Investigating the beneficial impact of segmentation-based modelling for credit scoring

Khaoula Idbenjra, Kristof Coussement () and Arno de Caigny ()
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Khaoula Idbenjra: LEM - Lille économie management - UMR 9221 - UA - Université d'Artois - UCL - Université catholique de Lille - Université de Lille - CNRS - Centre National de la Recherche Scientifique
Kristof Coussement: LEM - Lille économie management - UMR 9221 - UA - Université d'Artois - UCL - Université catholique de Lille - Université de Lille - CNRS - Centre National de la Recherche Scientifique
Arno de Caigny: LEM - Lille économie management - UMR 9221 - UA - Université d'Artois - UCL - Université catholique de Lille - Université de Lille - CNRS - Centre National de la Recherche Scientifique

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Abstract: Due to its vital role in financial risk management, credit scoring has been investigated extensively in extant information systems studies. However, most credit scoring studies rely on one-size-fits-all classifiers with logistic regression (LR) as a popular benchmark. Moreover, extant literature largely focuses on predictive performance as an evaluation criterion. To find a better balance between predictive performance and interpretability though, the current study investigates the beneficial impact of segmentation-based modelling by benchmarking the logit leaf model (LLM) which is based on LR and decision trees. By a large experimental setup using a real-life credit scoring data set containing 65,536 active customers, we find that LLM is a viable classifier over its constituent parts, i.e., LR and decision trees, and is very competitive to state-of-the-art credit decision making techniques (neural networks, support vector machines, bagging, boosting and random forests) on three evaluation metrics (AUC, top-decile lift and profit). Furthermore, we show its extraordinary interpretability capacities by proposing a new visualization based on the LLM output. In sum, the excellence of the LLM as a classifier for credit decision making problems stems from its ability to combine strong predictive performance with interpretable insights that in turn can inform managerial decisions.

Date: 2024-04
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Published in Decision Support Systems, 2024, 179, pp.114170. ⟨10.1016/j.dss.2024.114170⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-04543449

DOI: 10.1016/j.dss.2024.114170

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