So I’ve completed a complex rate revision where some segmentation is being added, and there are a number of moving parts. To understand what’s going on, I’ve got my policy level before and after premiums and the most important field, percent change.
Then the leadership asks, quite reasonably, what’s driving the change? When changes are simple, explanations are too, base rate only revisions lead to uniform changes. Revisions where base rates and one or two segments change can be explained by big changes to policies who had big factor changes.
But this time, I made many changes, so I’m not entirely certain what is driving the changes. So the fun idea is, build a model!
The setup would be simple, policy level attributes readily available from the data, and a response variable, the percent change.
And the question, and the reason for the post, is what is the best model to build?
In my head, I thought classification and regression trees would be fine, but say you had 2000 policies, and want to build something to predict the biggest driver of premium increase/decrease, how would you do it?
I’m thinking something in R studio because I’ve worked in this platform before. I’m in the process of using the rpart function, but am not sure if anything else is good out there, and not to difficult to use.
Any recommendations? Thanks!
Till All are One,