Anti-Fraud engineering for digital finance: behavioral modeling paradigm
Gespeichert in:
| 1. Verfasser: | |
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| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Singapore
Springer Nature Singapore
2023
Singapore Springer |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis Klappentext |
| Beschreibung: | VIII, 207 Seiten Illustrationen, Diagramme |
| ISBN: | 9789819952564 |
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Datensatz im Suchindex
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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 . 1 1 1 4 4 6 9 2 Vertical Association Modeling: Latent Interaction Modeling. 11 1 Overview of Digital Finance Anti-fraud . 2.1 3 Introduction to Vertical Association Modeling in Online Services. 11 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. 15 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 . 15 28 39 39 39 39 Horizontal Association Modeling: Deep Relation Modeling. 43 Introduction to Horizontal Association Modeling in Online Services. 43 3.1.1 Behavior Prediction . 3.1.2 Behavior Sequence Analysis. 44 45 3.1 13 13 14
Related Work . 3.2.1 Fraud Prediction by Account Risk Evaluation . 3.2.2 Fraud Detection by Optimizing Window-Based Features . 48 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 . 62 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 . 3.2 4 5 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 . 46 47 49 49 53 59 62 70 81 81 82 82 83 87 87 89 89 90 90 91 93 94 99 100 100 101 103 106 108 108 109 110 110 Multidimensional Behavior Fusion: Joint Probabilistic Generative Modeling . 113 5.1 Online Identity Theft Detection Based on Multidimensional Behavioral Records . 113 5.2 Overview of the Solution . 116
Contents Vil 5 3 Identity Theft Detection Solutions inOnline 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 . 118 118 121 121 121 123 131 134 136 137 6 Knowledge Oriented Strategies: Dedicated Rule Engine. 6.1 Online Anti-fraud Strategy Based on Semi-supervised 139 Learning . 139 6.2 Development and Present State . 6.2.1 Anti-fraud in Online Services . 6.2.2 Graph Neural Networks. 6.2.3 Weak Supervision. 6.3 Risk Prediction Measures in Online Lending Services
. 6.3.1 Preliminary . 6.3.2 Graph-Oriented Snorkel. 6.3.3 Heterogeneous Graph Neural Network . 6.3.4 Loss Function . 6.4 Risk Assessmentand Analysis . 6.4.1 Datasets and Evaluation Metrics. 6.4.2 Baseline Methods . 6.4.3 Implementation Details . 6.4.4 Performance Comparison . 6.4.5 Ablation Study . 6.4.6 Parameter Sensitivity . 6.5 Conclusion . References . 7 Enhancing Association Utility: Dedicated Knowledge Graph . 7.1 Gang Fraud Prediction System Based on Knowledge Graph . 7.2 Related Work . 7.3 Recovering-Mining-Clustering-Predicting Framework . 7.3.1 Recovering Missing Associations . 7.3.2 Mining Underlying Associations .
7.3.3 Clustering and Predicting . 7.4 Experimental Evaluation . 7.4.1 Dataset Description and Experiment Settings. 7.4.2 On Model Comparison. 141 141 142 142 143 143 144 148 152 152 152 154 155 155 157 159 160 160 163 163 165 167 167 172 174 177 177 178
7.4.3 On Address Disambiguation . 7.4.4 On Network Embedding . 7.5 Conclusion . References . 8 180 183 186 186 189 Dynamic Fraud Detection Solution Based on Graph Transformer . 189 Related Work . 192 Fraud Detectionin Online Lending Services. 193 Associations Dynamic Evolution: Evolving Graph Transformer. 8.1 8.2 8.3 Preliminary . 193 8.3.2 Graph Transformer . 8.3.3 Evolving Graph Transformer . 8.4 Experimental Evaluation . 8.4.1 Datasets and Metrics . 8.4.2 Baseline Methods . 8.4.3 Implementation Details . 8.4.4 Results for Node Classification. 8.4.5 Results for Edge
Classification. 8.4.6 Ablation Study . 8.4.7 Parameter Sensitivity . 8.5 Conclusion . References . 194 197 198 198 201 201 201 203 204 205 205 206 8.3.1
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 tor professionals. |
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| illustrated | Illustrated |
| index_date | 2024-07-03T23:27:18Z |
| indexdate | 2024-07-10T10:09:58Z |
| institution | BVB |
| isbn | 9789819952564 |
| language | English |
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| spelling | Wang, Cheng Verfasser (DE-588)1315814692 aut Anti-Fraud engineering for digital finance behavioral modeling paradigm Cheng Wang Singapore Springer Nature Singapore 2023 Singapore Springer VIII, 207 Seiten Illustrationen, Diagramme txt rdacontent n rdamedia nc rdacarrier Computer Application in Social and Behavioral Sciences Behavioral Finance Social sciences / Data processing Economics / Psychological aspects Verhaltensökonomie (DE-588)7751254-6 gnd rswk-swf Digitalisierung (DE-588)4123065-6 gnd rswk-swf Vermögensdelikt (DE-588)4129990-5 gnd rswk-swf Verhaltensökonomie (DE-588)7751254-6 s Vermögensdelikt (DE-588)4129990-5 s Digitalisierung (DE-588)4123065-6 s DE-604 Erscheint auch als Online-Ausgabe 978-981-9952-57-1 Digitalisierung UB Augsburg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=034878888&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis Digitalisierung UB Augsburg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=034878888&sequence=000003&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA Klappentext |
| spellingShingle | Wang, Cheng Anti-Fraud engineering for digital finance behavioral modeling paradigm Computer Application in Social and Behavioral Sciences Behavioral Finance Social sciences / Data processing Economics / Psychological aspects Verhaltensökonomie (DE-588)7751254-6 gnd Digitalisierung (DE-588)4123065-6 gnd Vermögensdelikt (DE-588)4129990-5 gnd |
| subject_GND | (DE-588)7751254-6 (DE-588)4123065-6 (DE-588)4129990-5 |
| title | Anti-Fraud engineering for digital finance behavioral modeling paradigm |
| title_auth | Anti-Fraud engineering for digital finance behavioral modeling paradigm |
| title_exact_search | Anti-Fraud engineering for digital finance behavioral modeling paradigm |
| title_exact_search_txtP | Anti-Fraud engineering for digital finance behavioral modeling paradigm |
| title_full | Anti-Fraud engineering for digital finance behavioral modeling paradigm Cheng Wang |
| title_fullStr | Anti-Fraud engineering for digital finance behavioral modeling paradigm Cheng Wang |
| title_full_unstemmed | Anti-Fraud engineering for digital finance behavioral modeling paradigm Cheng Wang |
| title_short | Anti-Fraud engineering for digital finance |
| title_sort | anti fraud engineering for digital finance behavioral modeling paradigm |
| title_sub | behavioral modeling paradigm |
| topic | Computer Application in Social and Behavioral Sciences Behavioral Finance Social sciences / Data processing Economics / Psychological aspects Verhaltensökonomie (DE-588)7751254-6 gnd Digitalisierung (DE-588)4123065-6 gnd Vermögensdelikt (DE-588)4129990-5 gnd |
| topic_facet | Computer Application in Social and Behavioral Sciences Behavioral Finance Social sciences / Data processing Economics / Psychological aspects Verhaltensökonomie Digitalisierung Vermögensdelikt |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=034878888&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=034878888&sequence=000003&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA |
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