Economic Bulletin of the National Mining University

 

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Article

Issue:2023 №2 (82)
Section:Finances, accounting and taxation
UDK:338.2
DOI:https://doi.org/10.33271/ebdut/82.128
Article language:Ukrainian
Pages:128-135
Title:Fintech tools for predicting bankruptcy: integration of traditional and innovative approaches
Authors:Solianyk L. H., Dnipro University of Technology,
Shtefan N. M., Dnipro University of Technology,
Nikolaienko A. O., Dnipro University of Technology
Annotation:Methods. The theoretical and methodological basis of the study is the scientific developments of domestic and foreign scholars related to the issues of determining the role and features of using traditional discriminant models of bankruptcy forecasting and modern FinTech tools. The results of the study were obtained using the methods of abstraction; general and particular – in establishing the unity of existing traditional methods of bankruptcy forecasting and innovative FinTech technologies; the systematic method was used to systematise a wide range of FinTech tools specifically designed to predict the bankruptcy of enterprises, the method of generalisation – in identifying common digital elements to create analytical capabilities for managing the financial activities of an enterprise and risk management. Methods of theoretical and empirical research, including abstraction and comparison, were also used to identify the specifics of applying various forecasting models and the capabilities and advantages of FinTech tools. Graphical analysis methods were used to visualise the results of the study. Results. The results of the study revealed the potential of integrating discriminant models and FinTech tools in bankruptcy forecasting, which will help improve financial analysis and risk management in the context of economic changes. The article shows that the use of discriminant models for predicting bankruptcy has the potential to improve financial analysis and effective risk management. These models allow to take into account various financial indicators and factors that affect the probability of bankruptcy of an enterprise. They provide an opportunity to assess a company's financial stability and ability to repay its liabilities. If used correctly, these models can help to ensure early detection of financial problems and take effective measures to prevent bankruptcy. Novelty. In the course of the study of bankruptcy forecasting models, it is established that the optimal combination of discriminant models and FinTech tools provides an opportunity to obtain accurate forecasts of the further functioning and development of enterprises and opens up new prospects for financial analysis to avoid risks and implement effective innovative solutions in today's changing environment. Practical value. It is the ability to integrate traditional and innovative approaches to solving specific problems related to bankruptcy forecasting and the development of enterprises in an unstable environment. 
Keywords:Bankruptcy, Bankruptcy prediction models, Risk, FinTech tools, Artificial intelligence, RiskCalc, Bloomberg Terminal
File of the article:EV20232_128-135.pdf
Literature:
  • 1. Altman, E. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. The Journal of Finance, Vol. 23(4), pp. 589-609. https://doi.org/10.2307/2978933.
  • 2. Zmiiewski, M.E. (1984). Methodological Issues Related to the Estimation of Financial Distress Prediction Models. Journal of Accounting Research, Vol. 22, pp. 59-82. http://dx.doi.org/10.2307/2490859.
  • 3. Springate, G.L.V. (1978). Predicting the possibility of failure in a Canadian firm (Unpublished master’s thesis). Canada: Simon Fraser University.
  • 4. Taffler, R.J. (1983) The Assessment of Company Solvency and Performance Using a Statistical Model. Accounting and Business Research, 13, 295-308. https://doi.org/10.1080/00014788.1983.9729767.
  • 5. Tereshchenko, O.O. (2003). Dyskryminantna model intehralnoi otsinky finansovoho stanu pidpryiemstva. Ekonomika Ukrainy, (8), 38-44.
  • 6. Matviychuk, A.V. (2006). Analiz ta prohnozuvannia rozvytku finansovo-ekonomichnykh system iz vykorystanniam nechitkoi lohiky. Kyiv: Tsentr navchalnoi literatury.
  • 7. Lennox, Clive. (1999). Identifying failing companies: A revaluation of the logit, probit and da approaches. Journal of Economics and Business, Vol. 51 (4), pp. 347-364. https://doi.org/10.1016/S0148-6195(99)00009-0.
  • 8. Shi, Yin, & Xiaoni Li. (2019). An overview of bankruptcy prediction models for corporate firms: A systematic literature review. Intangible Capital, Vol. 15 (2), pp. 114-127. https://doi.org/10.3926/ic.1354.
  • 9. Savchenko, A.M., & Fisher, N.V. (2020). Doslidzhennia ymovirnosti nastannia bankrutstva vyrobnychoho pidpryiemstva. Biznes-Inform, (11), 186-191. https://doi.org/10.32983/2222-4459-2020-11-186-191.
  • 10. Savchenko, A.M. (2019). Analiz pokaznykiv balansu z metoui poperedzhennia bankrutstva subiekta hospodariuvannia. Molodyi vchenyi, (1), 501-505. https://doi.org/10.32839/2304-5809/2019-1-65-115.
  • 11. RiskCalc. Moody's Analytics. Retrieved from https://www.moodysanalytics.com/
  • 12. Predata. Retrieved from https://predata.com/
  • 13. Bloomberg Professional Services. Bloomberg Terminal. Retrieved from https://www.bloomberg.com/professional/ [in USA].
  • 14. QuickBooks. Retrieved from https://quickbooks.intuit.com/global/reports/
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