Interpretable machine learning for imbalanced credit scoring datasets

Chen, Yujia; Calabrese, Raffaella; Martin-Barragan, Belen

doi:10.1016/j.ejor.2023.06.036

Cited by 214Explainable Artificial Intelligence (XAI)Imbalanced Data Classification TechniquesFinancial Distress and Bankruptcy Prediction

Interpretable machine learning for imbalanced credit scoring datasets

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Chen, Yujia, Calabrese, Raffaella, Martin-Barragan, Belen·2024·European Journal of Operational Research

DOI 10.1016/j.ejor.2023.06.036

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Abstract

The class imbalance problem is common in the credit scoring domain, as the number of defaulters is usually much less than the number of non-defaulters. To date, research on investigating the class imbalance problem has mainly focused on indicating and reducing the adverse effect of the class imbalance on the predictive accuracy of machine learning techniques, while the impact of that on machine learning interpretability has never been studied in the literature. This paper fills this gap by analysing how the stability of Local Interpretable Model-agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP), two popular interpretation methods, are affected by class imbalance. Our experiments use 2016-2020 UK residential mortgage data collected from European Datawarehouse. We evaluate the stability of LIME and SHAP on datasets of progressively increased class imbalance. The results show that interpretations generated from LIME and SHAP are less stable as the class imbalance increases, which indicates that the class imbalance does have an adverse effect on machine learning interpretability. To check the robustness of our outcomes, we also analyse two open-source credit scoring datasets and we obtain similar results.

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Authors: Chen, Y., Calabrese, R., & Martin-Barragan, B.
Affiliations: University of Edinburgh (GB)
Corresponding author: Chen, Yujia
ORCID: Chen, Yujia
Calabrese, Raffaella
Venue: European Journal of Operational Research
Publisher: Elsevier BV
Published: 2024-01
Volume: 312
Issue: 1
Pages: 357-372
ISSN: 0377-2217 (ISSN-L)
DOI: 10.1016/j.ejor.2023.06.036
OpenAlex: W4381997116
Cited by: 190
References: 71
Citations by year: 2026: 72 · 2025: 62 · 2024: 49 · 2023: 4 · 2022: 2 · 2021: 1
FWCI: 16.55
Type: journal article
Language: en
Open access: hybrid
doi.org
Licence: cc-by
www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/legal/tdmrep-license (tdm)
creativecommons.org/licenses/by/4.0/ (vor)
Locations: European Journal of Operational Research (publishedVersion, cc-by)
RePEc: Research Papers in Economics (submittedVersion)
Edinburgh Research Explorer (University of Edinburgh) (submittedVersion, cc-by)
Funders: Economic and Social Research Council (ES/W010259/1)
Topics: Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
Physical Sciences › Computer Science › Artificial Intelligence › Imbalanced Data Classification Techniques
Social Sciences › Business, Management and Accounting › Accounting › Financial Distress and Bankruptcy Prediction
Keywords: class imbalance, credit scoring, interpretability, lime, SHAP, explanation stability, mortgage default, residential mortgages, default prediction

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Interpretable machine learning for imbalanced credit scoring datasets

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