Credit Risk Classification using Statistical and Machine Learning Methods

Credit ratings present a rating agency or a bank’s assessment of a company’s risk profile. It is used extensively by business practitioners to judge the company’s credit worthiness, to determine whether or not to grant credit, and to calculate the interest rate for loans. Under BASEL II, banks are allowed to use advanced internal rating-based approaches to calculate credit risks themselves if said approaches comply with certain supervisory standards. This has sparked an interest in developing statistical credit classification models that can produce accurate ratings quickly and interpretably. We focus on the classification accuracy and interpretability of four classification methods on a credit portfolio of small Canadian businesses. The four methods that we compare are ordinal regression, ordinal gradient boosting, multinomial gradient boosting and random forest.