better anti-fraud performance. The goal of this book is to provide a non-intrusive second security line, rather than replaced with existing solutions, for anti-fraud in digital finance. By studying common weaknesses in typical fields, it can support the behavioral modeling paradigm across a wide array of applications. It covers the latest theoretical and experimental progress and offers important information that is just as relevant for researchers as for professionals.

Dettagli dell'edizione

• Editore: Springer-Nature New York Inc
• Data di pubblicazione: 2023
• Lingua: Inglese
• ISBN 10: 9819952565
• ISBN 13: 9789819952564
• Rilegatura: Copertina rigida
• Numero edizione: 1
• Numero di pagine: 216
• Contatto del produttore: Springer Nature Customer Service Center GmbH; ProductSafety@springernature.com; ; Europaplatz 3; Heidelberg; 69115; Germania