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<article article-type="research-article" dtd-version="1.3" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xml:lang="ru"><front><journal-meta><journal-id journal-id-type="publisher-id">finance</journal-id><journal-title-group><journal-title xml:lang="ru">Финансы: теория и практика/Finance: Theory and Practice</journal-title><trans-title-group xml:lang="en"><trans-title>Finance: Theory and Practice</trans-title></trans-title-group></journal-title-group><issn pub-type="ppub">2587-5671</issn><issn pub-type="epub">2587-7089</issn><publisher><publisher-name>Financial University under The Government of Russian Federation</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="doi">10.26794/2587-5671-2022-26-4-139-156</article-id><article-id custom-type="elpub" pub-id-type="custom">finance-1732</article-id><article-categories><subj-group subj-group-type="heading"><subject>Research Article</subject></subj-group><subj-group subj-group-type="section-heading" xml:lang="ru"><subject>АНАЛИЗ И УПРАВЛЕНИЕ РИСКАМИ</subject></subj-group><subj-group subj-group-type="section-heading" xml:lang="en"><subject>ANAlYSIS AND RISK MANAGEMENT</subject></subj-group></article-categories><title-group><article-title>Прямое нечеткое оценивание «цепочек» финансовых рисков организации</article-title><trans-title-group xml:lang="en"><trans-title>Direct Fuzzy Evaluation of Financial Risk “Chains” of an Organisation</trans-title></trans-title-group></title-group><contrib-group><contrib contrib-type="author" corresp="yes"><contrib-id contrib-id-type="orcid">https://orcid.org/0000-0002-2226-0204</contrib-id><name-alternatives><name name-style="eastern" xml:lang="ru"><surname>Фомченкова</surname><given-names>Л. В.</given-names></name><name name-style="western" xml:lang="en"><surname>Fomchenkova</surname><given-names>L. V.</given-names></name></name-alternatives><bio xml:lang="ru"><p>Лариса Владимировна Фомченкова - доктор экономических наук, профессор кафедры информационных технологий в экономике и управлении</p><p>Смоленск</p></bio><bio xml:lang="en"><p>Smolensk</p></bio><email xlink:type="simple">l.fomchenkova@gmail.com</email><xref ref-type="aff" rid="aff-1"/></contrib><contrib contrib-type="author" corresp="yes"><contrib-id contrib-id-type="orcid">https://orcid.org/0000-0003-2413-9827</contrib-id><name-alternatives><name name-style="eastern" xml:lang="ru"><surname>Харламов</surname><given-names>П. С.</given-names></name><name name-style="western" xml:lang="en"><surname>Kharlamov</surname><given-names>P. S.</given-names></name></name-alternatives><bio xml:lang="ru"><p>Павел Сергеевич Харламов - студент</p><p>Смоленск</p></bio><bio xml:lang="en"><p>Pavel S. Kharlamov - student</p><p>Smolensk</p></bio><email xlink:type="simple">pavel_kharlamov.mp67@mail.ru</email><xref ref-type="aff" rid="aff-1"/></contrib><contrib contrib-type="author" corresp="yes"><contrib-id contrib-id-type="orcid">https://orcid.org/0000-0001-5964-8029</contrib-id><name-alternatives><name name-style="eastern" xml:lang="ru"><surname>Мелихов</surname><given-names>К. С.</given-names></name><name name-style="western" xml:lang="en"><surname>Melikhov</surname><given-names>K. S.</given-names></name></name-alternatives><bio xml:lang="ru"><p>Кирилл Сергеевич Мелихов - студент</p><p>Москва</p></bio><bio xml:lang="en"><p>Kirill S. Melikhov - student</p><p>Moscow</p></bio><email xlink:type="simple">ks.melichov@gmail.com</email><xref ref-type="aff" rid="aff-2"/></contrib></contrib-group><aff-alternatives id="aff-1"><aff xml:lang="ru"><institution>Филиал Национального исследовательского университета «МЭИ» в Смоленске</institution><country>Россия</country></aff><aff xml:lang="en"><institution>Branch of the National Research University Moscow Power Engineering Institute in Smolensk</institution><country>Russian Federation</country></aff></aff-alternatives><aff-alternatives id="aff-2"><aff xml:lang="ru"><institution>Финансовый университет</institution><country>Россия</country></aff><aff xml:lang="en"><institution>Financial University</institution><country>Russian Federation</country></aff></aff-alternatives><pub-date pub-type="collection"><year>2022</year></pub-date><pub-date pub-type="epub"><day>11</day><month>09</month><year>2022</year></pub-date><volume>26</volume><issue>4</issue><fpage>139</fpage><lpage>156</lpage><permissions><copyright-statement>Copyright &amp;#x00A9; Фомченкова Л.