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

Petr Hajek (), Jean-Michel Sahut () and Vladimir Olej ()
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Petr Hajek: University of Pardubice
Jean-Michel Sahut: IDRAC Business School
Vladimir Olej: University of Pardubice

Annals of Operations Research, 2025, vol. 353, issue 1, No 12, 279 pages

Abstract: 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.

Keywords: Credit rating; Financial distress; Multi-criteria decision making; Fuzzy-TOPSIS-Sort-C; Fuzzy best-worst approach; Fuzzy cognitive map (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s10479-024-06183-2

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