A credit risk model based on contour subspaces for decision support systems in loan granting
Credit risk management is of considerable importance for banks, and the most common credit risk models are based on combining client’s private information with credit terms. However, if credit terms are an integral part of initial calculations, then results have to be recalculated for every alteration of credit terms. Thus, banks obtain ‘one-shot’ results from decision support systems that are built with application of these models. In the given paper a credit risk model is proposed. This model is based on a separate analysis of client’s private information and credit terms in order to construct a contour subspace for credit terms that correspond to an equal credit risk value. Application of a proposed model will add advanced options for decision support systems in loan granting, i.e. to visualize a contour subspace of credit terms for a client according to an individual creditworthiness estimation, provide options to choose credit terms from this contour subspace, and manage credit terms on-line according to the dynamics in a creditworthiness estimation.