Internal Credit Risk Models and Digital Transformation: What to Prepare for? An Application to Poland
Purpose: The digitization of credit risk through machine learning technology is becoming more attractive, especially nowadays. The article aims to analyze the performance of models estimated with Machine Learning (ML) algorithms in predicting the risk of default compared with standard statistical models such as logistic regression (benchmark model). Design/Methodology/Approach: The indicated models were estimated using an original dataset, including financial information and the credit history of non-financial Polish enterprises. The dataset is also enlarged 20-fold to obtain a set of the so-called Big Data that could also be accepted. The out-of-sample performance comparing one-year-ahead PD estimates and observed default data for the 2015-2020 period was verified about the models under consideration. The period above also includes that associated with the COVID-19 pandemic. Findings: Based on the results obtained, practical information was supplied to credit-risk researchers. Where only a limited data set is available, and where this is confined to financial indicators only, models based on ML are seen to offer a significant increase in discriminant power and precision as compared with statistical models, this being especially the case with an artificially generated set of so-called Big Data. Practical Implications: Models estimated with ML algorithms can benchmark the probability of default obtained using more apparent statistical models. In practice, this is useful when estimates under the two types of model prove notably different. Application is handy with, for example, more significant or higher-risk borrowers. Originality/Value: The article seeks to ascertain how the market expansion of a bank's product and digital divisions might be supported without the speed and quality of credit-risk assessment is limited. The inclusion here of the COVID-19 (exogenous economic shock) period ensures the particular usefulness of recommendations for credit-risk analysts.