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.