Variable Selection in the Credit Card Industry
Moez Hababou, Alec Y. Cheng, Ray Falk · 2006
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.