Abstract
The credit scoring industry has a long tradition of using statistical models for loan default probability prediction. Since this time methodology has strongly evolved, and most of the current research is dedicated to modern machine learning algorithms which contrasts with common practice in the finance industry where traditional regression models still denote the gold standard. In addition, strong emphasis is put on a preliminary binning of variables. Reasons for this may be not only the regulatory requirement of model comprehensiveness but also the possibility to integrate analysts’ expert knowledge in the modelling process. Although several commercial software companies offer specific solutions for modelling credit scorecards, open-source frameworks for this purpose have been missing for a long time. In recent years, this has changed, and today several R packages for credit scorecard modelling are available. This brings the potential to bridge the gap between academic research and industrial practice. The aim of this paper is to give a structured overview of these packages. It may guide users to select the appropriate functions for the desired purpose. Furthermore, this paper will hopefully contribute to future development activities.
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