The scope of this study is to investigate the capability of AI methods to accurately detect and predict credit risks based on retail borrowers' features. The comparison of logistic regression, decision tree, and random forest showed that machine learning methods are able to predict credit defaults of individuals more accurately than the logit model. Furthermore, it was demonstrated how random forest and decision tree models were more sensitive in detecting default borrowers.
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MA Bohdan Popovych is a data scientist and a researcher in quantitative finance. The main scientific focus of the author is application of advanced analytics and artificial intelligence in finance and economics.
The scope of this study is to investigate the capability of AI methods to accurately detect and predict credit risks based on retail borrowers' features. The comparison of logistic regression, decision tree, and random forest showed that machine learning methods are able to predict credit defaults of individuals more accurately than the logit model. Furthermore, it was demonstrated how random forest and decision tree models were more sensitive in detecting default borrowers.
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Taschenbuch. Zustand: Neu. Application of AI in Credit Scoring Modeling | Bohdan Popovych | Taschenbuch | xv | Englisch | 2022 | Springer Gabler | EAN 9783658401795 | Verantwortliche Person für die EU: Springer Gabler in Springer Science + Business Media, Tiergartenstr. 15-17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu. Artikel-Nr. 125804533
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Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - The scope of this study is to investigate the capability of AI methods to accurately detect and predict credit risks based on retail borrowers' features. The comparison of logistic regression, decision tree, and random forest showed that machine learning methods are able to predict credit defaults of individuals more accurately than the logit model. Furthermore, it was demonstrated how random forest and decision tree models were more sensitive in detecting default borrowers. Artikel-Nr. 9783658401795
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