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Logical-linguistic method for assessing the risk of specialized lending (on the example of project financing)
 
PIIS042473880020291-9-1
DOI10.31857/S042473880020295-3
Publication type Article
Status Published
Authors
Anna Krasavtseva 
Affiliation: AO AB ROSSYA
Address: Russian Federation, Saint-Petersburg
Andrey Gorodetsky
Affiliation: Institute of Mechanical Engineering Problems
Address: Russian Federation, Санкт-петербург
Journal nameEkonomika i matematicheskie metody
EditionVolume 58 Issue 4
Pages83-91
Abstract

Credit risk assessment is associated with uncertainty of many factors which are hard to describe mathematically. This reduces the probability of success of specialized lending as well. Among the various risk assessment methods suitable for specialized lending risk assessment, it’s possible to single out a scenario approach that uses the methods of fuzzy set theory to calculate the values of membership functions. However, the problem of ranking the set of project implementation scenarios for which a loan is requested is not studied enough. Despite the existing technical difficulties of risk factors management, the ranking of the risks of project implementation can be carried out by comparing with the ones from the database using the logical-linguistic classification method. The purpose of the study is to develop new methods of credit risk assessment based on the logical and linguistic classification of specialized lending projects as well as algorithms for assessing the credit risk of the project with determining its rating. The definition of a Project is used according to the concept of Project Finance as one of the classes of specialized lending. Methods: the assignment of weight factors for all subclasses of credit requirements, the assignment of risk levels for all values of the types of criteria, introduction of a table of the risk value assessment or the creditworthiness of the project, the formation of a database of reference attribute strings for all classes of projects, generating attribute strings for the analyzed project, assigning the analyzed project to the benchmark using the logical-linguistic classification method, assigning risk degree values for the analyzed project for all values of the criteria (attribute) types of the selected reference project, calculating the credit risk assessment of the analyzed project and determining the rating of the analyzed project for decision making. Results: based on the proposed risk assessment method, an algorithm for calculating the risk assessment of lending to the analyzed project with determining its rating was developed. Practical significance: the results of the research can be used in the development of computer program allowing to accelerate risk assessment of project financing.

Keywordsspecialized lending, risk assessment, project financing, creditworthiness level, weight coefficients, significance coefficients, benchmarks, logical-linguistic classification, rating, decision making
AcknowledgmentThe research was carried out within the state assignment of Ministry of Science and Higher Education of the Russian Federation (theme no. 121112500304-4).
Received22.05.2022
Publication date07.12.2022
Number of characters35754
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1. Anfilatov V. S., Emelyanov A. A., Kukushkin A. A. (2019). System analysis in management. A. A. Emelyanov (ed.). Moscow: Finansy i Statistika (in Russian).
2. Borisova O. V., Malykh N. I., Oveshnikova L. V. (2018). Investments. Vol. 1. Moscow: Yurait (in Russian).
3. Boyarkin G. N., Sheveleva O. G., Tkachenko A. L. (2017). Choosing an investment object using analytical methods for risk assessment. Global World: Economics, Science, Technology, 9–2 (56), 23–27 (in Russian).
4. Vedmed I. Yu. (2017). Analysis methods of quantitative risk assessment of investment projects. In: “Probabilistic mathematical models of economic processes”. XII conference “Russian regions in the focus of change”. November 16–18 2017, Yekaterinburg, 52–61 (in Russian).
5. Volkov I. M., Gracheva M. V. (1998). Project analysis. Moscow: Banki i Birzhi, UNITI (in Russian).
6. Gareev A. Z. (2016). Risks in the implementation of investment projects. Innovative Science, 10 (1), 30–33 (in Russian).
7. Gorodetsky A. E., Tarasova I. L. (2010). Fuzzy mathematical modeling of poorly formalized processes and systems. Saint Petersburg: Izd-vo Politekhnicheskogo universiteta (in Russian).
8. Efremova E. A., Pryadkina V. A. (2014). Application of the Monte Carlo method for evaluating investment projects. Scientific community of students of the XXI century. Economic sciences: Sat. Art. based on materials XXVII Stud. International scientific-pract. conf., 12 (27), 237–244 (in Russian).
9. Zhegalkin I. I. (1928). Arithmetization of symbolic logic. Matematicheskii sbornik, 35, 3–4, 335 (in Russian).
10. Zade L. A. (1976). The concept of a linguistic variable and its application to making approximate decisions. Moscow: MIR (in Russian).
11. Korolkova E. M. (2013). Risk management: Project risk management. Tambov: FGBOU VPO “TSTU” (in Russian).
12. Kulik Yu.A., Volovich V. N., Privalov N. G., Kozlovsky A. N. (2012). Classification and qualitative assessment of risks of innovative projects. Notes of the Mining Institute, 197, 124–125 (in Russian).
13. Mirkin B. G. (1974). The problem of group choice. Moscow: Nauka (in Russian).
14. Moskvin V. A. (2016). Risks of investment projects. Moscow: Course, INFRA-M (in Russian).
15. Knight F. H. (2003). Risk, uncertainty and profit. Trans. from the English by M. Ya. Kazhdan. Moscow: Delo (in Russian).
16. Popova A. Yu. (2006). Risk assessment of an investment project. Scientific Journal of KubSAU, 19, 1–26 (in Russian).
17. Reshetnyak E. I. (2017). Methods for assessing investment risks in business planning. Business Inform, 12, 189–194 (in Russian).
18. Sazonov A. A., Sazonova M. V. (2016). Application of the Monte Carlo method for modeling economic risks in projects. Science and Modernity, 43, 228–232 (in Russian).
19. Solojentsev E. D. (2009). Management of risk and efficiency in the economy: A logical-probabilistic approach. Saint Petersburg: Publishing House of St. Petersburg State University, 242 (in Russian).
20. Stepanenko N. V., Kharitonov S. V. (2017). Microsoft Excel application possibilities in modeling risks of investment projects. Applied Informatics, 12, 1 (67), 137–142 (in Russian).
21. Tkachenko A. L., Sheveleva O. G. (2019). Risk assessment in investment project. Omsk Scientific Bulletin. Series Society. History. Modernity, 4, 2, 140–145 (in Russian).
22. Tsamutali S. A. (2013). Risk assessment of real investments. Еkonomika. Nalogi. Pravo (Economics, Taxes & Law), 4, 32–37 (in Russian).
23. Shtele E. A., Guseva M. A., Rudi L. A. (2016). Assessment method of investment projects efficiency in view of the risks. Siberian State Automobile and Highway Industry Journal, 6 (52), 135–140 (in Russian).
24. Gorodetskiy A. E., Tarasova I. L., Kurbanov V. G. (2021). Classification of images in decision making in the central nervous system of SEMS. In: Smart electromechanical systems, studies in systems. Book series “Decision and Control”, 352, 187–196. DOI: 10.1007/978-3-030-68172-2-15
25. Gorodetskiy A. E., Tarasova I. L., Kurbanov V. G. (2022). Assessment of UAV intelligence based on the results of computer modeling. In: Smart electromechanical systems, studies in systems. Book series “Decision and Control”, 419, 105–116. DOI: 10.1007/978-3-030-97004-8_8
26. Gorodetskiy A. E., Tarasova I. L. (2022). Modeling of systems of logical-linguistic classification of images. Smart electromechanical systems. In: Smart electromechanical systems, studies in systems. Book series “Decision and Control”, 419, 95–103. DOI: 10.1007/978-3-030-97004-8_7
27. Zadeh L. A. (1965). Fuzzy sets. Inform. Contr, 8, 338–353.

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