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The paper proposes a two-stage consumer credit risk model that integrates class-imbalanced ensemble learning for predicting the probability of default (PD) and a regression ensemble for estimating exposure at default (EAD). This approach addresses the limitations of previous models that treated these parameters separately and demonstrates improved prediction accuracy and economic performance compared to state-of-the-art methods. The model utilizes multi-objective evolutionary feature selection to optimize b
- Author
- nguyenanh16102k4
- Language
- EN