Credit Scoring Steps

Data - includes loading the data into CreditScoring as well as selecting correct modelling samples
Target and Partitioning - during this step the target variable will be selected and analysed, as well as different subsets of the modelling data set used for development, validation and testing which will be explained in the next chapter
Grouping - presents the most crucial modelling step, where measures such as Weight of Evidence (WoE) and Information Value (IV) are calculated. In a nutshell they help determine how input variables and their corresponding attributes affect the target variable.
Grouping of input variables into distinct categories is also performed in this step.
Selection - used to determine which variables will be used in the regression process
Scorecard - helps us determine the final scorecard and creates charts that visualize corresponding measures that describe the created scorecard’s ability to correctly classify “default” and “non-default” cases (clients).