Suppose you have a set of insurance claims and you want to predict the probability that a claim will give rise to a complaint from some features of the claim at a certain point in time such as time from the first notification of loss to claim closure, time to first payment, etc.
The starting point is a data set of claims with some measures at time t and a flag for claims that currently have a complaint open on them. The dataset is quite unbalanced as the complaint rate is <3%.
I was thinking to model the response variable Y ('the claim has triggered a complaint') as a Bernoulli random variable and then to use logistic regression to model the probability of a claim triggering a complaint.
Do you think that this is an appropriate starting point? If so, what would you suggest to be careful with? If not, what other models would you use instead to model this probability?
Ideally, I want to update these probabilities as the claim progresses through its lifecycle, but I want to avoid Bayesian methods at this stage.