INTEGRATION OF THE RESULTS OF CORRUPTION RISK ASSESSMENT MODELING INTO THE OVERALL INDEX OF ECONOMIC SECURITY OF THE ENTERPRISE
DOI:
https://doi.org/10.32782/city-development.2025.3-2Keywords:
economic-mathematical model, corruption risks, assessment, economic security index, principal component method, Markov chains, enterprise, integrationAbstract
The article substantiates that corruption risks are one of the most dangerous threats to the economic security of an enterprise, since they directly undermine its stability and competitiveness. An economic and mathematical model for assessing corruption risks is presented with the subsequent integration of the obtained indicators into the general index of economic security of the enterprise, which allows systematizing and quantifying the risks arising from corrupt actions and determining their impact on key financial and economic indicators of activity. The application of the principal components method in determining the weights of factors that directly or indirectly affect the occurrence of corruption risks is justified. The use of Markov chains is argued, which model the dynamics of risk, allowing to predict the probability of its occurrence in future periods, taking into account the previous states of the enterprise, which is critically important for strategic management. It was determined that the use of this approach provides not only the fixation of the current level of corruption risk, but also allows assessing its possible evolution, taking into account different options for the behavior of the system. The use of an integrated approach provides a comprehensive assessment of the state of economic security, which contributes to the adoption of strategically sound management decisions and increasing the enterprise's resilience to internal and external threats. The advantages of integrating the assessment of corruption risks into the general index of economic security of the enterprise are highlighted: it allows quantitatively measuring the impact of risks on the level of economic security; determines their critical importance for the financial stability of the enterprise. The practical value of the work lies in the possibility of using the model for monitoring and forecasting corruption risks in various sectors of the economy, which ensures increased effectiveness of anti-corruption policy at the enterprise level.
References
Markov models and Markov chains explained in real life: probabilistic workout routine, Dec 31, 2020 by Carolina Bento. URL: https://towardsdatascience.com/markov-models-and-markov-chainsexplained-in-real-life-probabilistic-workout-routine-65e47b5c9a73
Paul D.B. Speech Recognition Using Hidden Markov Models 9. What Is the Markov Decision Process? Definition, Working, and Examples, December 20, 2022, Vijay Kanade. URL: https://www.spiceworks.com/tech/artificial-intelligence/articles/what-ismarkov-decision-process/
Markov and Hidden Markov Model Elaborated with examples, Aug 18, 2020, by Vivekvinushanth Christopher. URL: https://towardsdatascience.com/markov-and-hidden-markov-model3eec42298d75
Jolliffe I.T., and Cadima J., «Principal component analysis: are view and recent developments». Philosophical Transactions of the Royal Society, A 374: 20150202, 2016. DOI: https://doi.org/10.1098/rsta.2015.0202
Steven M. Holland, Principal components analysis (PCA). Department of Geology, University of Georgia, Athens, GA. 2019. P. 12. URL: http://strata.uga.edu/8370/handouts/pcaTutorial.pdf
Jeffrey J. Love. Principal Component Analysis in Paleomagnetism. Encyclopedia of Geomagnetism and Paleomagnetism. 2007. № 1. P. 845-850. DOI: https://doi.org/10.1007/978-1-4020-4423-6_271
Tang J., Sui H. Power System Transient Stability Assessment Based on PCA and Support Vector Machine. International Conference on Mechanical, Electrical, Electronic Engineering & Science MEEES2018. Published by Atlantis Press. 2018. Vol. 154. P. 361-365, 2018. DOI: https://doi.org/10.2991/meees-18.2018.63
Huang K. Principal Component Analysis in the Eigenface Technique for Facial Recognition. Senior Theses and Projects. Trinity College, Hartford, Connecticut. 2012. P. 34. URL: https://digitalrepository.trincoll.edu/theses/216
Приймак М.В. Періодичні ланцюги Маркова в задачах статистичного аналізу і прогнозу енергонавантажень. Технічна електродинаміка. 2004. № 2. С. 3-7.
