计算机与现代化 ›› 2024, Vol. 0 ›› Issue (05): 27-32.doi: 10.3969/j.issn.1006-2475.2024.05.006

• 人工智能 • 上一篇    下一篇

基于图神经网络的多层银企网络融合研究

  

  1. (南京航空航天大学经济与管理学院,江苏 南京 211106)
  • 出版日期:2024-05-29 发布日期:2024-06-12
  • 作者简介:作者简介:李珊(1977—),女,江苏南京人,副教授,博士,研究方向:商务智能,金融风险,E-mail: 15605182960@163.com; 王林娜(1999—),女,江苏常州人,硕士研究生,研究方向:数据挖掘,复杂系统建模,E-mail: lnwang@nuaa.edu.cn。
  • 基金资助:
    国家社会科学基金资助项目(17BGL055); 中央高校基本科研业务费专项资金资助项目(ND2023005); 南京航空航天大学科研与实践创新计划项目(xcxjh20220904)
        

Multi-layer Bank-enterprise Converged Network Based on Graph Neural Network

  1. (College of Economics and Management, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China)

  • Online:2024-05-29 Published:2024-06-12

摘要: 摘要:针对金融行业内潜在系统性风险难以精准识别问题,基于直接系统性风险传染渠道的借贷数据以及间接渠道的互联网文本信息,构建多层银企网络,并利用图卷积神经网络(GCN)设计多层银企网络融合模型,根据融合网络量化评估29家银行和75家房地产机构的不同渠道系统性风险传染过程。实验结果表明,在多层金融网络融合任务上,本文融合模型的准确率达到0.8559,优于对比模型。融合网络分析表明,多层网络共同冲击下的银企系统性风险传染能力明显大于单一或者2层网络的系统性风险,且基于间接渠道的企业间网络系统性风险更明显。金融审慎监管应该更多关注文本数据、深度学习等技术对于整合庞大金融资源的能力和有效提高风险监测预警的能力。






关键词: 关键词:多层网络融合, 系统性风险传染, 图卷积神经网络, 文本分析

Abstract: Abstract: The potential systemic risk in the financial industry is difficult to be accurately identified. Based on the loan data of the direct systemic risk contagion channel and internet text information of the indirect channel, a multi-layer bank-enterprise network is constructed, and a multi-layer bank-enterprise network convergence model is designed by using graph convolutional neural networks (GCN). Based on the converged network, this paper quantitatively evaluates the systemic risk contagion process of 29 banks and 75 real estate institutions. The converged network analysis shows that the systemic risk transmission capacity under the joint impact of multi-layer bank-enterprise network is significantly greater than the systemic risk of single or two-layer network, and the systemic risk of the inter-enterprise network based on the indirect channel is more obvious. Financial prudential supervision should pay more attention to the ability of data analysis, deep learning and other technologies to integrate big data financial resources and effectively improve the level of risk monitoring and warning.

Key words: Key words: convergence of multi-layer network, systemic risk contagion, graph convolutional neural network, text analysis

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