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Credit rating prediction using a fuzzy MCDM approach with criteria interactions and TOPSIS sorting

Author

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  • Petr Hajek

    (University of Pardubice)

  • Jean-Michel Sahut

    (IDRAC Business School)

  • Vladimir Olej

    (University of Pardubice)

Abstract

Multi-criteria decision making (MCDM) provides effective methods for dealing with the challenge of sorting credit ratings. This paper presents a novel data-driven MCDM sorting approach to predicting credit ratings. Our methodology combines the fuzzy TOPSIS-Sort-C model with the fuzzy best-worst approach, supported by a fuzzy cognitive map, to effectively deal with criteria interactions. This approach provides a corporate credit risk assessment, taking into account the uncertainties in credit risk assessment and relevance of its criteria by using fuzzy c-means and correlation-based feature selection. Our empirical analysis of 1138 US companies demonstrates the reliability of our model in dealing with a range of financial and non-financial indicators. The results demonstrate the potential of our methodology in credit rating assessment, with a good predictive performance relative to existing models.

Suggested Citation

  • Petr Hajek & Jean-Michel Sahut & Vladimir Olej, 2025. "Credit rating prediction using a fuzzy MCDM approach with criteria interactions and TOPSIS sorting," Annals of Operations Research, Springer, vol. 353(1), pages 251-279, October.
    • Handle: RePEc:spr:annopr:v:353:y:2025:i:1:d:10.1007_s10479-024-06183-2
    DOI: 10.1007/s10479-024-06183-2
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