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.