I couldn't find the right phrase to use so let me explain what I mean with this question.
Lets say I build a logistic regression model for predicting the probability a customer will go rogue and stop paying his debt. Lets assume it is the best one you can build with the data I have and no further improvement with this exact data can be made.
As time passes more data is collected, of course, and some variable can (they can, right?) become less "useful/significant" to the model and the opposite can happen to other variables I am currently using. At the same time other variables can emerge and could prove to be useful to the prediction.
That being said, I was wondering how often is it "okay" to re-train and re-test your model? Meaning I extract data, split it into training and test set and do everything else as if I am building a new model.
It probably depends on the quantities of data, economic situation and other factors due to the fundamentals of the data? All things being equal (no major real world events have occurred) is there a recommended interval of time after which it is advised to re-evaluate or re-build a model (if needed of course)?