Научный журнал Моделирование, оптимизация и информационные технологииThe scientific journal Modeling, Optimization and Information Technology
cетевое издание
issn 2310-6018

COGNITIVE MODELING OF RISK MANAGEMENT PROCESSES POS-LOANING

Schukina N.A.   Goremykina G.I.  

UDC 519.71
DOI: 10.26102/2310-6018/2019.25.2.009

  • Abstract
  • List of references
  • About authors

The purpose of this study is to consider the possibility of applying modern intellectual methods and decision-making technologies to manage complex semi-structured systems. The research methodology is based on the use of a fuzzy cognitive approach, cause-effect relationships analysis, dynamic modeling, stability analysis of the system under consideration, and scenarios analysis. The study proposes a fuzzy cognitive approach to modeling the risk management system of POS-loaning processes in a commercial Bank. The modeling system is represented as a fuzzy oriented weighted multigraph with a pulse effect transmitted through it. The modeling process is implemented in the form of the following stages: goal definition; fuzzy cognitive map construction; impulse processes for dynamic modeling; the situation scenarios analysis and the choice of the best. The developed management system model serves as the basis for the trends analysis in the development of various situations in the POS-loaning segment. It allows to predict and simulate the behavior strategies in response to external stimuli, and to determine the governance trajectory that reduce internal risks processes POSloaning commercial Bank. Fuzzy cognitive approach is an effective tool to support decision-making in the activities a commercial Bank risk management and can be used to model and analyze the functioning and other poorly structured socio-economic systems.

1. Prezenski S., Brechmann A., Wolff S., Russwinkel N. A Cognitive Modeling Approach to Strategy Formation in Dynamic Decision Making. Frontiers in Psychology. 2017. Vol. 8, P. 1335.

2. Thomson R., Lebiere C., Anderson J.R., Staszewski J. A general instancebased learning framework for studying intuitive decision-making in a cognitive architecture. Journal of Applied Research in Memory and Cognition. 2015. No. 4. Pp. 180-190.

3. Банки-лидеры в сегменте POS-кредитования. Электронный рерурс. URL: https://frankrg.com/1262

4. COSO 2017. "Conceptual framework for enterprise risk management: integration with strategy and business management" COSO 2017. URL: https://www.coso.org/Pages/erm.aspx

5. Lanskov P.M., Zenkovich E.V. Integrated system of internal control and risk management and internal audit in non-credit financial organizations. Money and Credit. 2017. No. 2. Pp. 34-36.

6. Lvovich Y.E., Sapozhnikov G.P. Intellectualization of resource-efficiency management of a non-profit educational organization with the use of monitoring and rating information. Modeling, optimization and information technologies. 2017. No. 4 (19). Pp. 21.

7. Sun R., Ling C.X. Computational Cognitive Modeling, the Source of Power, and Other Related Issues. Artificial Intelligence. 1998. Vol.19. No. 2. Pp. 113- 120.

8. Vasantha Kandasamy W.B., Smarandache F. Fuzzy Cognitive Maps and Neutrosophic Cognitive Maps. 2003. 212 p.

9. Khrustalev Ye.Yu. Cognitive Model of the Russian Banking System. Economics and mathematical methods. 2011. Vol. 47. No. 2. Pp. 117–127.

10. Kulba V., Shelkov A., Chernov I., & Zaikin O. Scenario analysis in the management of regional security and social stability. Intelligent Systems Reference Library. 2016. Vol. 98. Pp. 249-268.

11. Ginis L.A. The development of toolkit of cognitive modelling for research of difficult systems. Engineering journal of Don. 2013. No. 3 (26). Pp. 66.

12. Butenko E.D. Artificial intelligence in banks today: experience and perspectives. Finance and Credit. 2018, vol. 24, iss. 3, pp. 143–153.

13. Kosko B. Fuzzy Cognitive Maps. International Journal of Man-Machine Studies. 1986. Vol. 24. Pp. 65-75.

14. Tsadiras A.K. Comparing the inference capabilities of binary, trivalent and sigmoid fuzzy cognitive maps// Information Sciences.2008. Vol. 178, Iss. 20. Pp. 3880-3894.

15. Axelrod R. The Structure of Decision: Cognitive Maps of Political Elites. Princeton University Press. 1976. 395 p.

16. Roberts F.S. Discrete mathematical models with applications to social, biological and environmental problems. Moscow. Nauka. 1986. 496 p.

17. Chepurnyh N.V., Novoselov A.L. Economy and ecology: development, disaster. Moscow. Nauka. 1996. 272 p.

18. Chefranova M.А. Building a cognitive model of crediting and the development of the structural unit of management decisions on its basis. Bulletin of Adyghe State University. Series 5: Economy. 2011. No. 1. Pp. 201-208.

19. Christoforou A., Andreou A.S. A framework for static and dynamic analysis of multi-layer fuzzy cognitive maps. Neurocomputing. 2017. Vol. 232. Pp.133-145.

20. Maruyama M. The Second Cybernetics: Deviation-Amplifying Mutual Causal Process, Amer. Scientist. 1963. 51. Pp. 164-179.

21. Roberts F.S., Brown T.A. Signed Digraphs and the Energy Crisis, Amer. Math. Monthly. 82. 1975. Pp. 577-594.

22. Chernikova L.I., Evstifeeva S.A. Performance indicators of the banking sector during the crisis. Financial analytics: problems and solutions. 2017. Vol. 10. No. 5 (335). Pp. 506-517.

23. Komarov A.V., Pereverzeva A.A. Banking system of modern Russia: challenges and realities. Economy. Business. Banks. 2017. No. 1 (18). Pp. 65- 75.

Schukina Natalya Aleksandrovna
Candidate of Technical Sciences
Email: shchukinan@yandex.ru

Plekhanov Russian University of Economics
Financial University under the Government of the Russian Federation

Moscow, Russian Federation

Goremykina Galina Ivanovna
Candidate of Physical and Mathematical Sciences
Email: g_iv.05@mail.ru

Plekhanov Russian University of Economics

Moscow, Russian Federation

Keywords: fuzzy cognitive modeling, fuzzy oriented weighted graph, pos-loaning, risk management system, scenario approach

For citation: Schukina N.A. Goremykina G.I. COGNITIVE MODELING OF RISK MANAGEMENT PROCESSES POS-LOANING. Modeling, Optimization and Information Technology. 2019;7(2). Available from: https://moit.vivt.ru/wp-content/uploads/2019/05/ShchukinaGoremykina_2_19_1.pdf DOI: 10.26102/2310-6018/2019.25.2.009 (In Russ).

95

Full text in PDF