Dansk
Application of AI in Credit Scoring Modeling e-bog
656,09 DKK
(inkl. moms 820,11 DKK)
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 f...
E-bog
656,09 DKK
Forlag
Springer Gabler
Udgivet
7 december 2022
Genrer
Finance and the finance industry
Sprog
English
Format
pdf
Beskyttelse
LCP
ISBN
9783658401801
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.