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Modeling of Bank Credit Risk Management Using the Cost Risk Model

Author

Listed:
  • 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)

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.

Suggested Citation

  • Iryna Yanenkova & Yuliia Nehoda & Svetlana Drobyazko & Andrii Zavhorodnii & Lyudmyla Berezovska, 2021. "Modeling of Bank Credit Risk Management Using the Cost Risk Model," JRFM, MDPI, vol. 14(5), pages 1-15, May.
    • Handle: RePEc:gam:jjrfmx:v:14:y:2021:i:5:p:211-:d:549906
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    Cited by:

    1. Seyyide Doğan & Yasin Büyükkör & Murat Atan, 2022. "A comparative study of corporate credit ratings prediction with machine learning," Operations Research and Decisions, Wroclaw University of Science and Technology, Faculty of Management, vol. 32(1), pages 25-47.
    2. Oritsegbubemi Kehinde Natufe & Esther Ikavbo Evbayiro-Osagie, 2023. "Credit Risk Management and the Financial Performance of Deposit Money Banks: Some New Evidence," JRFM, MDPI, vol. 16(7), pages 1-23, June.
    3. Nartey Menzo, Benjamin Prince & Mogre, Diana & Asuamah Yeboah, Samuel, 2024. "Beyond Income: The Complexities of Credit Risk in Developing Countries," MPRA Paper 122364, University Library of Munich, Germany, revised 20 Sep 2024.

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