Heterogeneous Graph Neural Networks for Fraud Detection and Explanation in Supply Chain Finance

Bin Wu, Kuo-Ming Chao, Yinsheng Li

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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.
Original languageEnglish
Journalinformation systems
Publication statusPublished - 10 Dec 2023

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