Intro
Contents
1 Overview of Digital Finance Anti-fraud
1.1 Situation of Anti-fraud Engineering
1.2 Challenge of Anti-fraud Engineering
1.3 Strategies of Anti-fraud Engineering
1.4 Typical Application in Financial Scenarios
1.5 Outline of This Book
References
2 Vertical Association Modeling: Latent Interaction Modeling
2.1 Introduction to Vertical Association Modeling in Online Services
2.2 Related Work
2.2.1 Composite Behavioral Modeling
2.2.2 Customized Data Enhancement
2.3 Fine-Grained Co-occurrences for Behavior-Based Fraud Detection

2.3.1 Fraud Detection System Based in Online Payment Services
2.3.2 Experimental Evaluation
2.4 Conclusion
2.4.1 Behavior Enhancement
2.4.2 Future Work
References
3 Horizontal Association Modeling: Deep Relation Modeling
3.1 Introduction to Horizontal Association Modeling in Online Services
3.1.1 Behavior Prediction
3.1.2 Behavior Sequence Analysis
3.2 Related Work
3.2.1 Fraud Prediction by Account Risk Evaluation
3.2.2 Fraud Detection by Optimizing Window-Based Features
3.3 Historical Transaction Sequence for High-Risk Behavior Alert

3.3.1 Fraud Prediction System Based on Behavior Prediction
3.3.2 Experimental Evaluation
3.3.3 Enhanced Anti-fraud Scheme
3.4 Learning Automatic Windows for Sequence-Form Fraud Pattern
3.4.1 Fraud Detection System based on Behavior Sequence Analysis
3.4.2 Experimental Evaluation
3.5 Conclusion
3.5.1 Behavior Prediction
3.5.2 Behavior Analysis
3.5.3 Future Work
References
4 Explicable Integration Techniques: Relative Temporal Position Taxonomy
4.1 Concepts and Challenges
4.2 Main Technical Means of Anti-fraud Integration System

4.2.1 Anti-fraud Function Divisions
4.2.2 Module Integration Schemes
4.2.3 Explanation Methods
4.3 System Integration Architecture
4.3.1 Anti-fraud Function Modules
4.3.2 Center Control Module
4.3.3 Communication Architecture
4.4 Performance Analysis
4.4.1 Experimental Set-Up
4.4.2 Implementation
4.4.3 Evaluation of System Performance
4.4.4 Exemplification of CAeSaR's Advantages
4.5 Discussion
4.5.1 Faithful Explanation
4.5.2 Online Learning
4.6 Conclusion
References
5 Multidimensional Behavior Fusion: Joint Probabilistic Generative Modeling

5.1 Online Identity Theft Detection Based on Multidimensional Behavioral Records
5.2 Overview of the Solution
5.3 Identity Theft Detection Solutions in Online Social Networks
5.3.1 Composite Behavioral Model
5.3.2 Identity Theft Detection Scheme
5.4 Evaluation and Analysis
5.4.1 Datasets
5.4.2 Experiment Settings
5.4.3 Performance Comparison
5.5 Literature Review
5.6 Conclusion
References
6 Knowledge Oriented Strategies: Dedicated Rule Engine
6.1 Online Anti-fraud Strategy Based on Semi-supervised Learning
6.2 Development and Present State