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ACADEMY
OF FINANCIAL
MANAGEMENT
.


№ 11/2023

№ 11/2023

Fìnansi Ukr. 2023 (11): 34–48
https://doi.org/10.33763/finukr2023.11.034

In memory of Viktor Fedosov

ZATONATSKA Tetiana 1, LJUTYJ Igor 2, ANISIMOVA Olga 3

1Taras Shevchenko National University of Kyiv, Research ID : http://www.researcherid.com/rid/I-1647-2018
OrcID ID : https://orcid.org/0000-0001-9197-0560
2Taras Shevchenko National University of Kyiv
OrcID ID : https://orcid.org/0000-0002-3561-2432
3SSI «Institute of Educational Analytics»
OrcID ID : https://orcid.org/0000-0002-6721-3030


Public finance governance under uncertainty


Introduction. Effective public financial management is the basis for the stability of the national financial system. Most countries have faced the problem of uncertainty in planning and forecasting budget revenues and expenditures. This is due to a series of events that have significantly affected budgeting and budget forecasting methods. However, classical approaches remain at the heart of public finance management, as they are based on the national legal and regulatory framework, organizational and institutional architectonics of the budget system,features of the national economy and mentality.
Problem Statement. Russia's armed aggression against Ukraine has created uncertainty not only in our country but also throughout the world. The war has disrupted traditional economic relations, led to the imposition of sanctions, and changed the structure of revenues and expenditures of budgets around the world.
The purpose of the article is to analyze modern theories of public finance management and their adaptation to the conditions of uncertainty in the context of global turbulence and the war in Ukraine.
Methods. The comparison method was used to analyze the theories of public finance management, and the scientific methods of deduction, induction, comparison, synthesis were used to assess the benefits of using new technologies in the budgeting process.
Results. The basic theories of public finance management are considered. A number of approaches and methodologies are identified, which, in combination with modern digital tools, can be considered effective in conditions of uncertainty. It is substantiated that traditional theories remain relevant because they are based on national legislation in a particular area, so their modification and adaptation require a thorough quantitative and qualitative analysis, as well as changes in the legal and regulatory framework in the budgetary sphere.
Conclusions. The effectiveness of the main theories of public finance management under conditions of uncertainty is assessed. It is proved that the relevance of existing methods in the field of public finance management should be based on Data Science tools that allow making flexible and relevant forecasts, processing structured and unstructured large data sets. It is recommended to use fintech tools to increase the transparency of the use of funds and more reliable collection of information, which will help speed up budget execution and strengthen control over the movement of financial resources.

Keywords:public finances, budget policy, uncertainty, state budget revenues and expenditures, planning and forecasting of budget expenditures, Data Science

JEL: H50, H60


Zatonatska T. . Public finance governance under uncertainty / T. . Zatonatska, I. . Ljutyj, O. . ANISIMOVA // Фінанси України. - 2023. - № 11. - C. 34-48.

