An explainable artificial intelligence approach for financial distress prediction

•Propose a new explainable artificial intelligence approach for financial distress prediction.•Take financial distress prediction as an applied setting environment, analyze who the external stakeholders are and what their interpretative desires are, and then establish an explainable framework.•Apply shapley additive explanations, partial dependence plots and counterfactual explanations to generate local explanations and global explanations to improve the transparency and utilization of “black box” model.•Implement a whole process ensemble model from feature selection to predictor construction to maximize the effectiveness of ensemble.•Design a two-stage ensemble model integrated with a filter and wrapper technique for feature selection.