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The development of an ESG-rating model to assess the probability of default of corporate borrowersстатья
Информация о цитировании статьи получена из
Scopus
Статья опубликована в журнале из списка Web of Science и/или Scopus
Дата последнего поиска статьи во внешних источниках: 28 мая 2025 г.
- Авторы: Morgunov Alexei, Karminsky Alexander, Tarnovskaya Polina
- Журнал: Procedia computer science
- Том: 242
- Год издания: 2024
- Издательство: Elsevier BV
- Местоположение издательства: Netherlands
- Первая страница: 992
- Последняя страница: 999
- DOI: 10.1016/j.procs.2024.08.247
- Аннотация: Nowadays, Russian banks are developing and updating models for assessing the probability of default (PD) for various risk segments of corporate borrowers. This is to enable the use of rating models when assessing regulatory capital, in compliance with the requirements of the Regulations of the Bank of Russia. The application of rating models allows credit institutions to more accurately distribute regulatory and economic capital among borrowers, calculate reserves according to RAS and IFRS, set interest rates for transactions with borrowers as part of the pricing procedure, and conduct stress testing of both borrower portfolios and individual borrowers. The results of stress testing can then be incorporated into strategic planning.One of the Regulator’s requirements is to consider assessments of sustainable development and responsible financing ESG (environmental, social, and corporate governance) indicators as part of the credit risk management procedure, particularly in the development of models for assessing the probability of default. This research focuses on the development of an integral module (ESG rating) that includes ESG indicators. The aim is to improve the accuracy of existing models for assessing the probability of default (PD models) for corporate borrowers through the use of ESG factors. The findings of this research can be utilized by Russian banks to enhance the discriminatory and predictive capabilities of their own PD models, and by the regulator to understand the set of ESG indicators that impact the creditworthiness of corporate borrowers.
- Добавил в систему: Тарновская Полина Игоревна