Explainability of Machine Learning Models for Bankruptcy Prediction

As the amount of data increases, it is more likely that the assumptions in the existing economic analysis model are unsatisfied or make it difficult to establish a new analysis model. Therefore, there has been increased demand for applying the machine learning methodology to bankruptcy prediction due to its high performance. By contrast, machine learning models usually operate as black-boxes but credit rating regulatory systems require the provisioning of appropriate information regarding credit rating standards. If machine learning models have sufficient interpretablility, they would have the potential to be used as effective analytical models in bankruptcy prediction. From this aspect, we study the explainability of machine learning models for bankruptcy prediction by applying the Local Interpretable Model-Agnostic Explanations (LIME) algorithm, which measures the feature importance for each data point. To compare how the feature importance measured through LIME differs from that of models themselves, we first applied this algorithm to typical tree-based models that have ability to measure the feature importance of the models themselves. We showed that the feature importance measured through LIME could be a consistent generalization of the feature importance measured by tree-based models themselves. Moreover, we study the consistency of the feature importance through the model's predicted bankruptcy probability, which suggests the possibility that observations of important features can be used as a basis for the fair treatment of loan eligibility requirements.

Automated summary

With the increasing amount of data, there is a growing demand for utilizing machine learning methodologies in bankruptcy prediction. This study focuses on the interpretability of machine learning models, particularly measuring feature importance using the LIME algorithm, to address the black-box issue.