Business Failure Prediction From Textual and Tabular Data With Sentence-Level Interpretations

Henri Arno (), Klaas Mulier, Joke Baeck and Thomas Demeester
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Henri Arno: Ghent University - imec
Joke Baeck: Ghent University
Thomas Demeester: Ghent University - imec

Annals of Operations Research, 2025, vol. 353, issue 2, No 9, 667-692

Abstract: Abstract Business failure prediction models are crucial in high-stakes domains like banking, insurance, and investing. In this paper, we propose an interpretable model that combines numerical and sentence-level textual features through a well-known attention mechanism. Our model demonstrates competitive performance across various metrics, and the attention weights help identify sentences intuitively linked to business failure, offering a form of interpretability. Furthermore, our findings highlight the strength of traditional financial ratios for business failure prediction while textual data—particularly when represented as keywords—is mainly useful to correctly classify corporate disclosures where the possibility of failure is explicitly mentioned.

Keywords: Decision support systems; Business failure prediction; Natural language processing; Text analytics (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s10479-025-06574-z

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