Management Review

Management Review ›› 2025, Vol. 37 ›› Issue (10): 197-209.

• Accounting and Financial Management • Previous Articles    

A Method Based on Knowledge Graph to Detect Listed Companies’ Fraud in Financial Statement

Yang Yuemin1, Wang Chao2, Chen Haozhi3, Zhang Weiguo4   

  1. 1. School of Mechanical & Automotive Engineering, South China University of Technology, Guangzhou 510640;
    2. School of Economics, Guangdong University of Technology, Guangzhou 510520;
    3. School of Business Administration, South China University of Technology, Guangzhou 510641;
    4. School of Management, Shenzhen University, Shenzhen 518057
  • Received:2023-12-25 Published:2025-11-18

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Abstract: Financial statement fraud of listed companies is seriously threatening the stability of the global economy. Existing studies rarely consider the relationship between listed companies and other financial entities. It remains to be explored whether these relationships can enhance the accuracy of fraud detection. This paper proposes a detection method considering the relationship between listed companies. This method constructs a knowledge graph to depict common management, concerted action, and common related party relationships. Through knowledge reasoning, a homogeneous network is extracted. Integrating the features from the homogeneous network with the company’s own characteristics, a fraud detection model is constructed using the Light Gradient Boosting Machine (LightGBM). This paper conducts an empirical test based on the real data of China’s A-share listed companies from 2016 to 2020. The empirical results indicate that the network features related to interrelationships contribute to improved accuracy in financial statement fraud detection. In comparison with other machine learning methods, LightGBM methods considering the relationships exhibits better performance.

Key words: financial statement fraud, knowledge graph, machine learning, network centrality