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Is Artificial Intelligence Ready to Assess an Enterprise’s Financial Security?

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  • Oleksandr Melnychenko

    (Department of Finance, Gdansk University of Technology, 80-233 Gdansk, Poland
    The London Academy of Science and Business, London W1U 6TU, UK)

Abstract

This study contributes to the literature on financial security by highlighting the relevance of the perceptions and resulting professional judgment of stakeholders. Assessing a company’s financial security using only economic indicators—as suggested in the existing literature—would be inaccurate when undertaking a comprehensive study of financial security. Specifically, indices and indicators based on financial or managerial reporting calculated at any particular point in time, provide only a superficial understanding—and may even distort the overall picture. It has also been suggested that expert assessment is the most objective method, although it has disadvantages related to individual cognitive limitations. These limitations are not particular to artificial intelligence, which could assess an enterprise’s financial security in a less biased way. However, by only imitating human behavior, it is not able to perceive and evaluate with intuition the dynamics of the company’s development and holistically assess the financial condition—despite the possibility of learning and forecasting—because artificial intelligence is not able to think and predict, which, in an enterprise, is the most important skill of a manager. Therefore, the risk of developing artificial intelligence to assess a firm’s financial security lies in a biased assessment of the enterprise’s activities in general—and its financial security in particular.

