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A Framework for Business Failure Prediction

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Artificial Intelligence and Soft Computing (ICAISC 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10246))

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Abstract

Business failure prediction systems help predict financial failures before they actually happen and provide an early warning for enterprises. Using machine learning techniques, instead of traditional statistical models, has brought a considerable increase in performance into the area of business failure prediction. This paper presents a framework for predicting business failures by using different machine learning techniques. We, also, implemented a novel model for business failure prediction based on NARX (nonlinear autoregressive network with exogenous inputs) feedback neural network to be included into this framework which is a recurrent dynamic network with feedback connections. Detailed experiments are conducted to compare the performance of these approaches. Especially, for the long-term business failure predictions, there are no other papers investigating the performance of NARX. To the best of our knowledge, this is the first time NARX algorithm is applied for long-term business failure prediction.

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Acknowledgments

This research was partially supported by The Scientific and Technological Research Council of Turkey (TUBITAK) under TEYDEB grant 3150156.

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Authors and Affiliations

  1. Idea Teknoloji Cozumleri, Istanbul, Turkey

    Irem Islek & Idris Murat Atakli

  2. Department of Computer Engineering, Istanbul Technical University, Istanbul, Turkey

    Sule Gunduz Oguducu

Authors
  1. Irem Islek
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  2. Idris Murat Atakli
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  3. Sule Gunduz Oguducu
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Corresponding author

Correspondence to Irem Islek .

Editor information

Editors and Affiliations

  1. Częstochowa University of Technology, Częstochowa, Poland

    Leszek Rutkowski

  2. Częstochowa University of Technology, Częstochowa, Poland

    Marcin Korytkowski

  3. Częstochowa University of Technology, Częstochowa, Poland

    Rafał Scherer

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

    Ryszard Tadeusiewicz

  5. University of California, Berkeley, California, USA

    Lotfi A. Zadeh

  6. University of Louisville, Louisville, Kentucky, USA

    Jacek M. Zurada

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Islek, I., Atakli, I.M., Oguducu, S.G. (2017). A Framework for Business Failure Prediction. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L., Zurada, J. (eds) Artificial Intelligence and Soft Computing. ICAISC 2017. Lecture Notes in Computer Science(), vol 10246. Springer, Cham. https://doi.org/10.1007/978-3-319-59060-8_8

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  • DOI: https://doi.org/10.1007/978-3-319-59060-8_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-59059-2

  • Online ISBN: 978-3-319-59060-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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