I am building a predictive model using logistic regression to predict if an applicant should be given a credit product based on their telecommunication data of the previous eight months postpaid subscribers only. But the behaviors I am trying to capture can vary with time for the entire population. Example: data usage, spending power, etc will vary with time. So the question is how to build a model that performs consistently across time. Else, a suggestion on how to refresh/update the model with time should help as well.
Every time you conduct the analysis, you could use the latest obtained data points, for instance, data collected in the last 180 days. I suppose the applicants' data is stored and updated on database maintained by you or a third party. So a script would help to do the routine work, i.e., data manipulation, etc.