Forecasting the Risk of Bankruptcy in Poorly Formalized Processes

There is a class of complex systems characterized by dynamism, multi-link structural elements, multi-stage, multi-linked chain of processes. Moreover, each of these processes occurs under conditions of stochastic and non-stochastic uncertainty in the initial information, internal and external environment, which predetermine the uncertainty of the nature of the development of the situation. Decision-making problems in such systems are divided into two types: 1) decision-making problems under risk conditions, when uncertainty conditions are only probabilistic, stochastic in nature; 2) decision-making problems under conditions of uncertainty, when the accompanying conditions are of a non-stochastic nature, and also when the necessary reliable statistical data is unknown. In tasks of the second type, risks are manifested to a greater extent than in the first. At the same time, risk should be considered – as an object, event, phenomenon – as a formal mathematical category in accordance with its following information interpretation: risk is information uncertainty, fuzziness of the “object – subject – environment” system and its individual elements. The measure of this uncertainty determines the measure of danger, possible damage, loss from the implementation of some decision or event. The existence of risk is associated with the inability to predict the future with 100 % accuracy. Based on this, the main property of risk should be singled out: risk occurs only in relation to the future and is inextricably linked with forecasting, and therefore with decision-making in general (the word “risk” literally means “making a decision”, the result of which is unknown). Following the above, it is also worth noting that the categories “risk” and “uncertainty” are closely related and are often used as synonyms. In conditions when the initial factors are given in the form of fuzzy characteristics, other approaches based on the intelligent technologies of Soft Computing are widely used for forecasting. When evaluating alternative decision-making options for risk assessment under uncertainty, the problem of developing fuzzy models based on fuzzy inference rules arises. But there is no universal method for constructing fuzzy evaluation models. The advantage of fuzzy logic lies in the possibility of using expert knowledge about a given object in the form of if “inputs”, then “outputs”. In the paper a bankruptcy risk model is developed in poorly formalized processes for the purpose of forecasting.

1. Zadeh L. A. (1976) The Concept of linguistic Variable and its Application to the Adoption of Approximate Solutions. Moscow, Mir Publ. 165 (in Russian).

2. Altunin A. E., Semukhin M. V. (2000) Models and Algorithms for Making Decisions in Fuzzy Conditions. Tyumen, Publishing House of the Tyumen State University. 352 (in Russian).

3. Knopov P. S., Maryanovich T. P. (2003) On Some Topical Problems of Assessing the Risk of Complex Systems Under Conditions of Insufficient Information. Cybernetics and System Analysis, (4), 125–137 (in Russian).

4. Solieva B. T. (2011) Models and Algorithms of Risk Assessment Based on Fuzzy Data Processing. Dissertation. Available at: http://diss.natlib.uz/ru-RU/Search/Query?Query=%D0%A1%D0%BE%D0%BB%D0%B8%D0%B5%D0%B2%D0%B0 (in Uzbek).

5. Mukhamedieva D. T., Solieva B. T. (2007) Creation of a Fuzzy Inference System Using a Fuzzy Knowledge Base. Bulletin of Tashkent State Technical University, (1), 25–29 (in Uzbek).

6. Nedosekin A. O. (2002) Fuzzy-Multiple Risk Analysis of Stock Investments. St. Petersburg. 181 (in Russian).

7. Solieva B. T. (2007) The Model of Predicting the Level of risk in Decision-Making in Conditions of Uncertainty. Cyber Questions, (176), 161–165 (in Uzbek).

8. Вekmuratov T. F. (2008) Poorly structured Decision-Making in Problems of Management of Risks. WCIS-2008, Tashkent, Uzbekistan, November 25–27, 96–106.

9. Kamilov M., Muxamedieva D., Solieva B. (2021) The Problem of Classifying and Managing Risk Situations in Poorly Formed Processes. 11th World Conference “Intelligent System for Industrial Automation”. WCIS 2020. Advances in Intelligent Systems and Computing, vol. 1323. Springer, Cham., 280–286, https://doi.org/10.1007/978-3-030-68004-6_36.

