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Company Bankruptcy Prediction with Neural Networks

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Artificial Intelligence and Soft Computing (ICAISC 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10841))

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Abstract

Bankruptcy prediction is a very important issue in business financing. Raising availability of financial data makes it more and more viable. We use large data concerning the health of Polish companies to predict their possible bankruptcy in a relatively short period. To this end, we utilize feedforward neural networks.

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Author information

Authors and Affiliations

  1. Faculty of Mechanical Engineering and Computer Science, Institute of Mathematics, Czȩstochowa University of Technology, al. Armii Krajowej 21, 42-200, Czȩstochowa, Poland

    Jolanta Pozorska

  2. Faculty of Management, Czȩstochowa University of Technology, al. Armii Krajowej 19, 42-200, Czȩstochowa, Poland

    Magdalena Scherer

Authors
  1. Jolanta Pozorska
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  2. Magdalena Scherer
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Corresponding author

Correspondence to Jolanta Pozorska .

Editor information

Editors and Affiliations

  1. Częstochowa University of Technology, Częstochowa, Poland

    Leszek Rutkowski

  2. Częstochowa University of Technology, Częstochowa, Poland

    Rafał Scherer

  3. Częstochowa University of Technology, Częstochowa, Poland

    Marcin Korytkowski

  4. University of Alberta, Edmonton, AB, Canada

    Witold Pedrycz

  5. AGH University of Science and Technology, Kraków, Poland

    Ryszard Tadeusiewicz

  6. University of Louisville, Louisville, KY, USA

    Jacek M. Zurada

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Pozorska, J., Scherer, M. (2018). Company Bankruptcy Prediction with Neural Networks. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J. (eds) Artificial Intelligence and Soft Computing. ICAISC 2018. Lecture Notes in Computer Science(), vol 10841. Springer, Cham. https://doi.org/10.1007/978-3-319-91253-0_18

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  • DOI: https://doi.org/10.1007/978-3-319-91253-0_18

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-91252-3

  • Online ISBN: 978-3-319-91253-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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