The Power of Credit Scoring: Evaluating Machine Learning and Traditional Models in Swedish Retail Banking
Abstract
In this paper, we investigate and compare different credit scoring models, with
special attention paid to machine learning approaches outperforming traditional
models. We explore a recently proposed method called the PLTR model, which is
a combination of machine learning and traditional logistic regression. In addition,
we examine the models’ performance and analyze the economic impact for different
class weights. The main purpose of this paper was to identify the most effective
and practical approach for credit scoring in the Swedish retail banking context.
The findings suggest that the model that most accurately predicts defaults is the
random forest, but at a high cost of interpretability due to the models’ complexity.
According to our findings, the optimal substitute for the random forest is a penalized
logistic regression, as it compensates with interpretability, for slightly less accurate
predictions.
Degree
Master 2-years
Other description
MSc in Finance
Collections
View/ Open
Date
2023-06-29Author
von der Burg, Emma
Strömberg, Saga
Series/Report no.
2023:116
Language
eng