10. Muhamediyeva D. T., Niyozmatova N. A. (2019) Problems of Constructing Models of Intellectual Analysis of States of Weakly Formalizable Processes. Journal of Physics: Conference Series, 1210, 012102. https://doi.org/10.1088/1742-6596/1210/1/012102.

11. Bekmuratov T. F., Mukhamedieva D. T., Bobomuradov O. J. (2010) Models of Fuzzy Criteria and Algorithm for Ma-king Weakly Structured Decisions. Nauka i Obrazovanie: Nauchnoe Izdanie MGTU im. Baumana [Science and Education: Scientific Publication of MSTU Bauman], (7). Available at: https://cyberleninka.ru/article/n/modeli-ne-chetkih-kriteriev-i-algoritm-prinyatiya-slabostrukturirovan nyh-resheniy (in Russian).

12. Bokova N. (2016) The Essence of Bank Risk Management. Sovremennyi Nauchnyi Vestnik [Modern Scientific Herald], 5 (1), 170–176.

13. Savina Yu. I. (2012) Risk Management System at the Enterprise. Ekonomika i Upravlenie v XXI veke: Tendentsii Razvitiya [Economy and Management in the Xxi Century: Development Trends], (4), 186–192 (in Russian).

14. Nedosekin A. O., Maksimov O. B. (1999) A New Complex Indicator for Assessing the Financial Condition of an Enterprise. Voprosy Analiza Riska, (2–3). Available at: https://masters.donntu.ru/2005/kita/sroka/library/art3.htm (in Russian).

15. Fishbern P. (1978) Theory of Usefulness for Decision Making. Moscow, Nauka Publ. 352 (in Russian).

16. Primova H., Sotvoldiev D., Safarova L. (2018) Appro-aches to Solving the Problem of Risk Assessment with Fuzzy Initial Information. 12th International Scientific and Technical Conference “Dynamics of Systems, Mechanisms and Machines” (Dynamics 2018), art. No 8601485. https://doi.org/10.1109/dynamics.2018.8601485.

17. Gaibnazarova Z. T. (2023) Prospects for Improving the Forecasting of the Gross Domestic Product of Regions Based on Mathematical Models 2023 IX International Conference on Information Technology and Nanotechnology (ITNT). Samara, 1–4. https://doi.org/10.1109/ITNT57377.2023.10139074.

18. Mukhamedieva D. T., Solieva B. T. (2008) Model of Fuzzy Knowledge Base for the Problem of Forecasting of Agricultural Productivity. Proceeding of Fifth World Conference on Intelligent Systems for Industrial Automation (WCIS-2008), 178–181.

19. Bekmuratov T. F., Solieva B. T., Primova H. A. (2016) The adaptive algorithm of Fuzzy Synthesis. Chemical Technology. Control and Management, (5), 110–114.

20. Bekmuratov T. F., Solieva B. T., Primova H. A. (2016) The Adaptive Algorithm of Fuzzy Synthesis. Proceedings of Ninth World Conference “Intelligent Systems for Industrial Automation”, WCIS-2016, 25-27 October 2016, Tashkent, Uzbekistan, 289–293.

21. Mukhamedieva D. T., Solieva B. T. (2016) Solving the Problem of Fuzzy Multicriteria Optimization under Risk Conditions [Oral Report]. Twelfth International Asian School-Seminar “Problems of Optimization of Complex Systems” Novosibirsk, Akademgorodok, December 12–16, 2016. Available at: http://conf.nsc.ru/opcs2016/ru/opcs2016_docladu.

22. Bekmuratov T. F., Solieva B. T. (2018) Construction of Fuzzy Risk Assessment Models. Chemical Technology, Control and Management, (4-5), 24–28. https://doi.org/10.34920/2018.4-5.24-28.

23. Solieva B. T., Sotvoldiev D. M., Kxudayberganov A. (2018) Evaluation of a cotton in the Conditions of Fuzzy Initial Information. International Journal of Engineering &Technology, 7 (4.19), 374–377.

24. Solieva B. T., Juraev Z. Tasks of Fuzzy Parametric Programming. Problemy Optimizatsii Slozhnykh Sistem: Materialy XIV Mezhdunarodnoi Aziatskoi Shkoly-Seminara, Almaty, 20–31 Iyulya 2018 goda [Problems of Optimization of Complex Systems. Proceedings of the XIV International Asian School Seminar, Almaty, July 20–31, 2018]. Almaty, 218–225 (in Russian).

