Unveiling the Black Box: Nonlinear Effects of Digital Transformation on Financial Distress with XGBoost-SHAP Model

Existing research lacks consensus on how digital transformation affects financial distress, and traditional linear models struggle to capture complex variable dynamics. This study examines the link from prediction and association perspectives by constructing a high-precision financial risk prediction model and using explainable methods to explore feature contributions. Using 2010–2024 data from Chinese A-share listed companies, we apply Logit regression to assess the association between digital transformation and financial distress, and introduce an XGBoost model with SHAP for prediction and interpretability. Results show that XGBoost outperforms Logit (AUC, PR_AUC). Logit regression reveals a negative correlation between digital transformation and distress probability, which SHAP further supports. Higher digitalization corresponds to lower predicted risk, with non-linear characteristics: at low levels, marginal contributions fluctuate; beyond a threshold, the mitigation effect stabilizes. Feature importance identifies the asset-liability ratio as the top risk factor, while digital transformation and operating profit margin serve as key protective features. This paper contributes by constructing a digital transformation index and integrating it into the financial distress prediction model, comparing traditional econometric models with machine learning models, and using SHAP to provide an interpretable framework for understanding the digital transformation–financial distress association.