Information-analytical support to business processes for making investment decisions

Authors

DOI:

https://doi.org/10.15587/1729-4061.2024.304688

Keywords:

business process algorithm, investment attractiveness, information-analytical support

Abstract

The object of this study is the business processes of making an investment decision based on determining the state of the investment attractiveness of the enterprise.

To support the adoption of investment decisions under the conditions of a fast-moving and dynamic environment, information-analytical support to the algorithm using intelligent information systems has been developed. The relevance of the study is justified by the continuous development of digitization processes, in particular in the financial realm. The traditional approach to the reproduction of management decision-making technology is complemented by the tools and methods of intelligent information systems. In particular, the modeling of the target subject area using UML made it possible to determine the main requirements for the projected information-analytical support (user roles, available options, types of connections and the logic of interaction between them). SQL queries to the information database speed up the process of processing and obtaining the necessary data samples. Business intelligence (BI) tools are used to create interactive reports that provide access to operational financial data. At the stage of making investment decisions, these tools make it possible to  study a wide range of analytical data based on the results of the assessment of the investment attractiveness of the enterprise obtained at the previous stage of the developed algorithm. Monitoring of the main indicators of the enterprise's investment attractiveness is carried out on the basis of a dashboard, an information panel (display) with graphs, tables, and figures that clearly reflect the dynamics and rates of change of the investigated indicators. The results of the use of algorithmic information-analytical support make it possible to quickly prepare and make investment decisions. A visual description of the projected information-analytical support, visual content of the results of investment analysis, the validity of decisions due to the use of reliable retrospective information from an aggregated database

Author Biographies

Nataliya Vnukova, Simon Kuznets Kharkiv National University of Economics; Scientific & Research Institute of Providing Legal Framework for the Innovative Development of National Academy of Law Sciences of Ukraine

Doctor of Economic Sciences, Professor

Department of Customs Affairs and Financial Services

Leading Researcher

Inna Aleksieienko, Simon Kuznets Kharkiv National University of Economics

PhD, Assoсiate Professor

Department of Finance

Svitlana Leliuk, Simon Kuznets Kharkiv National University of Economics

PhD, Associate Professor

Department of Finance

Yevheniia Malyshko, Simon Kuznets Kharkiv National University of Economics

PhD, Assoсiate Professor

Department of Finance

Volodymyr Chernyshov, Simon Kuznets Kharkiv National University of Economics

PhD, Assoсiate Professor

Department of Finance

References

  1. Antoniuk, B. P. (2022). Osnovy alhorytmizatsii ta prohramuvannia. Ch. 1. Lutsk: Vezha-druk, 36. Available at: https://evnuir.vnu.edu.ua/bitstream/123456789/21329/1/ОАтП_ВСЕ02.pdf
  2. Number of fintechs worldwide from 2018 to 2024, by region. Available at: https://www.statista.com/statistics/893954/number-fintech-startups-by-region/
  3. Lei, X., Mohamad, U. H., Sarlan, A., Shutaywi, M., Daradkeh, Y. I., Mohammed, H. O. (2022). Development of an intelligent information system for financial analysis depend on supervised machine learning algorithms. Information Processing & Management, 59 (5), 103036. https://doi.org/10.1016/j.ipm.2022.103036
  4. Shiralkar, K., Bongale, A., Kumar, S., Bongale, A. M. (2023). An intelligent method for supply chain finance selection using supplier segmentation: A payment risk portfolio approach. Cleaner Logistics and Supply Chain, 8, 100115. https://doi.org/10.1016/j.clscn.2023.100115
  5. Hlibko, S., Vnukova, N., Davydenko, D., Pyvovarov, V., Avanesian, V. (2023). The Use of Linguistic Methods of Text Processing for the Individualization of the Bank’s Financial Servise. Proceedings of the 7th International Conference on Computational Linguistics and Intelligent Systems. Volume III: Intelligent Systems Workshop, 157–167. Available at: https://ceur-ws.org/Vol-3403/paper13.pdf
  6. Königstorfer, F., Thalmann, S. (2020). Applications of Artificial Intelligence in commercial banks – A research agenda for behavioral finance. Journal of Behavioral and Experimental Finance, 27, 100352. https://doi.org/10.1016/j.jbef.2020.100352
  7. Nalyvaichenko, K. (2013). Vplyv informatsiynykh system na efektyvnist investytsiynykh protsesiv na pidpryiemstvakh. Visnyk ekonomichnoi nauky Ukrainy, 2, 105–108. Available at: http://nbuv.gov.ua/UJRN/Venu_2013_2_28
  8. Liu, X., Yuan, X., Zhang, R., Ye, N. (2022). Risk Assessment and Regulation Algorithm for Financial Technology Platforms in Smart City. Computational Intelligence and Neuroscience, 2022, 1–13. https://doi.org/10.1155/2022/9903364
  9. Back, C., Morana, S., Spann, M. (2023). When do robo-advisors make us better investors? The impact of social design elements on investor behavior. Journal of Behavioral and Experimental Economics, 103, 101984. https://doi.org/10.1016/j.socec.2023.101984
  10. Cioranu, C., Cioca, M., Novac, C. (2015). Database Versioning 2.0, a Transparent SQL Approach Used in Quantitative Management and Decision Making. Procedia Computer Science, 55, 523–528. https://doi.org/10.1016/j.procs.2015.07.030
  11. Rao, A., Khankhoje, D., Namdev, U., Bhadane, C., Dongre, D. (2022). Insights into NoSQL databases using financial data: A comparative analysis. Procedia Computer Science, 215, 8–23. https://doi.org/10.1016/j.procs.2022.12.002
  12. Pavaloaia, V.-D., Strimbei, C. (2015). Experiments and Results by Modeling the Financial Domain with UML. Procedia Economics and Finance, 20, 510–517. https://doi.org/10.1016/s2212-5671(15)00103-3
  13. Karampure, R., Wang, C. Y., Vashi, Y. (2021). UML sequence diagram to axiomatic design matrix conversion: a method for concept improvement for software in integrated systems. Procedia CIRP, 100, 457–462. https://doi.org/10.1016/j.procir.2021.05.104
  14. Ding, D., Shen, Y., Jiang, J., Yuan, Q., Xiu, T., Ni, K., Liu, C. (2023). Data collection and information security analysis in sports teaching system based on intelligent sensor. Measurement: Sensors, 28, 100854. https://doi.org/10.1016/j.measen.2023.100854
  15. World investment report 2023. Available at: https://unctad.org/publication/world-investment-report-2023
  16. Kovalenko, A. G. (2013). Modern aspects of attractive investment analyses of enterprise. Efektyvna ekonomika, 7. Available at: http://www.economy.nayka.com.ua/?op=1&z=2165
  17. Kolodchak, O. M. (2013). Intelektualnyi analiz danykh. Visnyk Natsionalnoho universytetu "Lvivska politekhnika". Kompiuterni systemy ta merezhi, 773, 49–58. Available at: http://nbuv.gov.ua/UJRN/VNULPKSM_2013_773_11
Information-analytical support to business processes for making investment decisions

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Published

2024-06-28

How to Cite

Vnukova, N., Aleksieienko, I., Leliuk, S., Malyshko, Y., & Chernyshov, V. (2024). Information-analytical support to business processes for making investment decisions. Eastern-European Journal of Enterprise Technologies, 3(13 (129), 23–33. https://doi.org/10.15587/1729-4061.2024.304688

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Section

Transfer of technologies: industry, energy, nanotechnology