Abstract. Credit risk represents one of the most critical risks associated with the banking sector, having a direct impact on the banking institutions’ overall performance. As of today, such institutions can access a wide range of methods and systems for assessing credit risk, with direct impact on their capital adequacy ratios. Approaches based on internal rating models, as introduced by Basel II, allow banks to utilize their own methods to quantify credit risk, essential to the risk-weighting of their assets and therefore to the measuring of the capital requirements.

    This paper addresses a potential scoring model in order to quantify the default probability, based on quantitative information and forecasting of potential default scenarios. Qualitative variables have also been considered in order to generate higher prediction accuracy.

Keywords: scoring model; default probability; logit model; qualitative variables; credit risk.