Abstract
Credit risk management is one of the most important tasks in banking risk management. Credit risk is the possibility of financial losses if the borrower fails to fulfill its obligations in a timely manner and in full, in particular as a result of a delay or non-repayment of a payment on a banking product. This article is devoted to the search for optimal methods for assessing the main component of the credit risk model - the probability of default of the borrower (PD) at the stage of building behavioral models. The paper considers two blocks of mathematical models - quantitative and classification. The object of the study is a portfolio of homogeneous loans of a commercial bank, the subject is the dynamics of the exit of the considered contracts into default. As an effective approach, the author proposes an integrated method for introducing the forecast result obtained using migration matrices as a new variable for the scoring model. Namely, revealing the depth of delinquency, upon reaching which borrowers do not improve the quality of the loan, as a separate predictor that helps to improve the scoring result.
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