ScholarWorks@중앙대학교

초록

This study aims to explain the determinants of bank failures by evaluating existing prediction models. We reveal that existing machine learning models often result in inconsistent explanations within the financial domain, leading to flawed conclusions and suboptimal decision-making. Therefore, we propose the use of a neural additive model that combines the transparency of a general additive model with the high performance of a neural network. Our findings show that this approach enhances the interpretation of explanatory variables in bank failure predictions, offering valuable insights for improving financial risk assessment.

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