25. Bekmuratov T. F., Solieva B. T. Construction of Fuzzy Risk Assessment Models. Proceedings of the Tenth World Conference on Intelligent Systems for Industrial Automation Proceedings of WCIS-2018, 25–26 October 2018, Tashkent, Uzbekistan. Quadrat Verlag, 30–34.

26. Solieva B. T., Niyozmatova N. A. (2018) Development of the Algorithm of Estimation and Prediction of Risks in Weakly Formalizable Systems. Proceedings of the Tenth World Conference on Intelligent Systems for Industrial Automation Proceedings of WCIS-2018, 25–26 October 2018, Tashkent, Uzbekistan. Quadrat Verlag, 194–198.

27. Mukhamediyeva D., Solieva B. (2020) Construction of the Model of Crop Production Forecasting with Fuzzy Information. Pawar P., Ronge B., Balasubramaniam R., Vibhute A., Apte S. (eds.). Techno-Societal 2018. Sprin-ger, Cham., 187–196. https://doi.org/10.1007/978-3-030-16848-3_18.

28. Solieva B. T., Sotvoldiev D. M. (2019) Dialogue Algorithm of Adoption of Multi-Criterial Decisions in the Fuzzy Environment. 2019 International Conference on Information Science and Communications Technologies (ICISCT), Tashkent, Uzbekistan, 2019, 1–7.

29. Primova X. A. (2018) Methods and Algorithms for Solving Incorrect Problems of Constructing Fuzzy Models of Weakly Formed Processes. Dissertation. Available at: http://diss.natlib.uz/ru-RU/Search/Query?Query=%D0%9F%D1%80%D0%B8%D0%BC%D0%BE%D0%B2%D0%B0&RW.Author=&RW.Title=&RW.USTSpecialtyCodeTypeId=0&RW.KNSSpecialtyCodeTypeId=&RW.KNSSpecialtyCodeId=0&RW.UDC_BBKId=&RW.Year=&RW.USTSpecialtyCodeId=0 (in Uzbek.).

30. Muhamediyeva D. T., Urokov Sh. R. Application of Technology for Data Mining and Integration in Applied Tasks. Scientific Research of the SCO Countries: Synergy and Integration. Materials of the International Conference, April 10, 2020. Beijing, PRC, Part. 2, 222–226.

31. Gaibnazarov S. B., Gaibnazarova Z. T. (2020) Study of the Synthesis of the Stabilizer SG-1 by Phosphorylation of HIPAN. International Journal of Emerging Trends in Engineering Research, 8 (9), 5472–5476. https://doi.org/10.30534/ijeter/2020/91892020.

32. Gaibnazarova Z. T., Gaibnazarov S. B. (2020) Development of Education System in the Context of World Experience and National Specificity as an Investment in Human Capital. Journal of Critical Reviews, 7 (12), 302–308. https://doi.org/10.31838/jcr.07.12.57.

33. Raupov A. A., Gaibnazarov S. B., Aminov A. M., Nazarbekova D. K. Drilling mud for Prevention of Dispersion and Swelling of Clay Rocks of Various Genesis. Journal of Critical Reviews, 7 (9), 511–513. https://doi.org/10.31838/jcr.07.09.101.

34. Annakulov, T., Gaibnazarov, S., Askarov, A., Mamadie-va, L. Prospects for the Use of cyclic-Flow Technology for the Transportation of Rocks at the Yoshlik-1 Quarry of JSC Almalyk Mining and Metallurgical Combine. E3S Web of Conferences, 2023, 414, 06007. https://doi.org/10.1051/e3sconf/202341706007.

35. Annakulov T. J., Gaibnazarov S. B., Kuvandikov O. A., Otajonov F. O., Otajonov B. O. Development of a Methodology for Determining the Economic Efficiency of Cyclic-Flow Technology Schemes for Rock Mining Using Mobile Crushing and Reloading-Conveyor Complexes. AIP Conference Proceedings, 2022, 2432, 030115. https://doi.org/10.1063/5.0089668.