Application of Credit Risk Scoring Methods in Corporate Borrower Monitoring
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Abstract
A method for assessing the risk of default of a corporate borrower at the monitoring stage based on a scoring assessment is proposed. This paper provides proof of the hypothesis that scoring methods for assessing credit risks can be used not only at the stage of initial assessment of a potential borrower when making a decision on lending, but also at the stage of its monitoring when accompanying a transaction. Monitoring is a periodic review of the credit quality of the corporate borrower with whom the loan agreement is concluded. This is done for the purpose of timely detection of negative signals, as well as timely response to threatening trends in the borrower's activities.
Some credit institutions save on monitoring by relying on the decision-making system, considering it flawless. However, this saving can be a fatal mistake, since many things change over time during the "life" of the enterprise. This is facilitated by both external factors (political, economic) and internal (incorrect development strategy of the organization, inability to assess its own credit capabilities, unscrupulous counterparties).
The proposed method is a system of automatic risk signals that have been tested for predictive ability, excluding manual procedures. The proposed solution includes markers (risk signals) that have a predictive ability above average, which can lead to a default of the corporate borrower. In addition, color marking is applied – red, yellow, green, which allows you to visualize the criticality of the identified risk signal depending on the predictive ability - a visual representation of the borrower's risks in order to facilitate interpretation.
The analysis of the developed method showed how much it is possible to speed up the monitoring process, which will allow for a prompt response to the identified risk signals, as well as to predict the likely deterioration of the borrower's credit quality in the loan or guarantee portfolio without compromising the quality of risk assessment.
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References
2. Бордакова М.В. Особенности построения внутренних моделей рейтинговой системы оценки кредитного риска корпоративных заемщиков // Банковские услуги, 2012. С. 9–21.
3. Шаталова Е.П., Шаталов А.Н. Оценка кредитоспособности заемщиков в банковском риск-менеджменте. М.: КНОРУС, 2011. 166 с.
4. Александров В.В. Развивающиеся системы. В науке, технике, обществе и культуре. Часть 1. Теория систем и системное моделирование. СПб.: Изд-во СПбГТУ, 2000. 243 с.
5. Бухтин М.А. Принципы и подходы к формированию методик внутренних кредитных рейтингов для корпоративных клиентов // Управление финансовыми рисками. 2008. 27 с.
6. Бенгфорт Б., Билбро Р., Охеда Т. Прикладной анализ текстовых данных на Python. Машинное обучение и создание приложений обработки естественного языка. СПб.: Питер, 2019. 368 с.
7. Федеральный закон «О несостоятельности (банкротстве)» от 26.10.2002 № 127-ФЗ (последняя редакция). URL: http://www.consultant.ru/document/cons_doc_LAW_39331/, дата обращения 04.04.2021.
8. Положение Банка России от 28 июня 2017 г. № 590-П «О порядке формирования кредитными организациями резервов на возможные потери по ссудам, ссудной и приравненной к ней задолженности». URL: http://www.consultant.ru/document/cons_doc_LAW_220089/, дата обращения 04.04.2021.
9. Федеральный закон «О Центральном банке Российской Федерации (Банке России)» от 10.07.2002 № 86-ФЗ. URL: http://www.consultant.ru/document/cons_doc_LAW_37570/, дата обращения 04.04.2021.
10. Федеральный закон «О банках и банковской деятельности» от 02.12.1990 № 395-1. URL: http://www.consultant.ru/document/cons_doc_LAW_5842/, дата обращения 04.04.2021.
11. Ендовицкий Д.А., Бахтин К.В., Ковтун Д.В. Анализ кредитоспособности организации и группы компаний. М.: КНОРУС, 2012. 376 с.
12. Репин В.В., Елиферов В.Г. Процессный подход к управлению. Моделирование бизнес-процессов. М.: Манн, Иванов и Фербер, 2013. 45 с.
13. Лопез де Прадо М. Машинное обучение: алгоритмы для бизнеса. СПб.: Питер, 2019. 432 с.
14. Элбон К. Машинное обучение с использованием Python. Сборник рецептов. СПб.: БХВ-Петербург, 2019. 384 с.
15. Филипенков Н. Интеллектуальный анализ данных в управлении розничным кредитным риском // Спецсеминар «Интеллектуальный анализ данных в бизнесе», 2010. 42 с. URL: http://www.machinelearning.ru/wiki/images/b/b2/MSU-BI-Filipenkov-2010-10-01.pdf, дата обращения 04.04.2021.
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