One of the important decision-making problems of modern financial institutions is credit scoring, which involves assessing credit risk. Decision-making models based on classifiers and feature selection methods that reduce the complexity of a decision problem by limiting the number of conditional attributes find use in such problems. The article examines the effectiveness of various combinations of classifiers and feature selection methods in the problem of credit risk assessment. The results of the conducted research indicate that for the considered set of data on cash loans granted, the Correlation-based Feature Selection method is the best method among the considered ones, and the Random Forest is the most effective classifier.