Estimating Car Insurance Premia: a Case Study in High-Dimensional Data Inference

Estimating insurance premia from data is a difficult regression problem for several reasons: the large number of variables, many of which are .discrete, We compare several machine learning methods for estimating insurance premia, and test them on a large data base of car insurance policies. We find that func(cid:173) tion approximation methods that do not optimize a squared loss, like Support Vector Machines regression, do not work well in this context. Compared methods include decision trees and generalized linear models. The best results are obtained with a mixture of experts, which better identifies the least and most risky contracts, and allows to reduce the median premium by charging more to the most risky customers.