Subscribe to DSC Newsletter

Building a good predictive model for credit risk

A colleague of mine wanted to understand how to build predictive models, and asked if I had a strategy for building them. I thought it would be useful to share this. For more details about each stage see my personal blog (My suggested strategy for building a “good” predictive model).

 

Stage 1 – Perform initial investigations

Stage 2 - Getting the data ready

Stage 3 - Modelling

Stage 4 - Check the model

Stage 5 - Start again

 

The bottom line: It’s an iterative process and it might take some time to get a model that’s acceptable in terms of fit, and acceptable to business users. Always, always, always and at each stage consult with the business to check on ethical issues, applicability of the model, and that the model can be implemented.

Ian Morton worked in credit risk for big banks for a number of years. He learnt about how to (and how not to) build “good” statistical models in the form of scorecards using the SAS Language.

 

George E. P. Box “Essentially, all models are wrong, but some are useful”

Views: 1466

Comment

You need to be a member of AnalyticBridge to add comments!

Join AnalyticBridge

Comment by Jozo Kovac on May 10, 2013 at 2:12am

Great and very practical article! 

On Data Science Central

© 2019   AnalyticBridge.com is a subsidiary and dedicated channel of Data Science Central LLC   Powered by

Badges  |  Report an Issue  |  Privacy Policy  |  Terms of Service