I understand that there won't necessarily be a great answer to this, but I'd like to hear what people would do in this situation.
Here's the data situation: I have about 30K policy records of which there are about 400 claims associated with them. I have an additional 400 claims that can't be matched to a policy. This is due to really old data for which the policy information wasn't saved in a database. The point of the analysis is to see what the Loss Ratio would be for a potential policy - the loss ratio being loss/premium.
At this point, I've thought about maybe creating a loss distribution using all 800 claims and bootstrap from this distribution to get additional losses. Then I'd perhaps just generate random policies (along with some variables that I'd be testing) and just randomly assigning claims to them, and try doing a GLM from there. Obviously, this introduces quite a lot of bias, and I'm not really sure if this is the 'right' way at all... My statistical knowledge is limited, so please throw me some ideas that I could possibly try.