In today’s economic landscape, accurate credit risk assessment is paramount for prudent lending decisions. This paper proposes a qualitative framework for modeling default probability (PD) in credit risk assessment. Unlike traditional quantitative models, this approach integrates a broader range of factors including market position, ownership structure, and sector risk. By complementing quantitative techniques with qualitative insights, this model enhances PD assessments and can be utilized in stress testing and macroprudential analyses. The methodology involves systematically evaluating qualitative dimensions contributing to credit risk and employs a logit model with backward selection. Drawing from a comprehensive dataset, including approximately 30 qualitative characteristics from financial statements, Credit Registry loan-level information, and Business Register data, this approach offers a robust framework for assessing default probability.