Titlebook: Anti-Fraud Engineering for Digital Finance; Behavioral Modeling Cheng Wang Book 2023 Tongji University Press 2023 Learning Automata.Fraud

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Einführung in die Kostenrechnung.pert knowledge is required), and newer works (such as HGT) abandon meta-path and use meta-relation instead, and set up multiple sets of projections The type of matrix modeling edge. From the perspective of dynamics, the past methods mainly used the sequential combination of GNN+RNN (such as TGCN), b

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Overview of Digital Finance Anti-fraud,ent, the applications of digital financial technology have significantly reduced the information asymmetry in the financial field and made great contributions to improving the financial market. However, everything has two sides, especially new things. Digital financial technology is on the ascendant

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Explicable Integration Techniques: Relative Temporal Position Taxonomy,etection performance by overcoming the inability of single-function methods to cope with complex and varied frauds. However, a qualified integration is really inaccessible under multiple demanding requirements, i.e., improving detection performance, ensuring decision explainability, and limiting pro

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Multidimensional Behavior Fusion: Joint Probabilistic Generative Modeling, theft detection. We concentrate on this issue in online social networks (OSNs) where users usually have composite behavioral records, consisting of multi-dimensional low-quality data, e.g., offline check-ins and online user generated content (UGC). As an insightful result, we validate that there is

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Knowledge Oriented Strategies: Dedicated Rule Engine,iction of online credit loan services (OCLSs) is such a typical scenario. But it has another rather critical challenge, i.e., the scarcity of data labels. Fortunately, GNNs can also cope with this problem due to their good ability of semi-supervised learning by mining structure and feature informati

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Associations Dynamic Evolution: Evolving Graph Transformer,hallenging that such predictions need to detect evolving and increasingly impalpable fraud patterns. The technical difficulty mainly stems from one factor: evolution of fraud patterns. As a widely recognized method currently, GNNs has attracted much attention from researchers. According to the requi