Explainability of Machine Learning Models for Bankruptcy Prediction

Abstract

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.

Bankruptcy Prediction for Sustainability of Businesses: The Application of Graph Theoretical Modeling

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Artificial Intelligence Techniques for Bankruptcy Prediction of Tunisian Companies: An Application of Machine Learning and Deep Learning-Based Models

“…Some authors apply the decision trees method (Aoki and Hosonuma 2004;Zibanezhad et al 2011;Begović and Bonić 2020), some others utilize various machine learning techniques such as genetic algorithm (Shin and Lee 2002;Kim and Han 2003;Davalos et al 2014), support vector machine (Shin et al 2005;Härdle et al 2005;Dellepiane et al 2015) and random forest (Joshi et al 2018;Ptak-Chmielewska and Matuszyk 2020;Gurnani et al 2021). Recently, several comparative analyses of machine learning models have been carried out to predict bankruptcy (Narvekar and Guha 2021;Park et al 2021;Bragoli et al 2022;Máté et al 2023;Martono and Ohwada 2023).…”

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