В., Харламов П.С., Мелихов К.С., 2022</copyright-statement><copyright-year>2022</copyright-year><copyright-holder xml:lang="ru">Фомченкова Л.В., Харламов П.С., Мелихов К.С.</copyright-holder><copyright-holder xml:lang="en">Fomchenkova L.V., Kharlamov P.S., Melikhov K.S.</copyright-holder><license xml:lang="ru" license-type="creative-commons-attribution" xlink:href="https://creativecommons.org/licenses/by/4.0/" xlink:type="simple"><license-p>Данная работа распространяется под лицензией Creative Commons Attribution 4.0.</license-p></license><license xml:lang="en" license-type="creative-commons-attribution" xlink:href="https://creativecommons.org/licenses/by/4.0/" xlink:type="simple"><license-p>This work is licensed under a Creative Commons Attribution 4.0 License.</license-p></license></permissions><self-uri xlink:href="https://financetp.fa.ru/jour/article/view/1732">https://financetp.fa.ru/jour/article/view/1732</self-uri><abstract><p>Объектом исследования выступает диагностика и оценка финансовых рисков с целью создания эффективного риск-менеджмента. Предметом исследования является методика нечеткого прямого оценивания «цепочек» финансовых рисков организаций. Актуальность проблематики обусловлена, с одной стороны, динамичной и хаотичной как макросредой, так и бизнес-средой организаций, с другой — недостатками применяемых аналитических и экспертных методов оценки финансовых рисков. Первые при этом подразумевают статистическую обработку данных и оперируют количественными метриками. Для вторых трудность заключается в невозможности их применения на коротком временном интервале. С позиции оперативного риск-менеджмента заслуживают особого внимания финансовые риски, поскольку от них зависит эффективное функционирование всей организации. Цель исследования заключается в формировании методики нечеткого прямого оценивания «цепочек» финансовых рисков организаций. Использованы методы математического прогнозирования, нечеткого моделирования, расчета финансово-экономических показателей, экспертной оценки рисков. Предлагаемая методика состоит из 12 этапов, начинается с анализа бизнес-процессов и идентификации финансовых рисков организации. Основным ее этапом является построение нечеткой оценочной модели и расчет показателей: вероятность возникновения и реализации рисков и рисковых ситуаций «цепочки» финансовых рисков, степень уверенности проводимых расчетов. Конечный этап методики являет собой анализ полученных результатов с целью корректировки выбранной стратегии развития организации, выбора методов управления выявленными финансовыми рисками, несущими наиболее существенные финансово-экономические потери. Сделан вывод о том, что разработанная методика позволяет с высокой точностью оценить угрозу возникновения определенной «цепочки» рисков и потери от реализации конкретных рисковых ситуаций для любой организации в условиях динамичных изменений внутренних и внешних элементов бизнес-среды. Ее преимуществом следует считать сопоставимость точности проводимой оценки и небольших затрат на моделирование.