Article original in Ukrainian (pp. 34 - 48) DownloadDownloads :22
1. Shoup, C. (2017). Public finance. New York: Routledge. doi.org/10.4324/9781315127729
2. Fisher, R. C. (2022). State and local public finance. London: Routledge. doi.org/10.4324/9781003030645
3. Bovaird, T., & Löffler, E. (2023). Public management and governance. London: Routledge. doi.org/10.4324/9781003282839
4. Simson, R., Sharma, N., & Aziz, I. (2011). A guide to public financial management literature. London: Overseas Development Institute. Retrieved from www.academia.edu/24367768/A_guide_to_public_financial_management_literature.
5. Griffin, N., Uña, G., Bazarbash, M., & Verma, A. (2023). Fintech Payments in Public Financial Management: Benefits and Risks. IMF Working Papers, 020. doi.org/10.5089/9798400232213.001
6. Cangiano, M., Gelb, A., & Goodwin-Groen, R. (2019). Public financial management and the digitalization of payments. Center for Global Development. Retrieved from www.cgdev.org/sites/default/files/public-financial-management-and-digitalization-payments.pdf.
7. Oparin, V., & Fedosov, V. (2016). Dominants of th [in Ukrainian]e theory of public finance in the scientific school of KNEU. Securities market of Ukraine, 5-6, 3–13. Retrieved from www.securities.usmdi.org/?p=22&n=94&s=970 .
8. Leonenko, P., Fedosov, V., & Yukhymenko, P. (2017). Milestones of financial science development: problem methodology. Finance of Ukraine, 4, 55–74 [in Ukrainian]. doi.org/10.33763/finukr2017.04.055
9. Leonenko, P., Fedosov, V., & Yukhymenko, P. (2017). Financial science: genesis, evolution and development. Securities market of Ukraine, 1-2, 3–30. Retrieved from www.securities.usmdi.org/?p=22&n=95&s=993 [in Ukrainian].
10. Oparin, V., Fedosov, V., & Yukhymenko, P. (2017). Public finances: genesis, theoretical and practical conceptualization collision. Finance of Ukraine, 2, 110–128. Retrieved from finukr.org.ua/?page_id=723&aid=4399 [in Ukrainian].
11. Fedosov, V., Krysovatyy, A., Oparin, V., & Yukhymenko, P. (2019). Modern Ukrainian financial science: theoretical paradigm & practical concept of public finance. Digital Publishing House Oklahoma City. Retrieved from dspace.wunu.edu.ua/handle/316497/41463.
12. Paientko, T., & Fedosov, V. To implement controlling in financial management at the macro level in Ukraine. Finance of Ukraine, 6, 107–126. Retrieved from finukr.org.ua/?page_id=723&aid=4528 [in Ukrainian].
13. Fedosov, V., & Paientko, T. (2018). Government financial accountability: Key problems and main trends in post-communist countries. Theoretical Journal of Accounting, 99 (155), 25–39. doi.org/10.5604/01.3001.0012.2930
14. Fedosov, V., & Paientko, T. (2019). Opportunistic government behavior: How controlling approaches in public management can prevent it. Theoretical Journal of Accounting, 104 (160), 37–54. doi.org/10.5604/01.3001.0013.4355
15. Jovanović, T., & Vašiček, V. (2021). The role and application of accounting and budgeting information in government financial management process - a qualitative study in Slovenia. Public Money & Management, 41 (2), 99–106. doi.org/10.1080/09540962.2020.1724405
16. Jerow, S., & Wolff, J. (2022). Fiscal policy and uncertainty. Journal of Economic Dynamics and Control, 145, 104559. doi.org/10.1016/j.jedc.2022.104559
17. Zahid, A., Iqbal, A., Rasool, G., & Altaf, A. (2023). Uncertainty in Fiscal and Monetary Policy and its Impact on Economic Growth: An Analysis from Pakistan. Empirical Economic Review, 6 (1), 94–114. Retrieved from ojs.umt.edu.pk/index.php/eer/article/view/1587.
18. Chohan, U. W. (2022). The return of Keynesianism? Exploring path dependency and ideational change in post-covid fiscal policy. Policy and Society, 41 (1), 68–82. doi.org/10.1093/polsoc/puab013
