Systems-Oriented Explainable AI for Corporate Bankruptcy Early Warning: A Deep Learning Framework for Risk-Attribution Analysis

Abstract

Corporate bankruptcy early warning has often been treated as a binary classification task, yet financial distress is better understood as the outcome of interacting financial conditions that must be interpreted within practical risk-management contexts. To address this issue, this study proposes a systems-oriented explainable artificial intelligence framework for corporate bankruptcy early warning. The framework implements a Hierarchical LFRM-MACI architecture in which the Local Feature Refinement Module (LFRM) refines representations within profitability, solvency, liquidity, efficiency, and growth/shareholder performance subsystems, and cross-subsystem attention models their interactions. The framework is evaluated on the public UCI Taiwanese Bankruptcy Prediction dataset under balanced and moderately imbalanced training settings. For reporting clarity, the benchmark methods are classified into traditional machine learning methods and deep learning methods; traditional tabular learners are included in the formal single-split empirical comparison, while the proposed method’s contribution is positioned as a structured deep representation with an attribution workflow rather than as an overall superiority claim over traditional machine learning models. In the single 70%/30% validation split, the proposed model obtains ROC-AUC values of 0.9286 and 0.9269 under the 1:1.0 and 1:2.5 settings, respectively. In repeated 5-fold cross-validation with five repetitions, its ROC-AUC is 0.8940 [0.8777, 0.9103] under 1:1.0 and 0.9135 [0.9001, 0.9269] under 1:2.5. To examine the interpretability of the predictions, Permutation Feature Importance (PFI) and SHAP are applied to identify subsystem-level attribution patterns across major financial subsystems. The explanation results highlight influential predictors associated with leverage pressure, profitability and asset structure, liquidity, operating efficiency, and growth/shareholder performance. These findings indicate that explainable AI can support corporate bankruptcy early warning when predictive benchmarking is combined with transparent and auditable attribution analysis for financial decision-making.