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In order to determine a suitable automobile insurance policy premium oneneeds to take into account three factors, the risk associated with the driversand cars on the policy, the operational costs associated with management of thepolicy and the desired profit margin. The premium should then be some functionof these three values. We focus on risk assessment using a Data Scienceapproach. Instead of using the traditional frequency and severity metrics weinstead predict the total claims that will be made by a new customer usinghistorical data of current and past policies. Given multiple features of thepolicy (age and gender of drivers, value of car, previous accidents, etc.) onecan potentially try to provide personalized insurance policies basedspecifically on these features as follows. We can compute the average claimsmade per year of all past and current policies with identical features and thentake an average over these claim rates. Unfortunately there may not besufficient samples to obtain a robust average. We can instead try to includepolicies that are "similar" to obtain sufficient samples for a robust average.We therefore face a trade-off between personalization (only using closelysimilar policies) and robustness (extending the domain far enough to capturesufficient samples). This is known as the Bias-Variance Trade-off. We modelthis problem and determine the optimal trade-off between the two (i.e. thebalance that provides the highest prediction accuracy) and apply it to theclaim rate prediction problem. We demonstrate our approach using real data.

A Data Science Approach to Risk Assessment for Automobile Insurance Policies