The purpose of the article is to develop a methodological approach to support the decision-making process in determining the creditworthiness of legal entities, as well as to create economic mathematical models based on this approach using the theory of fuzzy logic and fuzzy sets. Methodology. In the author's work (Palamarchuk, 2013), 49 real financial statements (Form 1 and Form 2) of Ukrainian enterprises were selected, 25 of which were potentially bankrupt and 24 were normally operating enterprises. As a result, 7 coefficients were obtained. Here we continue our modelling and building rule base. Result of the experiment is based on statistical data of domestic enterprise. The model has been constructed with the use of theory of fuzzy logic. Considering the expert knowledge, this model helps to make decisions on whether to provide the legal entity with the loan. Practical implications. The model and methodology can be used in commercial banks of Ukraine for calculating application risks. The known models do not fit to every economy. This is the reason which provides value originality of the topic of this study, which solves the problem of creating a method that would give the most sufficient assessment of creditworthiness.
How to Cite
creditworthiness, default, fuzzy logic, legal entity, bankruptcy, stable enterprise, term
Altman, E. I. (1968). Financial Ratios, Discriminant Analysis and the Predic-tion of Corporate Bankruptcy. The Journal of Finance, vol. 4, pp. 589–609.
Davydova, G. V., & Belikov, A. Yu. (1999). Methods of quantitative as-sessment of the risk of bankruptcy of enterprises. Risk Management, vol. 3, pp. 13–20.
Matviichuk, A. V. (2007). Diagnosis of bankruptcy of enterprises in the conditions of transformational economy. "Organizational and legal aspects and economic security of modern entrepreneurship": materials of the IV Regional scientific-practical conference. In 2 parts. Vinnytsia, Part I: 80–86.
Matviichuk, A. V. (2006). Discriminant model for estimating the probabil-ity of bankruptcy. Modeling and information systems in economics. Kyiv: KNEU, vol. 74, pp. 299–314.
Palamarchuk, O. V. (2013). Selection of indicators to determine the credit-worthiness of legal entities. Economic Analysis: Coll. Science. Works. Ter-nopil, vol. 12(1), pp. 266–268.
Resolution of the Board of the National Bank of Ukraine of January 25, 2012. No. 23.
Resolution of the Board of the National Bank of Ukraine of July 6, 2000. No. 279.
Rothstein, A. P. (1999). Intelligent identification technologies: fuzzy sets, genetic algorithms, neural networks. Vinnytsia: Universum-Vinnytsia.
Rummelhart, D. E., Hinton, G. E., & Williams, R. J. (1986). Learning Internal Representation by Back-Propagation Errors. Nature, vol. 23, pp. 533–536.
Sytnyk, V. F., Tatarchuk, M. I., Pisarevskaya, T. A., & Sendzyuk, M. A. (2004). Systems of economic information processing: Teaching method. way. for independent study. Kyiv: KNEU.
Tereshchenko, O. O. (2003). Discriminant analysis in assessing the credit-worthiness of the enterprise .Bulletin of the NBU, vol. 6 (88), pp. 24–27.
Tereshchenko, O. O. (2004). Anti-crisis financial management at the enter-prise. Kyiv: KNEU.
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