Abstract

Financial policy of any entity comes down to managing economic parameters and relies on exploration of the strength of relations and dependencies between them. By means of analysis of the given indicators and identification of the degree of their correlation with the basic indicators, financial engineers get vast opportunities concerning economic processes modeling and optimization. Thus, in the sector of commercial credit, which is characterized by the absence of complete Russian procedures that would help assess the risk of the creditor, the study of credit analysis exhibitors system provides the key to the development of modern models and methodological assessment technologies. The present scientific work includes theoretical, methodological, analytical and financial engineering components. The theoretical part has to provide the theoretical basis for the research, reflect the terms and definitions overview and show the development of the basic complex concept – algorithmic modeling of commercial debtor segmentation. The methodological component is devoted to the study of the fundamental principles which help construct the system of analytical indicators and establish their connection with the algorithmic model. The analytical and financial-engineering component of the scientific work which is of fundamental importance (as it reflects the implementation of the research focus), consists of five stages: 1) identification and thematic grouping of the creditor's risk indicators; 2) indicators unification in the form of the commercial debtor segmentation algorithm; 3) algorithmic model testing and performance evaluation; 4) explanation of cause-effect relationships strength between the interpenetrating exponents of the algorithmic model; 5) identification of the most valuable parameters (identifying factors) of credit analysis in the commercial lending sector. Studies were carried out using selected materials taken from Kemerovo Region agricultural companies. Thus, the result of the research is the author's debtor segmentation. The model makes it possible to form risk groups in the commercial lending sector. The analytical structure is based on the combination of key credit risk parameters taking regional and industry specific features of businesses operation into account and justified by strong correlation of the algorithm elements. That helps financial analysts save time and have equivalent effect of credit risk assessment procedure.

Keywords

Commercial lending, commercial debtor, credit risk, credit risk indicators, algorithmic modeling

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