19. Amaglobeli, M. D., Hanedar, E., Hong, M. G. H., & Thévenot, C. (2022). Fiscal policy for mitigating the social impact of high energy and food prices. IMF Notes, 001. Retrieved from www.imf.org/en/Publications/IMF-Notes/Issues/2022/06/07/Fiscal-Policy-for-Mitigating-the-Social-Impact-of-High-Energy-and-Food-Prices-519013.
20. De Soyres, F., Santacreu, A. M., & Young, H. (2022, July 15). Fiscal policy and excess inflation during Covid-19: a cross-country view. FEDS Notes. doi.org/10.17016/2380-7172.3083
21. Ilori, A. E., Paez-Farrell, J., & Thoenissen, C. (2022). Fiscal policy shocks and international spillovers. European Economic Review, 141, 103969. doi.org/10.1016/j.euroecorev.2021.103969
22. Hariharan, N. K. (2017). Predictive model building for driver-based budgeting using machine learning. Journal of Emerging Technologies and Innovative Research, 4 (6), 567–575. doi.org/10.2139/ssrn.3899560
23. Li, W., Xiang, L., Zhou, Z., & Peng, F. (2021). Privacy budgeting for growing machine learning datasets. IEEE INFOCOM 2021-IEEE Conference on Computer Communications, pp. 1–10. doi.org/10.1109/INFOCOM42981.2021.9488920
24. Jang, H. (2019). A decision support framework for robust R&D budget allocation using machine learning. Decision Support Systems, 121. doi.org/10.1016/j.dss.2019.03.010
25. Faccia, A. (2020). Big Data-driven Budgeting and Business Planning. Preprint, 2020090747. doi.org/10.20944/preprints202009.0747.v1
26. Huacarpuma, R. C., Rodrigues, D. D. C., Serrano, A. M. R., da Costa, J. P. C. L., de Sousa, Jr., R. T., Holanda, M., & Araujo, A. P. F. (2013). Big data: A case study on data from the Brazilian ministry of planning, budgeting and management. IADIS Applied Computing, pp. 201–205. Retrieved from lasp.unb.br/wp-content/uploads/papers/AC_2013_Daniel_Ruben_Toni.pdf.
27. Shen, B., Hendri, P. A., & Shao, K. (2015). KPI-Driven Predictive ML Models Approach Towards Municipal Budgeting Optimization (CS229 Machine Learning Project Final Report). Stanford. Retrieved from cs229.stanford.edu/proj2015/194_report.pdf
28. Fisher, I. E., Garnsey, M. R., & Hughes, M. E. (2016). Natural language processing in accounting, auditing and finance: A synthesis of the literature with a roadmap for future research. Intelligent Systems in Accounting, Finance and Management, 23 (3), 157–214. doi.org/10.1002/isaf.1386
29. Valle-Cruz, D., Fernandez-Cortez, V., & Gil-Garcia, J. R. (2022). From E-budgeting to smart budgeting: Exploring the potential of artificial intelligence in government decision-making for resource allocation. Government Information Quarterly, 39 (2), 101–144. doi.org/10.1016/j.giq.2021.101644
30. Davies, J., Arana-Catania, M., Procter, R., van Lier, F. A., & He, Y. (2021, October). Evaluating the application of NLP tools in mainstream participatory budgeting processes in Scotland. Proceedings of the 14th International Conference on Theory and Practice of Electronic Governance, pp. 362–366. doi.org/10.1145/3494193.3494242
31. Tiron-Tudor, A., Donțu, A. N., & Bresfelean, V. P. (2022). Emerging Technologies’ Contribution to the Digital Transformation in Accountancy Firms. Electronics, 11 (22), 3818. doi.org/10.3390/electronics11223818
32. Eltweri, A., Faccia, A., & Khassawneh, O. (2021, December). Applications of Big Data within Finance: Fraud Detection and Risk Management within the Real Estate Industry. 2021 3rd International Conference on E-Business and E-commerce Engineering, pp. 67–73. doi.org/10.1145/3510249.3510262
33. Ljutyj, I., & Miedviedkova, N. The modern paradigm of the financial policy of the state and the features of its implementation under the war on the territory of Ukraine. Finance of Ukraine, 6, 61–74 [in Ukrainian]. doi.org/10.33763/finukr2023.06.061
34. Kudrjashov, V. (2023). Management of state budget financing in the aspect of Ukraine’s cooperation with the IMF. Finance of Ukraine, 6, 75–95 [in Ukrainian]. doi.org/10.33763/finukr2023.06.075