Corporate Credit Rating: A Survey
It offers a comprehensive overview for researchers and practitioners in finance, but is incremental as it builds on existing reviews by including recent neural network progress.
This paper provides a systematic survey of corporate credit rating (CCR) methods, covering statistical, machine learning, and neural network models, and compares their advantages and disadvantages while summarizing current research problems and future prospects.
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.