  It has been shown several times in the literature that telematics data
collected in motor insurance help to better understand an insured's driving
risk. Insurers that use this data reap several benefits, such as a better
estimate of the pure premium, more segmented pricing and less adverse
selection. The flip side of the coin is that collected telematics information
is often sensitive and can therefore compromise policyholders' privacy.
Moreover, due to their large volume, this type of data is costly to store and
hard to manipulate. These factors, combined with the fact that insurance
regulators tend to issue more and more recommendations regarding the collection
and use of telematics data, make it important for an insurer to determine the
right amount of telematics information to collect. In addition to traditional
contract information such as the age and gender of the insured, we have access
to a telematics dataset where information is summarized by trip. We first
derive several features of interest from these trip summaries before building a
claim classification model using both traditional and telematics features. By
comparing a few classification algorithms, we find that logistic regression
with lasso penalty is the most suitable for our problem. Using this model, we
develop a method to determine how much information about policyholders' driving
should be kept by an insurer. Using real data from a North American insurance
company, we find that telematics data become redundant after about 3 months or
4,000 kilometers of observation, at least from a claim classification
perspective.
