Investigating the beneficial impact of ...
Type de document :
Compte-rendu et recension critique d'ouvrage
Titre :
Investigating the beneficial impact of segmentation-based modelling for credit scoring
Auteur(s) :
Idbenjra, Khaoula [Auteur]
Lille économie management - UMR 9221 [LEM]
Coussement, Kristof [Auteur]
Lille économie management - UMR 9221 [LEM]
De Caigny, Arno [Auteur]
Lille économie management - UMR 9221 [LEM]
Lille économie management - UMR 9221 [LEM]
Coussement, Kristof [Auteur]
Lille économie management - UMR 9221 [LEM]
De Caigny, Arno [Auteur]
Lille économie management - UMR 9221 [LEM]
Titre de la revue :
Decision Support Systems
Pagination :
114170
Éditeur :
Elsevier
Date de publication :
2024-04
ISSN :
0167-9236
Discipline(s) HAL :
Sciences de l'Homme et Société/Gestion et management
Résumé en anglais : [en]
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 ...
Lire la suite >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.Lire moins >
Lire la suite >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.Lire moins >
Langue :
Anglais
Vulgarisation :
Non
Collections :
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