Corporate Credit Rating: A Survey

TL;DR

This survey reviews the development of corporate credit rating methods, covering statistical, machine learning, and neural network models, highlighting recent advances and future research directions.

Contribution

It provides a systematic overview of CCR methods, compares their advantages and disadvantages, and emphasizes recent progress in neural network applications.

Findings

Neural network models have shown significant recent progress in CCR.

Traditional statistical models are still widely used but have limitations.

The paper identifies current challenges and future research directions in CCR.

Abstract

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.