Credit Risk Estimation through Eventological Scoring
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URI (for links/citations):
http://cesmaa.eu/journals/jaes/https://elib.sfu-kras.ru/handle/2311/110963
Author:
Петрова, Аида Талятовна
Владимирова, Ольга Николаевна
Полякова, Ирина Александровна
Игнатова, Татьяна Васильевна
Шовхалов, Шамиль Ахьядович
Corporate Contributor:
Торгово-экономический институт
Кафедра бухгалтерского учета, анализа и аудита
Date:
2018-08Journal Name:
Journal of Applied Economic SciencesJournal Quartile in Scopus:
Q3Bibliographic Citation:
Петрова, Аида Талятовна. Credit Risk Estimation through Eventological Scoring [Текст] / Аида Талятовна Петрова, Ольга Николаевна Владимирова, Ирина Александровна Полякова, Татьяна Васильевна Игнатова, Шамиль Ахьядович Шовхалов // Journal of Applied Economic Sciences. — 2018. — VIII (5(59)).Текст статьи не публикуется в открытом доступе в соответствии с политикой журнала.
Abstract:
After IFRS 9 "Financial Instruments" entered into force and due to the fact that Russian companies are updating their accounting to comply with international financial reporting standards, a number of problems arise, and one of them is the issue of objectivity and accuracy of credit risk estimation. One of the challenges in the methodology what regulates the provisions of this standard deals with assessing the credit risk of a financial asset so that one can an approach to estimate its impairment allowance. In accordance with the requirements of the standard, the credit risk estimation should be based on a comprehensive analysis and use available and relevant information. At present moment, most methods for assessing credit risk are based on statistical data and are neither flexible nor imply the opportunity to change the assessed parameters. The article explores how the method of eventological scoring can be adapted to use within the credit field, as its application enables to calculate the conditional probability of the occurrence of the target event in the context of numerous random events and take into account all relevant factors that influence the credit risk for a particular financial instrument. Application of the proposed method will help to choose an approach to estimating the expected credit losses without using statistical data and quantitative scores of experts. Using the proposed method will make it possible to simplify the procedures for data collection and formalizing the results of the survey that considers various factors and aims to assess the dynamics of credit risk.
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