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Cost-sensitive ensemble methods for bankruptcy prediction in a highly imbalanced data distribution: a real case from the Spanish market

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Abstract

Bankruptcy is an issue of interest in the business world since decades. It is a crucial endeavor for survival to predict this phenomenon in periods of economic turmoil and recession. In fact, bankruptcy modeling is challenging due to the complexity of contributing factors and the highly imbalanced distribution of available data sets. This work aims at improving the prediction power of bankruptcy modeling, by applying cost-sensitive ensemble methods on a real-world Spanish bankruptcy data set to generate prediction models. The performance of the prediction models is highly competitive in comparison with the related research in the field. Cost-sensitive random forests over-performed other approaches in predicting bankruptcy, achieving a geometric mean of 90.7%, 0.094 and 0.088 type I & type II errors, respectively.

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Acknowledgements

This work has been supported in part by Ministerio español de Economía y Competitividad under Project TIN2017-85727-C4-2-P (UGR-DeepBio), SPIP2017-02116 and TEC2015-68752 (also funded by FEDER), as well as Project B-TIC-402-UGR18 (FEDER and Junta de Andalucíıa) and RTI2018-102002-A-I00 (Ministerio español de Ciencia, Innovación y Universidades).

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Authors and Affiliations

  1. Information Technology Department,Faculty of Information Technology and Systems, The University of Jordan, Aqaba, Jordan

    Nazeeh Ghatasheh

  2. Information Technology Department, King Abdullah II School for Information Technology, The University of Jordan, Amman, Jordan

    Hossam Faris

  3. King Abdullah II School for Information Technology, The University of Jordan, Amman, Jordan

    Ruba Abukhurma

  4. Department of Computer Architecture and Computer Technology, ETSIIT and CITIC, University of Granada, Granada, Spain

    Pedro A. Castillo

  5. Data Science Department, Princess Sumaya University for Technology (PSUT), Amman, Jordan

    Nailah Al-Madi

  6. Department of Signal Theory, Telematics and Communications, ETSIIT and CITIC, University of Granada, Granada, Spain

    Antonio M. Mora

  7. School of Sciences, Technology and Engineering, University of Granada, Granada, Spain

    Ala’ M. Al-Zoubi

  8. Department of Information Technology, Mutah University, Karak, Jordan

    Ahmad Hassanat

  9. Industrial Innovation and Robotics Center, University of Tabuk, Tabuk, 71491, Saudi Arabia

    Ahmad Hassanat

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  1. Nazeeh Ghatasheh
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Correspondence to Pedro A. Castillo.

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Ghatasheh, N., Faris, H., Abukhurma, R. et al. Cost-sensitive ensemble methods for bankruptcy prediction in a highly imbalanced data distribution: a real case from the Spanish market. Prog Artif Intell 9, 361–375 (2020). https://doi.org/10.1007/s13748-020-00219-x

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