</p></abstract><trans-abstract xml:lang="en"><p>The object of the research is the diagnosis and evaluation of financial risks in order to create an effective risk management policy. The subject of the research is the methodology of direct fuzzy evaluation of financial risk “chains” of an organisation. The relevance of the problem is due, on the one hand, to the dynamic and chaotic macro-environment and the business environment of organisations, on the other hand, to the drawback of the analytical and expert methods used to assess financial risks. The former, moreover, imply statistical data processing and operate with quantitative measures. For the latter, the difficulty is the impossibility of their application in a short time interval. From the perspective of operational risk management, financial risks deserve special attention since the effective operation of the entire organisation depends on them. The purpose of the research is to form a methodology for direct fuzzy evaluation of financial risk “chains” of an organisation. The authors apply the methods of mathematical forecasting, fuzzy modelling, calculation of financial and economic indicators, and expert risk assessment. The proposed methodology consists of 12 stages, beginning with the analysis of business processes and the identification of financial risks of the organisation. The main stage is the construction of a fuzzy evaluation model and the calculation of indicators: the probability of occurrence and realization of risks and risky situations of the financial risk “chains”, and the degree of confidence of the calculations conducted. The final stage of the methodology is an analysis of the results obtained to adjust the selected development strategy of the organisation, and the choice of methods for managing identified financial risks bearing the most significant financial and economic losses. The authors conclude the developed methodology allows to accurately assess the threat of a certain risk “chain” and losses from the implementation of specific risk situations for any organisation in the conditions of dynamic changes in internal and external elements of the business environment. The advantage of the methodology should be considered in the comparability of the accuracy of the evaluation and the low cost of modelling.</p></trans-abstract><kwd-group xml:lang="ru"><kwd>финансовые риски</kwd><kwd>риск-менеджмент</kwd><kwd>«цепочка» рисков</kwd><kwd>нечеткая оценочная модель</kwd><kwd>нечеткое прямое оценивание</kwd><kwd>динамическая среда</kwd><kwd>финансово-экономические потери</kwd><kwd>бизнес-процессы организации</kwd></kwd-group><kwd-group xml:lang="en"><kwd>финансовые риски</kwd><kwd>риск-менеджмент</kwd><kwd>«цепочка» рисков</kwd><kwd>нечеткая оценочная модель</kwd><kwd>нечеткое прямое оценивание</kwd><kwd>динамическая среда</kwd><kwd>финансово-экономические потери</kwd><kwd>бизнес-процессы организации</kwd></kwd-group></article-meta></front><back><ref-list><title>References</title><ref id="cit1"><label>1</label><citation-alternatives><mixed-citation xml:lang="ru">Глушенко С. А., Долженко А. И. Система нечеткого моделирования рисков инвестиционно-строительных проектов. Бизнес-информатика. 2015;(2):48–58.</mixed-citation><mixed-citation xml:lang="en">Glushenko S. A., Doljenko A. I. Fuzzy modelling of risks in investment and construction projects. Biznesinformatika = Business Informatics. 2015;(2):48–58. (In Russ.).</mixed-citation></citation-alternatives></ref><ref id="cit2"><label>2</label><citation-alternatives><mixed-citation xml:lang="ru">Федулов Я. А. Методы и программные средства поддержки выбора решений на основе прямого и обратного нечеткого оценивания: дис. … канд. техн. наук. Смоленск: Филиал НИУ МЭИ; 2015. 157 с.</mixed-citation><mixed-citation xml:lang="en">Fedulov Y. A. Decision support methods and software tools based on direct and inverse fuzzy evaluation. Cand. tech. sci. diss. Smolensk: MPEI, Smolensk Branch; 2015. 157 p. (In Russ.).</mixed-citation></citation-alternatives></ref><ref id="cit3"><label>3</label><citation-alternatives><mixed-citation xml:lang="ru">Pena A., Patino A., Chiclana F., Caraffini F., Gongora M., Gonzalez-Ruiz J.D., Duque-Grisales E. Fuzzy convolutional deep-learning model to estimate the operational risk capital using multi-source risk events. Applied Soft Computing. 