Neural networks and tree-based credit scoring models
|Author:||Bc. Tomáš Turlík|
|Year:||2018 - summer|
|Leaders:|| prof. PhDr. Ladislav Krištoufek Ph.D.
|Work type:|| Bachelors
|Awards and prizes:|
|Abstract:||The most basic task in credit scoring is to classify potential borrowers as
"good" or "bad" based on the probability that they would default in the
case they would be accepted. In this thesis we compare widely used logistic
regression, neural networks and tree-based ensemble models. During
the construction of neural network models we utilize recent techniques and
advances in the field of deep learning, while for the tree-based models we
use popular bagging, boosting and random forests ensembling algorithms.
Performance of the models is measured by ROC AUC metric, which should
provide better information value than average accuracy alone. Our results
suggest small or even no difference between models, when in the best case
scenario neural networks, boosted ensembles and stacked ensembles result in
only approximately 1%−2% larger ROC AUC value than logistic regression.