Modeling of Bank Credit Risk Management Using the Cost Risk Model

Iryna Yanenkova, Yuliia Nehoda, Svetlana Drobyazko, Andrii Zavhorodnii and Lyudmyla Berezovska
Additional contact information
Iryna Yanenkova: Sector of the Digital Economy, NASU Institute for Economics and Forecasting, 26 Panasa Myrnoho St., 01011 Kyiv, Ukraine
Yuliia Nehoda: Department of Finance, National University of Life and Environmental Sciences of Ukraine, Heroiv Oborony Str. 15, 03041 Kyiv, Ukraine
Svetlana Drobyazko: The European Academy of Sciences LTD, 71-75 Shelton Street Covent Garden, London WC2H 9JQ, UK
Andrii Zavhorodnii: Department of Economics and Information Technology, Mykolayiv Interregional Institute for the Development Human Rights of the Higher Educational Institution “Open International University of Human Development” Ukraine, 2 Military Str. 22, 54003 Nikolaev, Ukraine
Lyudmyla Berezovska: Department of Finance, National University of Life and Environmental Sciences of Ukraine, Heroiv Oborony Str. 15, 03041 Kyiv, Ukraine

JRFM, 2021, vol. 14, issue 5, 1-15

Abstract: This article deals with the issue of managing bank credit risk using a cost risk model. Modeling of bank credit risk management was proposed based on neural-cell technologies, which expand the possibilities of modeling complex objects and processes and provide high reliability of credit risk determination. The purpose of the article is to improve and develop methodical support and practical recommendations for reducing the level of risk based on the value-at-risk ( VaR ) methodology and its subsequent combination with methods of fuzzy programming and symbiotic methodical support. The model makes it possible to create decision support subsystems for nonperforming loan management based on the neuro-fuzzy approach. For this paper, economic and mathematical tools (based on the VaR methodology) were used, which made it possible to analyze and forecast the dynamics of overdue payment; assess the quality of the credit portfolio of the bank; determine possible trends in bank development. A scientific and practical approach is taken to assess and forecast the degree of credit problematicity by qualitative criteria using a mathematical model based on a fuzzy technology, which can forecast the increased risk of loan default at an early stage in the process of monitoring the loan portfolio and model forecasting changes in the degree of credit problematicity on change of indicators. A methodology is proposed for the analysis and forecasting of indicators of troubled loan debt, which should be implemented as software and included in the decision support system during the process of monitoring the risk of the bank’s credit portfolio.

Keywords: bank credit risks; credit portfolio; observation; simulation modeling; bank expenses; rating; default (search for similar items in EconPapers)
JEL-codes: C E F2 F3 G (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
https://www.mdpi.com/1911-8074/14/5/211/pdf (application/pdf)
https://www.mdpi.com/1911-8074/14/5/211/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jjrfmx:v:14:y:2021:i:5:p:211-:d:549906

Access Statistics for this article

JRFM is currently edited by Ms. Chelthy Cheng

More articles in JRFM from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().