An Overview on the Landscape of R Packages for Credit Scoring
This is an incremental contribution that helps users in the credit scoring industry navigate available R packages for modeling tasks.
The paper provides a structured overview of R packages for credit scoring, addressing the gap in dedicated tools for this domain, and aims to guide users in selecting appropriate functions for scorecard development processes.
The credit scoring industry has a long tradition of using statistical tools for loan default probability prediction and domain specific standards have been established long before the hype of machine learning. Although several commercial software companies offer specific solutions for credit scorecard modelling in R explicit packages for this purpose have been missing long time. In the recent years this has changed and several packages have been developed which are dedicated to credit scoring. The aim of this paper is to give a structured overview on these packages. This may guide users to select the appropriate functions for a desired purpose and further hopefully will contribute to directing future development activities. The paper is guided by the chain of subsequent modelling steps as they are forming the typical scorecard development process.