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

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.