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Predicting the Probability of Bankruptcy of Service Sector Enterprises Based on Ensemble Learning Methods

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Digital Transformation: What is the Company of Today?

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 805))

Abstract

This chapter focuses on developing an automated model for predicting the bankruptcy of trading enterprises. Due to the COVID-19 pandemic, many companies have suffered significant financial losses and continue to struggle with its negative consequences. This has resulted in an acute need for financial resources, making it crucial for credit organizations to enhance their credit scoring procedures. The chapter explores ensemble methods for predicting bankruptcy, including random forest, gradient boosting trees, and tree ensemble. By utilizing these methods, the researchers aim to improve the accuracy of bankruptcy predictions and provide credit organizations with a reliable tool for assessing the financial stability of trading enterprises. Given the current economic situation, the development of such an automated model has become more important than ever. By implementing these ensemble methods, credit organizations can make more informed decisions regarding lending and investment, which can have a significant impact on the stability of the financial market.

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Acknowledgements

The research is financed as part of the project “Development of a methodology for instrumental base formation for analysis and modeling of the spatial socio-economic development of systems based on internal reserves in the context of digitalization” (FSEG-2023-0008).

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Authors and Affiliations

  1. Peter the Great St. Petersburg Polytechnic University, Polytechnicheskaya, 29, 195251, St. Petersburg, Russia

    Dmitriy Rodionov, Aleksandra Pospelova, Evgenii Konnikov & Darya Kryzhko

Authors
  1. Dmitriy Rodionov
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  2. Aleksandra Pospelova
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  3. Evgenii Konnikov
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  4. Darya Kryzhko
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Corresponding author

Correspondence to Darya Kryzhko .

Editor information

Editors and Affiliations

  1. Department of Management, University of Pannonia, Veszprém, Hungary

    Andrea Bencsik

  2. Graduate School of Industrial Economics, Peter the Great St. Petersburg Polytechnic University, St. Petersburg, Russia

    Anastasia Kulachinskaya

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Rodionov, D., Pospelova, A., Konnikov, E., Kryzhko, D. (2023). Predicting the Probability of Bankruptcy of Service Sector Enterprises Based on Ensemble Learning Methods. In: Bencsik, A., Kulachinskaya, A. (eds) Digital Transformation: What is the Company of Today?. Lecture Notes in Networks and Systems, vol 805. Springer, Cham. https://doi.org/10.1007/978-3-031-46594-9_12

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