Application of Machine Learning to a Credit Rating Classification Model: Techniques for Improving the Explainability of Machine Learning

Machine learning (ML) has been used increasingly in a wide range of operations at financial institutions. In the field of credit risk management, many financial institutions are starting to apply ML to credit scoring models and default models. In this paper we apply ML to a credit rating classification model. First, we estimate classification models based on both ML and ordinal logistic regression using the same dataset to see how model structure affects the prediction accuracy of models. In addition, we measure variable importance and decompose model predictions using so-called eXplainable AI (XAI) techniques that have been widely used in recent years. The results of our analysis are twofold. First, ML captures more accurately than ordinal logit regression the nonlinear relationships between financial indicators and credit ratings, leading to a significant improvement in prediction accuracy. Second, SHAP (Shapley Additive exPlanations) and PDP (Partial Dependence Plot) show that several financial indicators such as total revenue, total assets turnover, and ICR have a significant impact on firms’ credit quality. Nonlinear relationships between financial indicators and credit rating are also observed: a decrease in ICR below about 2 lowers firms’ credit quality sharply. Our analysis suggests that using XAI while understanding its underlying assumptions improves the low explainability of ML.