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<p>The accuracy and understandability of bank failure prediction models are crucial because inaccurate predictions may lead to undetected financial distress, resulting in economic losses. Likewise, models that are not interpretable hinder regulators and bank managers from taking timely and effective corrective actions. While interpretable models like logistic regression are favored for their explainability, complex models such as random forest, support vector machines, and deep learning offer higher predictive performance but lower explainability. These models, known as black boxes, make it difficult to derive actionable insights. To address this challenge, using counterfactual explanations is suggested. These explanations demonstrate how changes in input variables can alter the model’s output and suggest ways to mitigate bank failure risk. The key challenge lies in selecting the most effective method for generating useful counterfactuals, which should demonstrate <i>validity</i>, <i>proximity</i>, <i>sparsity</i>, <i>plausibility</i>, and <i>diversity</i>. The paper evaluates several model-agnostic counterfactual generation methods: What-If, Multi-Objective, and Nearest Instance Counterfactual Explanation, and also explores resampling methods like undersampling, oversampling, SMOTE, and the cost-sensitive approach to address data imbalance in bank failure prediction in the US. The results indicate that the Nearest Instance Counterfactual Explanation method yields higher-quality counterfactual explanations, mainly using the cost-sensitive approach. Overall, the Nearest Instance Counterfactual Explanation method outperforms others regarding <i>validity</i>, <i>proximity</i>, <i>sparsity</i>, and <i>diversity</i> metrics, with the cost-sensitive approach providing the most desirable counterfactual explanations. The combined use of Random Forest with cost-sensitive counterfactual generation further enhances both predictive accuracy and explanation quality, demonstrating a balanced and reliable framework for interpretable bank failure prediction. These findings highlight the variability in the performance of counterfactual generation methods across different balancing strategies and machine learning models, offering valuable strategies to enhance the utility of black-box bank failure prediction models.</p>

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Explainable Bank Failure Prediction Models: Counterfactual Explanations to Reduce the Failure Risk

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月13日

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  • Seyma Gunonu,
  • Gizem Altun,
  • Mustafa Cavus

摘要

The accuracy and understandability of bank failure prediction models are crucial because inaccurate predictions may lead to undetected financial distress, resulting in economic losses. Likewise, models that are not interpretable hinder regulators and bank managers from taking timely and effective corrective actions. While interpretable models like logistic regression are favored for their explainability, complex models such as random forest, support vector machines, and deep learning offer higher predictive performance but lower explainability. These models, known as black boxes, make it difficult to derive actionable insights. To address this challenge, using counterfactual explanations is suggested. These explanations demonstrate how changes in input variables can alter the model’s output and suggest ways to mitigate bank failure risk. The key challenge lies in selecting the most effective method for generating useful counterfactuals, which should demonstrate validity, proximity, sparsity, plausibility, and diversity. The paper evaluates several model-agnostic counterfactual generation methods: What-If, Multi-Objective, and Nearest Instance Counterfactual Explanation, and also explores resampling methods like undersampling, oversampling, SMOTE, and the cost-sensitive approach to address data imbalance in bank failure prediction in the US. The results indicate that the Nearest Instance Counterfactual Explanation method yields higher-quality counterfactual explanations, mainly using the cost-sensitive approach. Overall, the Nearest Instance Counterfactual Explanation method outperforms others regarding validity, proximity, sparsity, and diversity metrics, with the cost-sensitive approach providing the most desirable counterfactual explanations. The combined use of Random Forest with cost-sensitive counterfactual generation further enhances both predictive accuracy and explanation quality, demonstrating a balanced and reliable framework for interpretable bank failure prediction. These findings highlight the variability in the performance of counterfactual generation methods across different balancing strategies and machine learning models, offering valuable strategies to enhance the utility of black-box bank failure prediction models.