The credit card industry is particular in its need for a wide variety of models and the wealth of data collected on
customers and prospects. We propose a methodology to select variables for predictive modeling purposes out of the
plethora of data available using a combination of Oblique Component Analysis (PROC VARCLUS), Information Value
(IV) and Weight Of Evidence (WOE) analysis, and business intelligence. Our tools enable us to quickly identify the
most informative variables for logistic regression models.

Key words:  SAS，Logistic regression, scorecard,  variable selection, bank