A high-order feature synthesis and selection algorithm applied to insurance risk modelling

In many jurisdictions, automobile insurers have access to risk-sharing pools to which they can transfer some risks. We consider different feature selection and modelling approaches to maximise profitability of these transfers through better risk selection. For that purpose, we introduce a flexible scoring model and devise a robust feature synthesis and selection method. We show what should be the most suitable sorting criterion depending on pool regulations. We use a technique, similar to cross validation, but that is coherent with the sequential structure of insurance data. We explain how software maturity level impacts profitability.