可解释的银行倒闭预测模型：反事实解释以降低倒闭风险
Explainable bank failure prediction models: Counterfactual explanations to reduce the failure risk
AI总结：
本文研究如何通过反事实解释提升银行倒闭预测模型的可解释性，评估了多种反事实生成方法，发现成本敏感方法能产生更高质量的解释，从而降低银行倒闭风险。
摘要：
银行倒闭预测模型的准确性和可解释性至关重要。尽管可解释模型如逻辑回归因其可解释性而被青睐，但复杂模型如随机森林、支持向量机和深度学习提供更高的预测性能但可解释性较低。这些被称为黑箱的模型难以得出可操作的见解。为解决这一挑战，建议使用反事实解释。这些解释展示了输入变量的变化如何影响模型输出，并建议如何降低银行倒闭风险。关键挑战在于选择最有效的反事实生成方法，该方法应展示有效性、接近性、稀疏性和合理性。本文评估了几种反事实生成方法：WhatIf、多目标和最近实例反事实解释，还探讨了如欠采样、过采样、SMOTE和成本敏感方法等重采样方法，以解决美国银行倒闭预测中的数据不平衡问题。结果表明，最近实例反事实解释方法产生更高质量的反事实解释，主要使用成本敏感方法。总体而言，多目标反事实和最近实例反事实解释方法在有效性、接近性和稀疏性指标上优于其他方法，而成本敏感方法提供最理想的反事实解释。这些发现突显了反事实生成方法在不同平衡策略和机器学习模型中的性能差异，为提升黑箱银行倒闭预测模型的实用性提供了有价值的策略。
英文摘要：
The accuracy and understandability of bank failure prediction models are crucial. 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 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, and plausibility. The paper evaluates several counterfactual generation methods: WhatIf, 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 Multi Objective Counterfactual and Nearest Instance Counterfactual Explanation methods outperform others regarding validity, proximity, and sparsity metrics, with the cost sensitive approach providing the most desirable counterfactual explanations. 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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
