IDEAS home Printed from https://ideas.repec.org/a/gam/jjrfmx/v14y2021i5p216-d551569.html
   My bibliography  Save this article

Model of Assessing the Overdue Debts in a Commercial Bank Using Neuro-Fuzzy Technologies

Author

Listed:
  • Dmytro Kovalenko

    (Department of Finance and Financial and Economic Security, Kyiv National University of Technologies and Design, Nemyrovycha-Danchenka Street, 2, 01011 Kyiv, Ukraine)

  • Olga Afanasieva

    (Department «Management of the Hi-Tech Enterprises», Moscow Aviation Institute, National Research University, Volokolamskoe Shosse, 4, 125993 Moscow, Russia)

  • Nani Zabuta

    (Department of Finance and Accounting, Open International University of Human Development “UKRAINE”, 23 Lvovskaya Street, 01001 Kiev, Ukraine)

  • Tetiana Boiko

    (Department of Finance, Accounting and Marketing, Prydniprovska State Academy of Civil Engineering and Architecture, 24 a, Chernyshevsky Street, 49600 Dnepro, Ukraine)

  • Rosen Rosenov Baltov

    (Bulgarian Academy of Sciences, 1 “15 Noemvri” Str., 1040 Sofia, Bulgaria)

Abstract

This article considers the problems of overdue credit debt and the creation of effective methods to manage problem debts in banks. The purpose of this paper is to study the problem of overdue credit debt and create effective methods to manage problem debts in financial institutions. Based on a combination of tools of fuzzy logic theory and artificial neural networks, an economic-mathematical model of collection scoring was built. Kohonen self-organizing maps were used to set the parameters of membership functions in the process of fuzzification of quantitative variables of the built model. Data were taken from the official websites of four Bulgarian banks for 2015–2019. The volume of the prepared sample amounted to 1000 credit agreements with active overdue payments. The practical value of the built model of collection scoring for the recovery of overdue debt lies in the possibility to make recommendations for work with each segment of the portfolio of overdue loans in accordance with the calculated level of credit risk. The introduction of credit risk assessment models based on neuro-fuzzy technologies in the work of financial institutions will have a positive impact on the financial results of lending activities of banks.

