CREDIT SCORING MODELS IN BANK CREDIT RISK MANAGEMENT: COMPARATIVE ASSESSMENT AND APPLICATION IN UKRAINIAN BANKING PRACTICE

Abstract

Banking is increasingly shaped by expanding data volumes, more complex borrower behaviour, and stricter credit risk management requirements. Under such conditions, scoring models are becoming especially relevant as instruments for the formalised assessment of creditworthiness, combining analytical accuracy, speed of decision-making, and the possibility of integration into the bank’s risk management system. The study compares traditional and modern scoring models in bank credit risk management and proposes an approach to their practical use in Ukrainian banking. Its focus is on scoring models as instruments for credit risk assessment. The study combines comparative analysis, matrix modelling, simulation, statistical modelling, and machine learning methods. Given limited access to primary banking information and confidentiality requirements, the empirical analysis was conducted on a synthesised demonstration dataset designed to reflect the structure of a real retail credit portfolio. For the analysis, a sample of 1,000 observations with a default share of 22.0% was constructed, and logistic regression, discriminant analysis, Random Forest, XGBoost, and a hybrid logit + ML re-ranking model were used for comparison. The results showed that XGBoost provided the highest predictive accuracy, with an AUC-ROC of 0.861, Gini of 0.722, Recall of 0.781, and Brier score of 0.141, whereas logistic regression demonstrated an AUC-ROC of 0.781 and retained advantages in terms of interpretability and suitability for validation. The hybrid model achieved an AUC-ROC of 0.848, Gini of 0.696, Recall of 0.773, and Brier score of 0.144, thus ensuring the best balance between accuracy, explainability, calibration, and practical applicability. Practically, the study offers an adaptive approach to selecting scoring models and a matrix for evaluating them under Ukrainian banking conditions, taking into account the requirements of the regulatory environment, data quality, and the instability of the operating conditions of Ukrainian banks.