基于因果机器学习的信用风险预测:贝叶斯网络学习、违约推断和解释

IF 3.4 3区 经济学 Q1 ECONOMICS
Jiaming Liu, Xuemei Zhang, Haitao Xiong
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引用次数: 0

摘要

模型的预测和解释能力对于金融风险管理至关重要。本研究旨在通过数据处理、结构学习、参数学习和推理解释四个阶段,在结构化因果网络中进行信用风险预测,并利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了信用违约的结果。我们还将我们的模型与事后解释模型局部可解释模型-不可知论解释(LIME)和夏普利加法解释(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的机器学习模型,与集合模型的性能相似。此外,贝叶斯网络通过考虑特征之间的因果关系,为了解特征之间的相互作用提供了有价值的见解,并能评估单个特征如何影响预测结果。在本研究中,我们进行了假设分析,以评估各种条件下的信贷违约概率。这种分析为决策者提供了必要的工具,使其能够对借款人的风险状况做出明智的判断。因此,就预测性能和可解释性而言,我们认为贝叶斯网络是信用风险预测模型的可行工具。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Credit risk prediction based on causal machine learning: Bayesian network learning, default inference, and interpretation

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.

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来源期刊
CiteScore
5.40
自引率
5.90%
发文量
91
期刊介绍: The Journal of Forecasting is an international journal that publishes refereed papers on forecasting. It is multidisciplinary, welcoming papers dealing with any aspect of forecasting: theoretical, practical, computational and methodological. A broad interpretation of the topic is taken with approaches from various subject areas, such as statistics, economics, psychology, systems engineering and social sciences, all encouraged. Furthermore, the Journal welcomes a wide diversity of applications in such fields as business, government, technology and the environment. Of particular interest are papers dealing with modelling issues and the relationship of forecasting systems to decision-making processes.
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