Credit Risk Estimation of the Banking System within the Macroprudential Stress-testing Approach: an Architectural Solution Development

Student: Kirpishchikova Marina

Faculty: Faculty of Economics, Management, and Business Informatics

Educational Programme: Information Analytics in Enterprise Management (Master)

Final Grade: 10

Year of Graduation: 2021

This work is devoted to the analysis of the key points of credit risk estimation. With the increase in lending services, there is a significant growth in risks for credit organizations. Mostly it is associated with a default by a borrower. To prevent defaults and reduce losses it is necessary for banks to estimate possible risks at the beginning of credit relations with borrowers. Despite the diversity of research in the lending’s field most credit organizations still suffer from financial losses of credit risk. The purpose of the article is to develop a universal algorithm for credit risk estimation. This algorithm have to be suitable for credit organizations and banking sector in whole. The algorithm was developed based on financial and intellectual data analysis.

Full text (added May 30, 2021)

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