The predictive and interpretable power of models is crucial for financial risk management. The purpose of this study was to perform credit risk prediction in a structured causal network with four stages—data processing, structural learning, parameter learning, and interpretation of inferences—and use six real credit datasets to conduct empirical research on the proposed model. Compared with traditional machine learning algorithms, we comprehensively explain the results of credit default through forward and reverse reasoning. We also compared our model with the post hoc interpretation models local interpretable model‐agnostic explanations (LIME) and shapley additive explanations (SHAP) to verify the interpretability of Bayesian networks. The experimental results show that the prediction performance of Bayesian networks is superior to traditional machine learning models and similar to the performance of ensemble models. Furthermore, Bayesian networks offer valuable insights into the interplay of features by considering their causal relationships and enable an assessment of how individual features influence the prediction outcome. In this study, what‐if analysis was performed to assess credit default probabilities under various conditions. This analysis provides decision‐makers with the necessary tools to make informed judgments about the risk profile of borrowers. Consequently, we consider Bayesian networks as a viable tool for credit risk prediction models in terms of prediction performance and interpretability.

中文翻译：

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基于因果机器学习的信用风险预测：贝叶斯网络学习、违约推理和解释

模型的预测和解释能力对于金融风险管理至关重要。本研究的目的是在结构化因果网络中进行信用风险预测，包括数据处理、结构学习、参数学习和推论解释四个阶段，并使用六个真实信用数据集对所提出的模型进行实证研究。与传统的机器学习算法相比，我们通过正向和反向推理全面解释信用违约的结果。我们还将我们的模型与事后解释模型、局部可解释模型不可知解释（LIME）和沙普利附加解释（SHAP）进行了比较，以验证贝叶斯网络的可解释性。实验结果表明，贝叶斯网络的预测性能优于传统机器学习模型，与集成模型的性能相似。此外，贝叶斯网络通过考虑特征之间的因果关系，为特征之间的相互作用提供了有价值的见解，并能够评估各个特征如何影响预测结果。在本研究中，进行假设分析来评估各种条件下的信用违约概率。该分析为决策者提供了必要的工具，以便对借款人的风险状况做出明智的判断。因此，我们认为贝叶斯网络在预测性能和可解释性方面是信用风险预测模型的可行工具。