Multi-Relational Graph Representation Learning for Financial Statement Fraud Detection

Abstract

Financial statement fraud refers to malicious manipulations of financial data in listed companies’ annual statements. Traditional machine learning approaches focus on individual companies, overlooking the interactive relationships among companies that are crucial for identifying fraud patterns. Moreover, fraud detection is a typical imbalanced binary classification task with normal samples outnumbering fraud ones. In 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 companies. We then develop a multi-relational graph convolutional network to aggregate information within each relationship and employ an attention mechanism to fuse information across multiple relationships. The attention mechanism enables the model to distinguish the importance of different relationships, thereby aggregating more useful information from key relationships. To alleviate the class imbalance problem, we present a diffusion-based under-sampling strategy that strategically selects key nodes globally for model training. We also employ focal loss to assign greater weights to harder-to-classify minority samples. We build a real-world dataset from the annual financial statement of listed companies in China. The 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 method. The dataset and codes are publicly available at: https://github.com/XNetLab/MRG-for-Finance.

Keywords

• financial statement fraud
• class imbalance
• graph neural networks (gnn)
• multi-relational graphs