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CatBoost model and artificial intelligence techniques for corporate failure prediction

Author

Listed:
  • Sami Ben Jabeur

    (UR CONFLUENCE : Sciences et Humanités (EA 1598) - UCLy - UCLy (Lyon Catholic University), ESDES - ESDES, Lyon Business School - UCLy - UCLy - UCLy (Lyon Catholic University))

  • Cheima Gharib

    (LIEC - Laboratoire Interdisciplinaire des Environnements Continentaux - INSU - CNRS - Institut national des sciences de l'Univers - UL - Université de Lorraine - CNRS - Centre National de la Recherche Scientifique)

  • Salma Mefteh-Wali

    (ESSCA - ESSCA – École supérieure des sciences commerciales d'Angers = ESSCA Business School)

  • Wissal Ben Arfi

    (EDC - EDC Paris Business School)

Abstract

Financial distress prediction provides an effective warning system for banks and investors to correctly guide decisions on granting credit. Ensemble methods have demonstrated their performance in corporate failure prediction. Among the ensemble methods, gradient boosting has been successfully used in bankruptcy prediction. In this paper, we propose a novel approach to classify categorical data using gradient boosting decision trees, namely, CatBoost. First, we investigate the importance of the features identified by the CatBoost model. Second, we compare our approach with eight reference machine learning models at one, two and three years before failure. Our model demonstrates an effective improvement in the power of classification performance compared with other advanced approaches.

Suggested Citation

  • Sami Ben Jabeur & Cheima Gharib & Salma Mefteh-Wali & Wissal Ben Arfi, 2021. "CatBoost model and artificial intelligence techniques for corporate failure prediction," Post-Print hal-05238300, HAL.
    • Handle: RePEc:hal:journl:hal-05238300
    DOI: 10.1016/j.techfore.2021.120658
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    Citations

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    Cited by:

    1. Lin, Yu-Cheng & Padliansyah, Roni & Lu, Yu-Hsin & Liu, Wen-Rang, 2025. "Bankruptcy prediction: Integration of convolutional neural networks and explainable artificial intelligence techniques," International Journal of Accounting Information Systems, Elsevier, vol. 56(C).
    2. Rosanne Larocque & Anne-Marie Boulé & Quentin Cappart, 2025. "Estimating Road Construction Costs with Explainable Machine Learning," Interfaces, INFORMS, vol. 55(2), pages 137-153, March.
    3. Owoo, Natalia & Odei-Mensah, Jones, 2025. "Hierarchical clustering-based early warning model for predicting bank failures: Insights from Ghana's financial sector reforms (2017–2019)," Research in International Business and Finance, Elsevier, vol. 77(PB).
    4. Zhongjie Li, 2025. "Bridging pedagogy and technology: a generative AI and IoT approach to transformative English language education," Humanities and Social Sciences Communications, Palgrave Macmillan, vol. 12(1), pages 1-13, December.
    5. Shahbazbegian, Amirhussein & Ghiasi, Mahmoud, 2026. "Developing a machine learning (ML) based graphical user interface (GUI) for significant wave height (SWH) forecasting to support wave energy converters (WECs) operations planning," Renewable Energy, Elsevier, vol. 256(PG).
    6. Yao, Haixiang & Wan, Chunzhuo, 2025. "Multi-factor portfolio optimization: A combined random Forest–AdaBoost model with cost-sensitive learning11This paper was supported by the National Natural Science Foundation of China (Nos. 71,871,071, 72071051); the Natural Science Foundation of Gua," Pacific-Basin Finance Journal, Elsevier, vol. 94(C).

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