Banking system being the most important sector of the economy of every country often encounters a number of risks. Financial institutions of that system operate in an unstable environment and without having complete information about that environment, they may suffer significant losses. The main source of such losses is considered to be a credit risk, for the management of which various mathematical models are being developed, which allow to make decision on granting a loan. Lately on this level machine learning (ML) classification algorithms are often used for credit risk modeling. In this research work using the ideas of ML well-known algorithms a new algorithm for solving the binary classification problem was developed. By means of the created algorithm based on real data a classification model has been de-eloped, qualitative indicators of that model such as: ROC AUC, PR AUC, Precision, Re-call, F1 score, were evaluated. By modifying the resulting probabilities into a range of 300-850 score points a scoring model has been developed, the usage of which can mitigate the credit risk and protect financial organizations from major losses.
Arakelyan et al. (Tue,) studied this question.