Cognitive Modeling and Formation of the Knowledge Base of the Information System for Assessing the Rating of Enterprises


  • Olena Kryvoruchko State University of Trade and Economics
  • Alona Desyatko State University of Trade and Economics
  • Igor Karpunin State University of Trade and Economics
  • Dmytro Hnatchenko State University of Trade and Economics
  • Myroslav Lakhno National University of Life and Environmental Sciences of Ukraine
  • Feruza Malikova Almaty Technological University
  • Ayezhan Turdaliev Kazakh University of Railways and Transportation


A mathematical model is proposed that makes it possible to describe in a conceptual and functional aspect the formation and application of a knowledge base (KB) for an intelligent information system (IIS). This IIS is developed to assess the financial condition (FC) of the company. Moreover, for circumstances related to the identification of individual weakly structured factors (signs). The proposed model makes it possible to increase the understanding of the analyzed economic processes related to the company's financial system. An iterative algorithm for IIS has been developed that implements a model of cognitive modeling. The scientific novelty of the proposed approach lies in the fact that, unlike existing solutions, it is possible to adjust the structure of the algorithm depending on the characteristics of a particular company, as well as form the information basis for the process of assessing the company's FC and the parameters of the cognitive model.



Swiderski, B., Kurek, J., & Osowski, S. (2012). Multistage classification by using logistic regression and neural networks for assessment of financial condition of company. Decision Support Systems, 52(2), 539-547.

Kostyukova, E. I., Yakovenko, V. S., et al. (2017). Evaluation of the company's financial condition from the position of different groups of stakeholders. Revista ESPACIOS, 38(33).

Rafiei, F. M., Manzari, S. M., & Bostanian, S. (2011). Financial health prediction models using artificial neural networks, genetic algorithm and multivariate discriminant analysis: Iranian evidence. Expert systems with applications, 38(8), 10210-10217.

Liang, L., & Wu, D. (2005). An application of pattern recognition on scoring Chinese corporations financial conditions based on backpropagation neural network. Computers & Operations Research, 32(5), 1115-1129.

Borodin, A., Mityushina, I., Streltsova, E., Kulikov, A., Yakovenko, I., & Namitulina, A. (2021). Mathematical modeling for financial analysis of an enterprise: Motivating of not open innovation. Journal of Open Innovation: Technology, Market, and Complexity, 7(1), 79.

Borodin, A. I., Tatuev, A. A., Shash, N. N., Lyapuntsova, E. V., & Rokotyanskaya, V. V. (2015). Economic-mathematical model of building a company's potential. Asian Social Science, 11(14), 198.

Loginovskiy, O. V., Dranko, O. I., & Hollay, A. V. (2018). Mathematical models for decision-making on strategic management of industrial enterprise in conditions of instability. In Leipzig: CEUR Workshop Proceedings (Vol. 2093, pp. 1-12).

Iremadze, E. O., & Antonova, N. A. (2016). Prediction of financial indicators of a company using mathematical methods. International Research Journal, (11-1(53)), 36-38.

Dziamulych, M., Moskovchuk, A., Vavdiiuk, N., Kovalchuk, N., Kulynych, M., & Naumenko, N. (2021). Analysis and economic and mathematical modeling in the process of forecasting the financial capacity of milk processing enterprises of the agro-industrial sector: a case study of Volyn region, Ukraine. Scientific Papers Series "Management, Economic Engineering in Agriculture and Rural Development, 21(1), 259-272.

Lakhno, V., Adilzhanova, S., Ydyryshbayeva, M., Turgynbayeva, A., Kryvoruchko, O., Chubaievskyi, V., Desiatko, A. Adaptive Monitoring of Companies' Information Security (2023) International Journal of Electronics and Telecommunications, 69 (1), pp. 75-82.

Lakhno, V., Mazaraki, A., Kasatkin, D., Kryvoruchko, O., Khorolska, K., Chubaievskyi, V. Models and Algorithms for Optimization of the Backup Equipment for the Intelligent Automated Control System Smart City (2023) Lecture Notes in Networks and Systems, 383, pp. 749-762.

Lakhno, V., Akhmetov, B., Smirnov, O., Chubaievskyi, V., Khorolska, K., Bebeshko, B. Selection of a Rational Composition of Information Protection Means Using a Genetic Algorithm (2023) Lecture Notes on Data Engineering and Communications Technologies, 131, pp. 21-34.

Alshatti, Ali Sulieman. "The effect of credit risk management on financial performance of the Jordanian commercial banks." Investment management and financial innovations 12.1 (2015): 338-345.

Akhmetov, B., Lakhno, V., Boiko, Y., Mishchenko, A. Designing a decision support system for the weakly formalized problems in the provision of cybersecurity (2017) Eastern-European Journal of Enterprise Technologies, 1 (2-85), pp. 4-15.

Olena Shkarupa, Viktoriia Boronos, Dmytro Vlasenko and Kostiantyn Fedchenko (2021). Multilevel transfer of innovations: Cognitive modeling to decision support in managing the economic growth. Problems and Perspectives in Management, 19(1), 151-162. doi:10.21511/ppm.19(1).2021.13

Axelrod, R. (1976). The analysis of cognitive maps. In R. Axelrod (Ed.), Structure of Decision: The Cognitive Maps of Political Elites (pp. 55-73). Princeton, NY: Princeton University Press.

Valeriy, L., Andrii, S., Vladyslav, K., Elena, P., Anatolii, C., Nataliia, U. (2022). Evaluation of the Probability of Breaking the Electronic Digital Signature Elements. In: Karrupusamy, P., Balas, V.E., Shi, Y. (eds) Sustainable Communication Networks and Application. Lecture Notes on Data Engineering and Communications Technologies, vol 93. Springer, Singapore.

Terentiev, O., Prosiankina-Zharova, T., Savastiyanov, V., Lakhno, V., & Kolmakova, V. (2021). The Features of Building a Portfolio of Trading Strategies Using the SAS OPTMODEL Procedure. Computation, 9(7), 77.

Hulak, H.M., Lakhno, V.–ê., & Adiljanova, S.A. (2020).Method for Rational Management of the Cybersecurity and Reliability Radio Technical Systems. Visnyk NTUU KPI Seriia-Radiotekhnika Radioaparatobuduvannia, (83), 62-68.

Kartbayev, T., Akhmetov, B., et al. Development of decision support system based on feature matrix for cyber threat assessment (2019) International Journal of Electronics and Telecommunications, 65 (4), pp. 545-550.

Busemeyer, J. R., & Diederich, A. (2010). Cognitive modeling. Sage.






Applied Informatics