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.
Prenumerera på vårt nyhetsbrev för att få fantastiska erbjudanden och inspiration för din nästa läsning.
Genom att registrera dig accepterar du vår personuppgiftspolicy.