The article discusses a statistical framework for modeling credit risk, incorporating default information and credit ratings. The researchers aim to improve the prediction of bankruptcies and understand factors affecting creditworthiness. They extend existing models to handle correlated ordinal data, like credit ratings. The study highlights the importance of credit risk management for banks and insurance companies, especially after the financial crisis. Internal statistical models may outperform credit ratings in predicting failures when defaults are rare. Overall, the research focuses on enhancing credit risk modeling to better assess and manage risks in the financial sector.