Suggested Citation

  • Dmytro Kovalenko & Olga Afanasieva & Nani Zabuta & Tetiana Boiko & Rosen Rosenov Baltov, 2021. "Model of Assessing the Overdue Debts in a Commercial Bank Using Neuro-Fuzzy Technologies," JRFM, MDPI, vol. 14(5), pages 1-20, May.
    • Handle: RePEc:gam:jjrfmx:v:14:y:2021:i:5:p:216-:d:551569
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1911-8074/14/5/216/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1911-8074/14/5/216/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Edward I. Altman, 1968. "The Prediction Of Corporate Bankruptcy: A Discriminant Analysis," Journal of Finance, American Finance Association, vol. 23(1), pages 193-194, March.
    2. Gabriel Jiménez & Steven Ongena & José-Luis Peydró & Jesús Saurina, 2017. "Macroprudential Policy, Countercyclical Bank Capital Buffers, and Credit Supply: Evidence from the Spanish Dynamic Provisioning Experiments," Journal of Political Economy, University of Chicago Press, vol. 125(6), pages 2126-2177.
    3. Matthew Baron & Wei Xiong, 2017. "Credit Expansion and Neglected Crash Risk," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 132(2), pages 713-764.
    4. Carmelo Salleo & Alberto Grassi & Constantinos Kyriakopoulos, 2020. "A Comprehensive Approach for Calculating Banking Sector Risks," IJFS, MDPI, vol. 8(4), pages 1-21, November.
    5. Key Pousttchi & Maik Dehnert, 2018. "Exploring the digitalization impact on consumer decision-making in retail banking," Electronic Markets, Springer;IIM University of St. Gallen, vol. 28(3), pages 265-286, August.
    6. Irina Lock & Peter Seele, 2015. "Analyzing Sector‐Specific CSR Reporting: Social and Environmental Disclosure to Investors in the Chemicals and Banking and Insurance Industry," Corporate Social Responsibility and Environmental Management, John Wiley & Sons, vol. 22(2), pages 113-128, March.
    7. Roper, Stephen & Tapinos, Efstathios, 2016. "Taking risks in the face of uncertainty: An exploratory analysis of green innovation," Technological Forecasting and Social Change, Elsevier, vol. 112(C), pages 357-363.
    8. Yi-Kai Chen & Chung-Hua Shen & Lanfeng Kao & Chuan-Yi Yeh, 2018. "Bank Liquidity Risk and Performance," Review of Pacific Basin Financial Markets and Policies (RPBFMP), World Scientific Publishing Co. Pte. Ltd., vol. 21(01), pages 1-40, March.
    9. Luigi Infante & Stefano Piermattei & Raffaele Santioni & Bianca Sorvillo, 2020. "Diversifying away risks through derivatives: an analysis of the Italian banking system," Economia Politica: Journal of Analytical and Institutional Economics, Springer;Fondazione Edison, vol. 37(2), pages 621-657, July.
    10. WeiMing Mou & Wing-Keung Wong & Michael McAleer, 2018. "Financial Credit Risk Evaluation Based on Core Enterprise Supply Chains," Sustainability, MDPI, vol. 10(10), pages 1-17, October.
    11. Mou, W.M. & Wong, W.-K. & McAleer, M.J., 2018. "Financial Credit Risk and Core Enterprise Supply Chains," Econometric Institute Research Papers EI2018-27, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    12. Martin Leo & Suneel Sharma & K. Maddulety, 2019. "Machine Learning in Banking Risk Management: A Literature Review," Risks, MDPI, vol. 7(1), pages 1-22, March.
    13. Hamid Reza Khedmatgozar, 2021. "The impact of perceived risks on internet banking adoption in Iran: a longitudinal survey," Electronic Commerce Research, Springer, vol. 21(1), pages 147-167, March.
    14. Madalina-Gabriela ANGHEL & Marian SFETCU & Gyorgy BODO & Doina BUREA, 2017. "Bank Risk Management," Romanian Statistical Review Supplement, Romanian Statistical Review, vol. 65(11), pages 87-94, November.
    15. Saba Moradi & Farimah Mokhatab Rafiei, 2019. "A dynamic credit risk assessment model with data mining techniques: evidence from Iranian banks," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 5(1), pages 1-27, December.
    16. Tang, Lingxiao & Cai, Fei & Ouyang, Yao, 2019. "Applying a nonparametric random forest algorithm to assess the credit risk of the energy industry in China," Technological Forecasting and Social Change, Elsevier, vol. 144(C), pages 563-572.
    17. Junye Li & Gabriele Zinna, 2018. "How Much of Bank Credit Risk Is Sovereign Risk? Evidence from Europe," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 50(6), pages 1225-1269, September.
    18. Nobuyoshi Yamori, 2019. "The Effects of the Financing Facilitation Act after the Global Financial Crisis: Has the Easing of Repayment Conditions Revived Underperforming Firms?," JRFM, MDPI, vol. 12(2), pages 1-17, April.
    19. Sophie Harnay & Laurence Scialom, 2016. "The influence of the economic approaches to regulation on banking regulations: a short history of banking regulations," Cambridge Journal of Economics, Cambridge Political Economy Society, vol. 40(2), pages 401-426.
    20. Gersbach, Hans & Rochet, Jean-Charles, 2017. "Capital regulation and credit fluctuations," Journal of Monetary Economics, Elsevier, vol. 90(C), pages 113-124.
    21. Duc Hong Vo & Anh The Vo & Zhaoyong Zhang, 2019. "Exchange Rate Volatility and Disaggregated Manufacturing Exports: Evidence from an Emerging Country," JRFM, MDPI, vol. 12(1), pages 1-25, January.
    22. Altman, Edward I. & Haldeman, Robert G. & Narayanan, P., 1977. "ZETATM analysis A new model to identify bankruptcy risk of corporations," Journal of Banking & Finance, Elsevier, vol. 1(1), pages 29-54, June.
