Machine Learning and Telematics for Risk Assessment in Auto Insurance

Sammanfattning: Pricing models for car insurance traditionally use variables related to the policyholder and the insured vehicle (e.g. car brand and driver age) to determine the premium. This can lead to situations where policyholders belonging to a group that is seen as carrying a higher risk for accidents wrongfully get a higher premium, even if the higher risk might not necessarily apply on a per- individual basis. Telematics data offers an opportunity to look at driving behavior during individual trips, enabling a pricing model that can be customized to each policyholder. While these additional variables can be used in a generalized linear model (GLM) similar to the traditional pricing models, machine learning methods can possibly unravel non-linear connections between the variables. Using telematics data, we build a gradient boosting model (GBM) and a neural network (NN) to predict the claim frequency of policyholders on a monthly basis. We find that both GBMs and NNs offer predictive power that can be generalized to data that has not been used in the training of the models. The results of the study also show that telematics data play a considerable role in the model predictions, and that the frequency and distance of trips are important factors in determining the risk using these models.