2021;107:107381. DOI: 10.1016/j.asoc.2021.107381</mixed-citation><mixed-citation xml:lang="en">Pena A., Patino A., Chiclana F., Caraffini F., Gongora M., Gonzalez-Ruiz J.D., Duque-Grisales E. Fuzzy convolutional deep-learning model to estimate the operational risk capital using multi-source risk events. Applied Soft Computing. 2021;107:107381. DOI: 10.1016/j.asoc.2021.107381</mixed-citation></citation-alternatives></ref><ref id="cit4"><label>4</label><citation-alternatives><mixed-citation xml:lang="ru">Hasan N. I., Elghareeb H., Farahat F. F., AboElfotouh A. A proposed fuzzy model for reducing the risk of insolvent loans in the credit sector as applied in Egypt. International Journal of Fuzzy Logic and Intelligent Systems. 2021;21(1):66–75. DOI: 10.5391/IJFIS.2021.21.1.66</mixed-citation><mixed-citation xml:lang="en">Hasan N. I., Elghareeb H., Farahat F. F., AboElfotouh A. A proposed fuzzy model for reducing the risk of insolvent loans in the credit sector as applied in Egypt. International Journal of Fuzzy Logic and Intelligent Systems. 2021;21(1):66–75. DOI: 10.5391/IJFIS.2021.21.1.66</mixed-citation></citation-alternatives></ref><ref id="cit5"><label>5</label><citation-alternatives><mixed-citation xml:lang="ru">Фомченкова Л. В. Динамическая концепция стратегического анализа организационно-экономического развития промышленного предприятия. Дис. … докт. экон. наук. Орел: ОГУ; 2014. 347 с.</mixed-citation><mixed-citation xml:lang="en">Fomchenkova L. V. The dynamic concept of strategic analysis of the organisational and economic development of an industrial enterprise. Doct. econ. sci. diss. Orel: Orel State University; 2014. 347 p. (In Russ.).</mixed-citation></citation-alternatives></ref><ref id="cit6"><label>6</label><citation-alternatives><mixed-citation xml:lang="ru">Zhang J., Liu T., Li Q., Zhang T. Research on computer aided risk evaluation model through fuzzy hierarchical analysis. Journal of Physics: Conference Series. 2021;2033:012020. DOI: 10.1088/1742–6596/2033/1/012020</mixed-citation><mixed-citation xml:lang="en">Zhang J., Liu T., Li Q., Zhang T. Research on computer aided risk evaluation model through fuzzy hierarchical analysis. Journal of Physics: Conference Series. 2021;2033:012020. DOI: 10.1088/1742–6596/2033/1/012020</mixed-citation></citation-alternatives></ref><ref id="cit7"><label>7</label><citation-alternatives><mixed-citation xml:lang="ru">Nie T., Feng F., Guo Y. Financial risk evaluation of automobile supply chain based on improved VIKOR method. In: 8th Int. conf. on automation and logistics (ICAL 2021). (Chongqing, June 3–5, 2021). New York: ACM; 2021:44–48. (ACM International Conference Proceeding Series). DOI: 10.1145/3477543.3477547</mixed-citation><mixed-citation xml:lang="en">Nie T., Feng F., Guo Y. Financial risk evaluation of automobile supply chain based on improved VIKOR method. In: 8th Int. conf. on automation and logistics (ICAL 2021). (Chongqing, June 3–5, 2021). New York: ACM; 2021:44–48. (ACM International Conference Proceeding Series). DOI: 10.1145/3477543.3477547</mixed-citation></citation-alternatives></ref><ref id="cit8"><label>8</label><citation-alternatives><mixed-citation xml:lang="ru">Ma X., Chen J., Sun Y., Zhu Z. Assistant reference point guided evolutionary algorithm for manyobjective fuzzy portfolio selection. Swarm and Evolutionary Computation. 2021;62:100862. DOI: 10.1016/j.swevo.2021.100862</mixed-citation><mixed-citation xml:lang="en">Ma X., Chen J., Sun Y., Zhu Z. Assistant reference point guided evolutionary algorithm for manyobjective fuzzy portfolio selection. Swarm and Evolutionary Computation. 2021;62:100862. DOI: 10.1016/j.swevo.2021.100862</mixed-citation></citation-alternatives></ref><ref id="cit9"><label>9</label><citation-alternatives><mixed-citation xml:lang="ru">Zhu M., Meng Z. Fuzzy comprehensive evaluation model of M&amp;A synergy based on transfer learning graph neural network. Computational Intelligence and Neuroscience. 