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.
Table of Content
Overview of Digital Finance Anti Fraud Vertical Association Modeling: Latent Interaction Modeling.- Horizontal Association Modeling: Deep Relation Modeling.- Explicable Integration Techniques: Relative Temporal Position Taxonomy.- Multidimensional Behavior Fusion: Joint Probabilistic Generative Modeling.- Knowledge Oriented Strategies: Dedicated Rule Engine.- Enhancing Association Utility: Dedicated Knowledge Graph.- Associations Dynamic Evolution: Evolving Graph Transformer.
About the author
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.
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