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Modifications of order scales for assessing debtors

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Języki publikacji
EN
Abstrakty
EN
In previous research, the Extended Order Scale (EOS) dedicated to risk assessment was analysed. It was characterised by a Numerical Order Scale (NOS) evaluated by trapezoidal oriented fuzzy numbers (TrOFNs). However, the research showed that EOS with two-stage orientation phases, was too complicated. Therefore, the main aim of our paper is to simplify a Complete Order Scale (COS) to a zero- or one-stage order scale and a hybrid approach. For this purpose, a way to calculate the scoring function is presented. The results show that changes in the COS structure influence the values of a scoring function. Replacing just one linguistic indicator gives different results. Another finding of the research is the method’s flexibility that allows an expert to individually choose the most suitable COS. The research proves that the boundary between various linguistic labels cannot be precisely defined. However, knowledge of a formal COS structure allows it to be transformed into a less complex one.
Rocznik
Strony
142--151
Opis fizyczny
Bibliogr. 25 poz.
Twórcy
  • Department of Operations Research and Mathematical Economics, Poznań University of Economics and Business, Poznań, Poland
  • Institute of Economy and Finance, WSB University in Poznań, Poznań, Poland
Bibliografia
  • [1] Final report on specification of types of exposures to be associated with high risk under article 128(3) of regulation (EU) NO 575/2013. European Banking Authority (17 January 2019), accessed 4 maj 2022.
  • [2] Risk-based capital requirements RBC20. Calculation of minimum risk-based capital requirements. Bank for International Settlements (15 December 2019), accessed 4 May 2022.
  • [3] Revision to the standardised approach for credit risk. Second Consultative Document. Bank for International Settlements(10 December 2015), accessed 4 May 2022.
  • [4] Černevičiene, J., and Kabasinskas, A. Review of multi-criteria decision-making methods in finance using explainable artificial intelligence. Frontiers in Artificial Intelligence 5, 827584 (2022), 1–16.
  • [5] Corazza, M., Funari, S., and Gusso, R. Creditworthiness evaluation of Italian SMEs at the beginning of the 2007–2008 crisis: An MCDA approach. The North American Journal of Economics and Finance 38 (2016), 1–26.
  • [6] de Lima Silva, D. F., Soares Silva, J. C., de Oliveira Silva, L. G., Ferreira, L., and de Almeida-Filho,A. Sovereign credit risk assessment with multiple criteria using an outranking method. Mathematical Problems in Engineering 2018 2018), 1–11.
  • [7] Dubois, D., and Prade, H. Operations on fuzzy numbers. International Journal of Systems Science 9, 6 (1978), 613–626.
  • [8] Herrera, F., Alonso, S., Chiclana, F., and Herrera-Viedma, E. Computing with words in decision making: foundations, trends and prospects. Fuzzy Optimization and Decision Making 8, 4 (2009), 337–364.
  • [9] Herrera, F., and Herrera-Viedma, E. Linguistic decision analysis: Steps for solving decision problems under linguistic information. Fuzzy Sets and Systems 115, 1 (2000), 67–82.
  • [10] Kosiński, W. On fuzzy number calculus. International Journal of Applied Mathematics and Computer Science 16, 1 (2006), 51–57.
  • [11] Kosiński, W., Prokopowicz, P., and Ślęzak, D. Drawback of fuzzy arithmetics - new intuitions and propositions. In Methods of Artificial Intelligence, T. Burczynski, W. Cholewa, and M. Moczulski, Eds. PACM, (Gliwice 2002), 2002, pp. 231–237.
  • [12] Locurcio, M., Tajani, F., Morano, P., Anelli, D., and Manganelli, B. Credit risk management of property investments through multi-criteria indicators. Risks 9, 6, 106 (2021), 1–23.
  • [13] Piasecki, K. Revision of the Kosinski’s theory of ordered fuzzy numbers. Axioms 7, 1, 16 (2018), 1–14.
  • [14] Piasecki, K., and Łyczkowska-Hanćkowiak, A. Oriented fuzzy numbers vs. fuzzy numbers. Mathematics 9, 5 (2021), 523.
  • [15] Simons, T., and Tripp, T. The negotiation checklist: how to win the battle before it begins. Cornell Hotel and Restaurant Administration Quarterly 38, 1 (1997), 14–23.
  • [16] Soares, J. O., Pina, J., Ribeiro, M. S., and Catalão-Lopes, M. Quantitative vs. qualitative criteria for credit risk assessment. Frontiers in Finance and Economics 8, 1 (2011), 69–87.
  • [17] Tomić-Plazibat, N., Aljinović, Z., and Babić, Z. A multi-criteria approach to credit risk assessment. In Proceedings of the 7th WSEAS International Conference on Mathematics & Computers in Business & Economics, Cavtat, Croatia (2006), Z. Panian, Ed., vol. 15, World Scientific and Engineering Academy and Society (WSEAS), Stevens Point, pp. 76–81.
  • [18] Wójcicka-Wójtowicz, A. Can experts knowledge in eNS inspire efficient classification of potential debtors? In 38th International Conference Mathematical Methods in Economics MME 2020: Conference Proceedings (2020), S. Kapounek and H. Vránová, Eds., Mendel University in Brno, pp. 650–655.
  • [19] Wójcicka-Wójtowicz, A. How to include experts’ imprecision in credit risk assessment? In 13th International Scientific Conference Analysis of International Relations 2020. Methods and Models of Regional Development. Winter Edition (2020), W. Szkutnik, A. Sączewska-Piotrowska, M. Hadas-Dyduch, and J. Acedański, Eds., Wydawnictwo Uniwersytetu Ekonomicznego w Katowicach, pp. 234–247.
  • [20] Wójcicka-Wójtowicz, A. Influence of imprecision on credit risk assessment - case study. In Proceedings of the International Scientific Conference Hradec Economic Days (2020), P. Jedlicka, K. Firlej, P. Mareová, and I. Soukal, Eds., University of Hradec Králové, pp. 871–880.
  • [21] Wójcicka-Wójtowicz, A., and Piasecki, K. Application of the oriented fuzzy numbers in credit risk assessment. Frontiers in Mathematics 9, 5, 535 (2021), 1–13.
  • [22] Wójcicka-Wójtowicz, A., and Piasecki, K. Different ways of extending order scales dedicated to credit risk assessment. In Proceedings of the 39th International Conference on Mathematical Methods in Economics MME 2021 (2021), R. Hlavatý, Ed., Czech University of Life Sciences, Prague, pp. 387–392.
  • [23] Yi-Chung, H., and Chen, C.-J. A PROMETHEE-based classification method using concordance and discordance relations and its application to bankruptcy prediction. Information Sciences 181, 22 (2011), 4959–4968.
  • [24] Yu, L., Wang, S., Lai, K. K., and Zhou, L. An intelligent-agent-based multicriteria fuzzy group decision making model for credit risk analysis. In Bio-inspired credit risk analysis. Computational Intelligence with Support Vector Machines. Springer, Berlin, Heidelberg, 2008, pp. 197–222.
  • [25] Zhang, Z., Gao, G., and Shi, Y. Credit risk evaluation using multi-criteria optimization classifier with kernel, fuzzification and penalty factors. European Journal of Operational Research 237, 1 (2014), 335–348.
Uwagi
Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-1e75d078-c0b3-4223-9927-9287d8bc6eb9
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