Confidence-scaled margin adaptation boosting for interpretable financial distress prediction

Financial distress prediction (FDP) is paramount for economic stability in an increasingly volatile global landscape. Existing statistical and machine learning models confront two persistent challenges: the inherent class imbalance in financial datasets, where distress cases are rare; and the poor interpretability of complex black-box models, which hinders trust in high-stakes contexts. To address these limitations, we propose the XGBoost-CSMA, a reweighting-based boosting ensemble framework for interpretable imbalanced financial distress prediction. Departing from cost-sensitive methods that rely on static, expert-defined cost matrices, our confidence-scaled margin adaptation (CSMA) objective dynamically adjusts predictive margins based on confidence levels. This enables automatic adaptation to varying financial distress signals without predefined costs, mitigating imbalance while preserving discriminative power for critical borderline cases. Furthermore, XGBoost-CSMA embeds TreeSHAP explanations as an intrinsic component of model validation, creating a feedback loop that enhances both predictive performance and decision transparency. We also provide the theoretical foundations for efficiently integrating cost-sensitive learning into gradient boosting. Empirical assessments utilizing real-world corporate financial datasets substantiate the superior efficacy of XGBoost-CSMA relative to state-of-the-art baselines, a conclusion reinforced by substantial improvements in the geometric mean and true positive rate that underscore its enhanced sensitivity to distress signals. Furthermore, this predictive advancement is coupled with the provision of transparent risk interpretations, thereby ensuring that the model delivers both high diagnostic accuracy and actionable decision support.