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multi-relational Graph Representation Learning for Financial Statement Fraud Detection

Big Data Mining and Analytics 2024年第3期7卷 920-941页

作者： Chenxu Wang Mengqin Wang Xiaoguang Wang Luyue Zhang Yi Long School of Software Engineering and also with MoE Key Lab of Intelligent Networks and Network SecurityXi’an Jiaotong UniversityXi’an 710049China School of Software Engineering Xi’an Jiaotong UniversityXi’an 710049China Shenzhen Finance Institute The Chinese University of Hong KongShenzhen(CUHK-Shenzhen)Shenzhen 518026China

Financial statement fraud refers to malicious manipulations of financial data in listed companies'annual *** machine learning approaches focus on individual companies,overlooking the interactive relationships among companies that are crucial for identifying fraud ***,fraud detection is a typical imbalanced binary classification task with normal samples outnumbering fraud *** this paper,we propose a multi-relational graph convolutional network,named FraudGCN,for detecting financial statement fraud.A multi-relational graph is constructed to integrate industrial,supply chain,and accounting-sharing relationships,effectively encapsulating the multidimensional and complex interactions among *** then develop a multi-relational graph convolutional network to aggregate information within each relationship and employ an attention mechanism to fuse information across multiple *** attention mechanism enables the model to distinguish the importance of different relationships,thereby aggregating more useful information from key *** alleviate the class imbalance problem,we present a diffusion-based under-sampling strategy that strategically selects key nodes globally for model *** also employ focal loss to assign greater weights to harder-to-classify minority *** build a real-world dataset from the annual financial statement of listed companies in *** experimental results show that FraudGCN achieves an improvement of 3.15%in Macro-recall,3.36%in Macro-F1,and 3.86%in GMean compared to the second-best *** dataset and codes are publicly available at:https://***/XNetLab/MRG-for-Finance.

关键词： financial statement fraud class imbalance Graph Neural Networks(GNN) multi-relational graphs

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