2021;2021:6516722. DOI: 10.1155/2021/6516722</mixed-citation><mixed-citation xml:lang="en">Zhu M., Meng Z. Fuzzy comprehensive evaluation model of M&amp;A synergy based on transfer learning graph neural network. Computational Intelligence and Neuroscience. 2021;2021:6516722. DOI: 10.1155/2021/6516722</mixed-citation></citation-alternatives></ref><ref id="cit10"><label>10</label><citation-alternatives><mixed-citation xml:lang="ru">Huang Y. Construction and analysis of green investment risk evaluation index system based on information entropy fuzzy hierarchical analysis model. Wireless Communications and Mobile Computing. 2021;2021:4850321. DOI: 10.1155/2021/4850321</mixed-citation><mixed-citation xml:lang="en">Huang Y. Construction and analysis of green investment risk evaluation index system based on information entropy fuzzy hierarchical analysis model. Wireless Communications and Mobile Computing. 2021;2021:4850321. DOI: 10.1155/2021/4850321</mixed-citation></citation-alternatives></ref><ref id="cit11"><label>11</label><citation-alternatives><mixed-citation xml:lang="ru">Chen Y., Yao D., Duan Y. Complexity of the analysis of financial cloud based on fuzzy theory in the wisdom of sustainable urban development. Complexity. 2021;2021:3444437. DOI: 10.1155/2021/3444437</mixed-citation><mixed-citation xml:lang="en">Chen Y., Yao D., Duan Y. Complexity of the analysis of financial cloud based on fuzzy theory in the wisdom of sustainable urban development. Complexity. 2021;2021:3444437. DOI: 10.1155/2021/3444437</mixed-citation></citation-alternatives></ref><ref id="cit12"><label>12</label><citation-alternatives><mixed-citation xml:lang="ru">Qu Q., Liu C., Bao X. E-commerce enterprise supply chain financing risk assessment based on linked data mining and edge computing. Mobile Information Systems. 2021;2021:9938325. DOI: 10.1155/2021/9938325</mixed-citation><mixed-citation xml:lang="en">Qu Q., Liu C., Bao X. E-commerce enterprise supply chain financing risk assessment based on linked data mining and edge computing. Mobile Information Systems. 2021;2021:9938325. DOI: 10.1155/2021/9938325</mixed-citation></citation-alternatives></ref><ref id="cit13"><label>13</label><citation-alternatives><mixed-citation xml:lang="ru">Li L., Li H. Analysis of financing risk and innovation motivation mechanism of financial service industry based on Internet of things. Complexity. 2021;2021:5523290. DOI: 10.1155/2021/5523290</mixed-citation><mixed-citation xml:lang="en">Li L., Li H. Analysis of financing risk and innovation motivation mechanism of financial service industry based on Internet of things. Complexity. 2021;2021:5523290. DOI: 10.1155/2021/5523290</mixed-citation></citation-alternatives></ref><ref id="cit14"><label>14</label><citation-alternatives><mixed-citation xml:lang="ru">Ding Q. Risk early warning management and intelligent real-time system of financial enterprises based on fuzzy theory. Journal of Intelligent and Fuzzy Systems. 2021;40(4):6017–6027. DOI: 10.3233/JIFS-189441</mixed-citation><mixed-citation xml:lang="en">Ding Q. Risk early warning management and intelligent real-time system of financial enterprises based on fuzzy theory. Journal of Intelligent and Fuzzy Systems. 2021;40(4):6017–6027. DOI: 10.3233/JIFS-189441</mixed-citation></citation-alternatives></ref><ref id="cit15"><label>15</label><citation-alternatives><mixed-citation xml:lang="ru">Xuan F. Regression analysis of supply chain financial risk based on machine learning and fuzzy decision model. Journal of Intelligent and Fuzzy Systems. 2021;40(4):6925–6935. DOI: 10.3233/JIFS‑189523</mixed-citation><mixed-citation xml:lang="en">Xuan F. Regression analysis of supply chain financial risk based on machine learning and fuzzy decision model. Journal of Intelligent and Fuzzy Systems. 2021;40(4):6925–6935. DOI: 10.3233/JIFS-189523</mixed-citation></citation-alternatives></ref><ref id="cit16"><label>16</label><citation-alternatives><mixed-citation xml:lang="ru">Zong Q. Research on fuzzy evaluation model of enterprise financial risk based on low-carbon economic environment. Fresenius Environmental Bulletin. 