# Every Corporation Owns Its Structure: Corporate Credit Ratings via Graph Neural Networks

arXiv: https://arxiv.org/abs/2012.01933 · HTML: https://mlsanity.com/paper/2012.01933/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Nov 2020.

## Built on by (most cited first, 6 of 6)

- [Company-as-Tribe: Company Financial Risk Assessment on Tribe-Style Graph with Hierarchical Graph Neural Networks](https://mlsanity.com/paper/2301.13492/) (2023)
- [Predicting the Silent Majority on Graphs: Knowledge Transferable Graph Neural Network](https://mlsanity.com/paper/2302.00873/) (2023)
- [Contrastive Pre-training for Imbalanced Corporate Credit Ratings](https://mlsanity.com/paper/2102.12580/) (2021)
- [Combining Intra-Risk and Contagion Risk for Enterprise Bankruptcy Prediction Using Graph Neural Networks](https://mlsanity.com/paper/2202.03874/) (2022)
- [Adversarial Semi-supervised Learning for Corporate Credit Ratings](https://mlsanity.com/paper/2104.02479/) (2021)
- [Corporate Credit Rating: A Survey](https://mlsanity.com/paper/2309.14349/) (2023)

## Abstract (arXiv)

Credit rating is an analysis of the credit risks associated with a corporation, which reflects the level of the riskiness and reliability in investing, and plays a vital role in financial risk. There have emerged many studies that implement machine learning and deep learning techniques which are based on vector space to deal with corporate credit rating. Recently, considering the relations among enterprises such as loan guarantee network, some graph-based models are applied in this field with the advent of graph neural networks. But these existing models build networks between corporations without taking the internal feature interactions into account. In this paper, to overcome such problems, we propose a novel model, Corporate Credit Rating via Graph Neural Networks, CCR-GNN for brevity. We firstly construct individual graphs for each corporation based on self-outer product and then use GNN to model the feature interaction explicitly, which includes both local and global information. Extensive experiments conducted on the Chinese public-listed corporate rating dataset, prove that CCR-GNN outperforms the state-of-the-art methods consistently.