# Corporate Credit Rating: A Survey

arXiv: https://arxiv.org/abs/2309.14349 · HTML: https://mlsanity.com/paper/2309.14349/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 49 references that are in the map.
- First posted on arXiv 19 Sep 2023.

## Builds on (most cited first, 11 of 11)

- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Distributed Representations of Sentences and Documents](https://mlsanity.com/paper/1405.4053/) (2014)
- [Model Agnostic Supervised Local Explanations](https://mlsanity.com/paper/1807.02910/) (2018)
- [Classification-based Financial Markets Prediction using Deep Neural Networks](https://mlsanity.com/paper/1603.08604/) (2016)
- [Application of Deep Neural Networks to assess corporate Credit Rating](https://mlsanity.com/paper/2003.02334/) (2020)
- [Every Corporation Owns Its Structure: Corporate Credit Ratings via Graph Neural Networks](https://mlsanity.com/paper/2012.01933/) (2020)
- [Every Corporation Owns Its Image: Corporate Credit Ratings via Convolutional Neural Networks](https://mlsanity.com/paper/2012.03744/) (2020)
- [Contrastive Pre-training for Imbalanced Corporate Credit Ratings](https://mlsanity.com/paper/2102.12580/) (2021)
- [Adversarial Semi-supervised Learning for Corporate Credit Ratings](https://mlsanity.com/paper/2104.02479/) (2021)
- [Convolutional Feature Extraction and Neural Arithmetic Logic Units for Stock Prediction](https://mlsanity.com/paper/1905.07581/) (2019)
- [A Sparsity Algorithm with Applications to Corporate Credit Rating](https://mlsanity.com/paper/2107.10306/) (2021)

## Abstract (arXiv)

Corporate credit rating (CCR) plays a very important role in the process of contemporary economic and social development. How to use credit rating methods for enterprises has always been a problem worthy of discussion. Through reading and studying the relevant literature at home and abroad, this paper makes a systematic survey of CCR. This paper combs the context of the development of CCR methods from the three levels: statistical models, machine learning models and neural network models, summarizes the common databases of CCR, and deeply compares the advantages and disadvantages of the models. Finally, this paper summarizes the problems existing in the current research and prospects the future of CCR. Compared with the existing review of CCR, this paper expounds and analyzes the progress of neural network model in this field in recent years.