Медведєв, М.Г., Пащенко О.І. Теорія ймовірностей та математична статистика: підруч. Київ: «Ліра-К», 2008. 536 с.
Real World Applications of Markov Decision Process, Jan 9, 2021, Somnath Banerjee. URL: https://towardsdatascience.com/real-world-applications-of-markov-decisionprocess-mdp-a39685546026
Олех Т.М. Застосування ланцюгів маркова для дослідження багатовимірних оцінок при управлінні проектами. Електронний архів наукових та освітніх матеріалів ОНПУ: веб-сайт. URL: http://dspace.opu.ua/jspui/bitstream/123456789/2084/1/064-068.pdf
Markov models and Markov chains explained in real life: probabilistic workout routine, Dec 31, 2020 by Carolina Bento. Available at: https://towardsdatascience.com/markov-models-and-markov-chainsexplained-in-real-life-probabilistic-workout-routine-65e47b5c9a73
Paul D.B. Speech Recognition Using Hidden Markov Models 9. What Is the Markov Decision Process? Definition, Working, and Examples, December 20, 2022, Vijay Kanade. Available at: https://www.spiceworks.com/tech/artificial-intelligence/articles/what-ismarkov-decision-process/
Markov and Hidden Markov Model Elaborated with examples, Aug 18, 2020, by Vivekvinushanth Christopher. Available at: https://towardsdatascience.com/markov-and-hidden-markov-model3eec42298d75
Jolliffe I.T., Cadima J. (2016) Principal component analysis: are view and recent developments. Philosophical Transactions of the Royal Society, A 374. DOI: https://doi.org/10.1098/rsta.2015.0202
Steven M. (2019) Holland, Principal components analysis (PCA). Department of Geology, University of Georgia, Athens, GA, 12 p. Available at: http://strata.uga.edu/8370/handouts/pcaTutorial.pdf
Jeffrey J. Love (2007) Principal Component Analysis in Paleomagnetism. Encyclopedia of Geomagnetism and Paleomagnetism, no. 1, pp. 845-850. DOI: https://doi.org/10.1007/978-1-4020-4423-6_271
Tang J., Sui H. (2018) Power System Transient Stability Assessment Based on PCA and Support Vector Machine.International Conference on Mechanical, Electrical, Electronic Engineering & Science MEEES2018. Published by Atlantis Press, vol. 154, pp. 361-365. DOI: https://doi.org/10.2991/meees-18.2018.63
Kevin Huang. (2012). Principal Component Analysis in the Eigenface Technique for Facial Recognition. Senior Theses and Projects. Trinity College, Hartford, Connecticut, p. 34. Available at: https://digitalrepository.trincoll.edu/theses/216
Pryimak M.V. (2004) Periodychni lantsiuhy Markova v zadachakh statystychnoho analizu i prohnozu enerhonavantazhen [Periodic Markov chains in problems of statistical analysis and energy load forecasting]. Tekhnichna elektrodynamika, no. 2, pp. 3-7.
Medvediev M.H., Pashchenko O.I. (2008) Teoriia ymovirnostei ta matematychna statystyka: pidruch [Probability theory and mathematical statistics: textbook]. Kyiv: «Lira-K, p. 536.
Real World Applications of Markov Decision Process, Jan 9, 2021, Somnath Banerjee. Available at: https://towardsdatascience.com/real-world-applications-of-markov-decisionprocess-mdp-a39685546026
Olekh T.M. Zastosuvannia lantsiuhiv markova dlia doslidzhennia bahatovymirnykh otsinok pry upravlinni proektamy [Application of Markov chains to study multivariate estimates in project management]. Elektronnyi arkhiv naukovykh ta osvitnikh materialiv ONPU. Available at: http://dspace.opu.ua/jspui/bitstream/123456789/2084/1/064-068.pdf
