Cost-sensitive ensemble learning for bankruptcy prediction under extreme class imbalance

Abstract Corporate bankruptcy prediction is a high-stakes artificial intelligence (AI) task characterized by extreme class imbalance and asymmetric misclassification costs. Although ensemble learning models have shown strong predictive performance, most existing studies rely on cost-insensitive metrics and fixed decision thresholds, which can misrepresent real-world decision utility. This study reframes bankruptcy prediction as a decision-centric AI problem and proposes a cost-sensitive ensemble