2020;29(11):9872–9879.</mixed-citation><mixed-citation xml:lang="en">Zong Q. Research on fuzzy evaluation model of enterprise financial risk based on low-carbon economic environment. Fresenius Environmental Bulletin. 2020;29(11):9872–9879.</mixed-citation></citation-alternatives></ref><ref id="cit17"><label>17</label><citation-alternatives><mixed-citation xml:lang="ru">Li W. Financial crisis warning of financial robot based on artificial intelligence. Revue d’Intelligence Artificielle. 2020;34(5):553–561. DOI: 10.18280/ria.340504</mixed-citation><mixed-citation xml:lang="en">Li W. Financial crisis warning of financial robot based on artificial intelligence. Revue d’Intelligence Artificielle. 2020;34(5):553–561. DOI: 10.18280/ria.340504</mixed-citation></citation-alternatives></ref><ref id="cit18"><label>18</label><citation-alternatives><mixed-citation xml:lang="ru">Yoshida Y. Portfolio optimization in fuzzy asset management with coherent risk measures derived from risk averse utility. Neural Computing and Applications. 2020;32(15):10847–10857. DOI: 10.1007/s00521–018–3683-y</mixed-citation><mixed-citation xml:lang="en">Yoshida Y. Portfolio optimization in fuzzy asset management with coherent risk measures derived from risk averse utility. Neural Computing and Applications. 2020;32(15):10847–10857. DOI: 10.1007/s00521–018– 3683-y</mixed-citation></citation-alternatives></ref><ref id="cit19"><label>19</label><citation-alternatives><mixed-citation xml:lang="ru">Peng X., Huang H. Fuzzy decision making method based on CoCoSo with critic for financial risk evaluation. Technological and Economic Development of Economy. 2020;26(4):695–724. DOI: 10.3846/tede.2020.11920</mixed-citation><mixed-citation xml:lang="en">Peng X., Huang H. Fuzzy decision making method based on CoCoSo with critic for financial risk evaluation. Technological and Economic Development of Economy. 2020;26(4):695–724. DOI: 10.3846/tede.2020.11920</mixed-citation></citation-alternatives></ref><ref id="cit20"><label>20</label><citation-alternatives><mixed-citation xml:lang="ru">Zhang J. Investment risk model based on intelligent fuzzy neural network and Va R. Journal of Computational and Applied Mathematics. 2020;371:112707. DOI: 10.1016/j.cam.2019.112707</mixed-citation><mixed-citation xml:lang="en">Zhang J. Investment risk model based on intelligent fuzzy neural network and Va R. Journal of Computational and Applied Mathematics. 2020;371:112707. DOI: 10.1016/j.cam.2019.112707</mixed-citation></citation-alternatives></ref><ref id="cit21"><label>21</label><citation-alternatives><mixed-citation xml:lang="ru">Song P., Li L., Huang D., Wei Q., Chen X. Loan risk assessment based on Pythagorean fuzzy analytic hierarchy process. Journal of Physics: Conference Series. 2020;1437:012101. DOI: 10.1088/1742–6596/1437/1/012101</mixed-citation><mixed-citation xml:lang="en">Song P., Li L., Huang D., Wei Q., Chen X. Loan risk assessment based on Pythagorean fuzzy analytic hierarchy process. Journal of Physics: Conference Series. 2020;1437:012101. DOI: 10.1088/1742–6596/1437/1/012101</mixed-citation></citation-alternatives></ref><ref id="cit22"><label>22</label><citation-alternatives><mixed-citation xml:lang="ru">Liu S., Ma D. Risk evaluation of intellectual property pledge financing based on fuzzy analytical network process. Journal of Intelligent and Fuzzy Systems. 2020;38(6):6785–6793. DOI: 10.3233/JIFS-179756</mixed-citation><mixed-citation xml:lang="en">Liu S., Ma D. Risk evaluation of intellectual property pledge financing based on fuzzy analytical network process. Journal of Intelligent and Fuzzy Systems. 2020;38(6):6785–6793. DOI: 10.3233/JIFS-179756</mixed-citation></citation-alternatives></ref></ref-list><fn-group><fn fn-type="conflict"><p>The authors declare that there are no conflicts of interest present.</p></fn></fn-group></back></article>
