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    面向企业信用风险评估的多视角异质图神经网络方法

    Multi-View Heterogeneous Graph Neural Network Method for Enterprise Credit Risk Assessment

    • 摘要: 企业信用风险评估是一个重要且具有挑战的问题. 由于金融市场中存在大量的异质关联关系,使得异质图神经网络天然适合建模企业信用风险. 然而,现有大部分研究不能充分捕捉到复杂金融网络中企业的综合信用风险. 针对此问题,提出了一个面向企业信用风险评估的多视角异质图神经网络方法——CRGNN. 该方法包含自身风险编码器以及传染风险编码器,其中自身风险编码器建模基于企业特征信息的自身风险,传染风险编码器由新提出的分层异质图Transformer网络和分层异质图特征注意力网络2个子模块组成. 这2个模块分别挖掘基于企业不同邻居视角的传染风险和基于不同特征维度视角的传染风险. 为了充分利用异质关系信息,2个模块都采用了分层机制. 在企业破产预测数据集SMEsD和企业信用评估数据集ECAD上进行了大量的实验,AUC指标相比最优基线模型分别提高了3.98个百分点和3.47个百分点.

       

      Abstract: Credit risk assessment for enterprises is a critical issue, significantly impacting investor decisions. It also plays a crucial role in enabling government warnings and the handling of financial risks in a timely manner. Given the numerous heterogeneous relationships in the financial market, graph neural networks are naturally suitable for modeling enterprise credit risk. However, most existing research primarily focuses on modeling either the intra-risk of enterprises based on their financial information or the inter-enterprise contagion risk using simulation methods. Therefore, these approaches fail to fully capture the comprehensive credit risk of enterprises in complex financial networks. To address this limitation in current research, we propose a multi-perspective heterogeneous graph neural network method CRGNN for enterprise credit risk assessment. This method includes an enterprise intra-risk encoder and an enterprise contagion risk encoder, where the enterprise intra-risk encoder models the intra-risk based on enterprise feature information, and the enterprise contagion risk encoder consists of two sub-modules: a hierarchical heterogeneous graph Transformer network and a hierarchical heterogeneous graph feature attention network newly proposed in this paper. These two modules respectively explore contagion risks from the views of different neighbors and different feature dimensions. To fully utilize heterogeneous relationship information, both modules employ hierarchical mechanisms. With these designs, the model proposed in this study can adequately capture the comprehensive credit risk faced by enterprises. Extensive experiments are conducted on the SMEsD bankruptcy prediction dataset and the ECAD enterprise credit assessment dataset, resulting in an improvement of 3.98% and 3.47% in AUC compared with the best baseline model, respectively.

       

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