A necessary condition for a good binning algorithm in credit scoring

Binning is a categorization process to transform a continuous variable into a small set of groups or bins. Binning is widely used in credit scoring. In particular, it can be used to define the Weight of Evidence (WOE) transformation. In this paper, we first derive an explicit solution to a logistic regression model with one independent variable that has undergone a WOE transformation. We then use this explicit solution as a necessary condition for a good binning algorithm, thus providing a simple way to catch binning errors.