Suggested Citation

  • Oleksandr Melnychenko, 2020. "Is Artificial Intelligence Ready to Assess an Enterprise’s Financial Security?," JRFM, MDPI, vol. 13(9), pages 1-19, August.
  • Handle: RePEc:gam:jjrfmx:v:13:y:2020:i:9:p:191-:d:402052
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    as
    1. Aleksandra Kuzior & Aleksy Kwilinski & Volodymyr Tkachenko & Volodymyr Tkachenko, 2019. "Sustainable development of organizations based on the combinatorial model of artificial intelligence," Entrepreneurship and Sustainability Issues, VsI Entrepreneurship and Sustainability Center, vol. 7(2), pages 1353-1376, December.
    2. Sivarajah, Uthayasankar & Kamal, Muhammad Mustafa & Irani, Zahir & Weerakkody, Vishanth, 2017. "Critical analysis of Big Data challenges and analytical methods," Journal of Business Research, Elsevier, vol. 70(C), pages 263-286.
    3. Charnes, A. & Cooper, W. W. & Rhodes, E., 1978. "Measuring the efficiency of decision making units," European Journal of Operational Research, Elsevier, vol. 2(6), pages 429-444, November.
    4. Jappelli, Tullio & Padula, Mario, 2013. "Investment in financial literacy and saving decisions," Journal of Banking & Finance, Elsevier, vol. 37(8), pages 2779-2792.
    5. Elisabetta Raguseo & Claudio Vitari & Federico Pigni, 2020. "Profiting from big data analytics: The moderating roles of industry concentration and firm size," Post-Print hal-03032504, HAL.
    6. Zoričák, Martin & Gnip, Peter & Drotár, Peter & Gazda, Vladimír, 2020. "Bankruptcy prediction for small- and medium-sized companies using severely imbalanced datasets," Economic Modelling, Elsevier, vol. 84(C), pages 165-176.
    7. Rowe, Gene & Wright, George, 1999. "The Delphi technique as a forecasting tool: issues and analysis," International Journal of Forecasting, Elsevier, vol. 15(4), pages 353-375, October.
    8. Nataliya Dalevska & Nataliya Dalevska & Valentyna Khobta & Valentyna Khobta & Aleksy Kwilinski & Aleksy Kwilinski & Sergey Kravchenko & Sergey Kravchenko, 2019. "A model for estimating social and economic indicators of sustainable development," Entrepreneurship and Sustainability Issues, VsI Entrepreneurship and Sustainability Center, vol. 6(4), pages 1839-1860, June.
    9. Ardalan, Kavous, 2018. "Neurofinance versus the efficient markets hypothesis," Global Finance Journal, Elsevier, vol. 35(C), pages 170-176.
    10. Elisabetta Raguseo & Claudio Vitari & Federico Pigni, 2020. "Profiting from big data analytics: The moderating roles of industry concentration and firm size," Post-Print hal-03511355, HAL.
    11. Norman Dalkey & Olaf Helmer, 1963. "An Experimental Application of the DELPHI Method to the Use of Experts," Management Science, INFORMS, vol. 9(3), pages 458-467, April.
    12. Wall, Larry D., 2018. "Some financial regulatory implications of artificial intelligence," Journal of Economics and Business, Elsevier, vol. 100(C), pages 55-63.
    13. Butaru, Florentin & Chen, Qingqing & Clark, Brian & Das, Sanmay & Lo, Andrew W. & Siddique, Akhtar, 2016. "Risk and risk management in the credit card industry," Journal of Banking & Finance, Elsevier, vol. 72(C), pages 218-239.
    14. Johan Eklund & Nadine Levratto & Giovanni B. Ramello, 2020. "Entrepreneurship and failure: two sides of the same coin?," Small Business Economics, Springer, vol. 54(2), pages 373-382, February.
    15. Salim Lahmiri & Stelios Bekiros, 2019. "Can machine learning approaches predict corporate bankruptcy? Evidence from a qualitative experimental design," Quantitative Finance, Taylor & Francis Journals, vol. 19(9), pages 1569-1577, September.
    16. Henryk Dzwigol & Mariola Dzwigol-Barosz & Radosław Miśkiewicz & Aleksy Kwilinski, 2020. "Manager competency assessment model in the conditions of industry 4.0," Entrepreneurship and Sustainability Issues, VsI Entrepreneurship and Sustainability Center, vol. 7(4), pages 2630-2644, June.
    17. Dimitras, A. I. & Zanakis, S. H. & Zopounidis, C., 1996. "A survey of business failures with an emphasis on prediction methods and industrial applications," European Journal of Operational Research, Elsevier, vol. 90(3), pages 487-513, May.
    18. Elisabetta Raguseo & Claudio Vitari & Federico Pigni, 2020. "Profiting from big data analytics: The moderating roles of industry concentration and firm size," Post-Print hal-03511381, HAL.
    19. Raguseo, Elisabetta & Vitari, Claudio & Pigni, Federico, 2020. "Profiting from big data analytics: The moderating roles of industry concentration and firm size," International Journal of Production Economics, Elsevier, vol. 229(C).
    20. Aksana A. Turgaeva & Liudmila V. Kashirskaya & Yulia A. Zurnadzhyants & Olga A. Latysheva & Irina V. Pustokhina & Andrei V. Sevbitov, 2020. "Assessment of the financial security of insurance companies in the organization of internal control," Entrepreneurship and Sustainability Issues, VsI Entrepreneurship and Sustainability Center, vol. 7(3), pages 2243-2254, March.
    21. Oleksandr Melnychenko, 2019. "Application of artificial intelligence in control systems of economic activity," Virtual Economics, The London Academy of Science and Business, vol. 2(3), pages 30-40, July.
    22. Aleksy Kwilinski, 2018. "Mechanism of formation of industrial enterprise development strategy in the information economy," Virtual Economics, The London Academy of Science and Business, vol. 1(1), pages 7-25, October.
    23. Elisabetta Raguseo & Claudio Vitari & Federico Pigni, 2020. "Profiting from big data analytics: The moderating roles of industry concentration and firm size," Grenoble Ecole de Management (Post-Print) hal-03032504, HAL.
    24. Edward I. Altman & Małgorzata Iwanicz-Drozdowska & Erkki K. Laitinen & Arto Suvas, 2020. "A Race for Long Horizon Bankruptcy Prediction," Applied Economics, Taylor & Francis Journals, vol. 52(37), pages 4092-4111, July.
    25. Fethi, Meryem Duygun & Pasiouras, Fotios, 2010. "Assessing bank efficiency and performance with operational research and artificial intelligence techniques: A survey," European Journal of Operational Research, Elsevier, vol. 204(2), pages 189-198, July.
    26. William W. Cooper & Lawrence M. Seiford & Kaoru Tone, 2007. "Data Envelopment Analysis," Springer Books, Springer, edition 0, number 978-0-387-45283-8, September.
    27. A. V. Belyanin., 2018. "Richard Thaler and behavioral economics: From the lab experiments to the practice of nudging (Nobel Memorial Prize in Economic Sciences 2017)," VOPROSY ECONOMIKI, N.P. Redaktsiya zhurnala "Voprosy Economiki", vol. 1.
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