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.
Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.
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Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 100 pp. Englisch. N° de réf. du vendeur 9783658401795
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Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. 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 me. N° de réf. du vendeur 757484132
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Taschenbuch. Etat : 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. N° de réf. du vendeur 9783658401795
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