    23. Edward I. Altman, 1968. "Financial Ratios, Discriminant Analysis And The Prediction Of Corporate Bankruptcy," Journal of Finance, American Finance Association, vol. 23(4), pages 589-609, September.
    24. Rakesh Raut & Naoufel Cheikhrouhou & Manoj Kharat, 2017. "Sustainability in The Banking Industry: A Strategic Multi‐Criterion Analysis," Business Strategy and the Environment, Wiley Blackwell, vol. 26(4), pages 550-568, May.
    25. Mian, Atif & Santos, João A.C., 2018. "Liquidity risk and maturity management over the credit cycle," Journal of Financial Economics, Elsevier, vol. 127(2), pages 264-284.
    26. Levrel, Harold & Scemama, Pierre & Vaissière, Anne-Charlotte, 2017. "Should We Be Wary of Mitigation Banking? Evidence Regarding the Risks Associated with this Wetland Offset Arrangement in Florida," Ecological Economics, Elsevier, vol. 135(C), pages 136-149.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ao Yu & Zhuoqiang Jia & Weike Zhang & Ke Deng & Francisco Herrera, 2020. "A Dynamic Credit Index System for TSMEs in China Using the Delphi and Analytic Hierarchy Process (AHP) Methods," Sustainability, MDPI, vol. 12(5), pages 1-21, February.
    2. Haitao Lu & Xiaofeng Hu, 2024. "Enhancing Financial Risk Prediction for Listed Companies: A Catboost-Based Ensemble Learning Approach," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 15(2), pages 9824-9840, June.
    3. Aleksandra Kuzior & Anna Liakisheva & Iryna Denysiuk & Halyna Oliinyk & Liudmyla Honchar, 2020. "Social Risks of International Labour Migration in the Context of Global Challenges," JRFM, MDPI, vol. 13(9), pages 1-31, September.
    4. Mikel Bedayo & Raquel Vegas & Gabriel Jiménez & José-Luis Peydró, 2020. "Screening and Loan Origination Time: Lending Standards, Loan Defaults and Bank Failures," Working Papers 1215, Barcelona School of Economics.
    5. Li, Chunyu & Lou, Chenxin & Luo, Dan & Xing, Kai, 2021. "Chinese corporate distress prediction using LASSO: The role of earnings management," International Review of Financial Analysis, Elsevier, vol. 76(C).
    6. Suzan Hol, 2006. "The influence of the business cycle on bankruptcy probability," Discussion Papers 466, Statistics Norway, Research Department.
    7. Lin, Hsiou-Wei William & Lo, Huai-Chun & Wu, Ruei-Shian, 2016. "Modeling default prediction with earnings management," Pacific-Basin Finance Journal, Elsevier, vol. 40(PB), pages 306-322.
    8. Enrico Supino & Nicola Piras, 2022. "Le performance dei modelli di credit scoring in contesti di forte instabilit? macroeconomica: il ruolo delle Reti Neurali Artificiali," MANAGEMENT CONTROL, FrancoAngeli Editore, vol. 2022(2), pages 41-61.
    9. Quader, Syed Manzur, 2017. "Differential effect of liquidity constraints on firm growth," Review of Financial Economics, Elsevier, vol. 32(C), pages 20-29.
    10. Mitroussi, K. & Abouarghoub, W. & Haider, J.J. & Pettit, S.J. & Tigka, N., 2016. "Performance drivers of shipping loans: An empirical investigation," International Journal of Production Economics, Elsevier, vol. 171(P3), pages 438-452.
    11. M. Naresh Kumar & V. Sree Hari Rao, 2015. "A New Methodology for Estimating Internal Credit Risk and Bankruptcy Prediction under Basel II Regime," Computational Economics, Springer;Society for Computational Economics, vol. 46(1), pages 83-102, June.
    12. Golaszewski, Richard & Sanders, Matthew, 1991. "Financial Stress in the U.S. Airline Industry," Transportation Research Forum Proceedings 1990s 319097, Transportation Research Forum.
    13. Roman Vavrek & Ivana Kravčáková Vozárová & Rastislav Kotulič, 2021. "Evaluating the Financial Health of Agricultural Enterprises in the Conditions of the Slovak Republic Using Bankruptcy Models," Agriculture, MDPI, vol. 11(3), pages 1-19, March.
    14. Chiuling Lu & Ann Yang & Jui-Feng Huang, 2015. "Bankruptcy predictions for U.S. air carrier operations: a study of financial data," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 39(3), pages 574-589, July.
    15. Amin Jan & Maran Marimuthu & Muhammad Kashif Shad & Haseeb ur-Rehman & Muhammad Zahid & Ahmad Ali Jan, 2019. "Bankruptcy profile of the Islamic and conventional banks in Malaysia: a post-crisis period analysis," Economic Change and Restructuring, Springer, vol. 52(1), pages 67-87, February.
    16. Tang, Lingxiao & Cai, Fei & Ouyang, Yao, 2019. "Applying a nonparametric random forest algorithm to assess the credit risk of the energy industry in China," Technological Forecasting and Social Change, Elsevier, vol. 144(C), pages 563-572.
    17. Mark Clintworth & Dimitrios Lyridis & Evangelos Boulougouris, 2023. "Financial risk assessment in shipping: a holistic machine learning based methodology," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 25(1), pages 90-121, March.
    18. B. Luppi & M. Marzo & E. Scorcu, 2007. "A credit risk model for Italian SMEs," Working Papers 600, Dipartimento Scienze Economiche, Universita' di Bologna.
    19. Ha-Thu Nguyen, 2015. "How is credit scoring used to predict default in China?," EconomiX Working Papers 2015-1, University of Paris Nanterre, EconomiX.
    20. Edward I. Altman & Marco Balzano & Alessandro Giannozzi & Stjepan Srhoj, 2023. "Revisiting SME default predictors: The Omega Score," Journal of Small Business Management, Taylor & Francis Journals, vol. 61(6), pages 2383-2417, November.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jjrfmx:v:14:y:2021:i:5:p:216-:d:551569. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.