Corporate Credit Ratings Based on Hierarchical Heterogeneous Graph Neural Networks

In order to help investors understand the credit status of target corporations and reduce investment risks, the corporate credit rating model has become an important evaluation tool in the financial market. These models are based on statistical learning, machine learning and deep learning especially graph neural networks (GNNs). However, we found that only few models take the hierarchy, heterogeneity or unlabeled data into account in the actual corporate credit rating process. Therefore, we propose a novel framework named hierarchical heterogeneous graph neural networks (HHGNN), which can fully model the hierarchy of corporate features and the heterogeneity of relationships between corporations. In addition, we design an adversarial learning block to make full use of the rich unlabeled samples in the financial data. Extensive experiments conducted on the public-listed corporate rating dataset prove that HHGNN achieves SOTA compared to the baseline methods.