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

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.