Bankruptcy prediction using machine learning and Shapley additive explanations

Hoang Hiep Nguyen (), Jean-Laurent Viviani () and Sami Ben Jabeur ()
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Hoang Hiep Nguyen: CREM - Centre de recherche en économie et management - UNICAEN - Université de Caen Normandie - NU - Normandie Université - UR - Université de Rennes - CNRS - Centre National de la Recherche Scientifique
Jean-Laurent Viviani: CREM - Centre de recherche en économie et management - UNICAEN - Université de Caen Normandie - NU - Normandie Université - UR - Université de Rennes - CNRS - Centre National de la Recherche Scientifique
Sami Ben Jabeur: ESDES - ESDES, Lyon Business School - UCLy - UCLy - UCLy (Lyon Catholic University), UCLy - UCLy (Lyon Catholic University)

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Abstract: Recently, ensemble-based machine learning models have been widely used and have demonstrated their efficiency in bankruptcy prediction. However, these algorithms are black box models and people cannot understand why they make their forecasts. This explains why interpretability methods in machine learning attract attention from many artificial intelligence researchers. In this paper, we evaluate the prediction performance of Random Forest, LightGBM, XGBoost, and NGBoost (Natural Gradient Boosting for probabilistic prediction) for French firms from different industries with the horizon of 1-5 years. We then use Shapley Additive Explanations (SHAP), a model-agnostic method to explain XGBoost, one of the best models for our data. SHAP can show how each feature impacts the output from XGBoost. Furthermore, single prediction can also be explained, thus allowing black box models to be used in credit risk management.

Keywords: Shapley additive explanations; Explainable machine learning; Bankruptcy prediction; Ensemble-based model; XGBoost (search for similar items in EconPapers)
Date: 2023
New Economics Papers: this item is included in nep-big, nep-cfn, nep-cmp, nep-gth and nep-rmg
Note: View the original document on HAL open archive server: https://hal.science/hal-04223161v1
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Citations: View citations in EconPapers (2)

Published in Review of Quantitative Finance and Accounting, In press, ⟨10.1007/s11156-023-01192-x⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-04223161

DOI: 10.1007/s11156-023-01192-x

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