It is a critical mission for financial service providers to discover fraudulent borrowers in a supply chain. The borrowers' transactions in an ongoing business are inspected to support the providers' decision ...
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It is a critical mission for financial service providers to discover fraudulent borrowers in a supply chain. The borrowers' transactions in an ongoing business are inspected to support the providers' decision on whether to lend the money. Considering multiple participants in a supply chain business, the borrowers may use sophisticated tricks to cheat, making fraud detection challenging. In this work, we propose a multitask learning framework, MultiFraud, for complex fraud detection with reasonable explanation. The heterogeneous information from multi-view around the entities is leveraged in the detection framework based on heterogeneous graph neural networks. MultiFraud enables multiple domains to share embeddings and enhance modeling capabilities for fraud detection. The developed explainer provides comprehensive explanations across multiple graphs. Experimental results on five datasets demonstrate the framework's effectiveness in fraud detection and explanation across domains.
graph Neural Networks (GNNs) have shown remarkable results in graph-related tasks, yet interpreting their decision-making process remains challenging. Most existing methods for interpreting GNNs focus on finding a sub...
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