Application of Machine Learning to a Credit Rating Classification Model: Techniques for Improving the Explainability of Machine Learning
April 21, 2023
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.
- JEL classifications
- C49, C55, G32
- Credit risk management, Machine learning, Explainability, eXplainable AI (XAI)
The authors would like to thank the staff of the Bank of Japan for their valuable comments. The views expressed in this paper are those of the authors and do not necessarily reflect the official view of the Bank of Japan or of the Financial System and Bank Examination Department.
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- *2Financial System and Bank Examination Department, Bank of Japan
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- *3Financial System and Bank Examination Department, Bank of Japan
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