A Dual-Encoder Approach to Bankruptcy Prediction With Financial Ratios and Corporate Disclosures

Accurate and early prediction of corporate bankruptcy is critical for financial risk assessment and decision-making. Traditional models rely solely on structured financial ratios, overlooking valuable qualitative information embedded in corporate disclosures. This paper proposes a multimodal deep learning framework that integrates structured accounting data with unstructured Management Discussion and Analysis (MD&A) text to improve bankruptcy prediction. The model employs a dual-encoder architecture with a gated fusion mechanism to dynamically combine numerical and textual representations. We evaluate our approach on three publicly available datasets: the U.S. Corporate Bankruptcy dataset, the Polish Companies Bankruptcy dataset, and the ECL dataset. Experimental results demonstrate that the proposed model consistently outperforms classical statistical methods and unimodal deep learning baselines across multiple metrics, including accuracy, F1-score, ROC-AUC, and PR-AUC. The model also generalizes well across domains and time horizons, offering a robust and interpretable tool for financial risk analysis. This work highlights the importance of leveraging both quantitative and qualitative signals for more reliable bankruptcy forecasting.