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Journal Articles European Actuarial Journal Year : 2020

A Recommendation System For Car Insurance


We construct a recommendation system for car insurance, to allow agents to optimize up-selling performances, by selecting customers who are most likely to subscribe an additional cover. The originality of our recommendation system is to be suited for the insurance context. While traditional recommendation systems, designed for online platforms (e.g. e-commerce, videos), are constructed on huge datasets and aim to sug- gest the next best offer, insurance products have specific properties which imply that we must adopt a different approach. Our recommendation system combines the XGBoost algorithm and the Apriori algorithm to choose which customer should be recommended and which cover to rec- ommend, respectively. It has been tested in a pilot phase of around 150 recommendations, which shows that the approach outperforms standard results for similar up-selling campaigns. Recommendation system Up-selling Car insurance XGBoost algorithm Apriori algorithm
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Dates and versions

hal-02420954 , version 1 (20-12-2019)
hal-02420954 , version 2 (06-07-2020)



Laurent Lesage, Madalina Deaconu, Antoine Lejay, Jorge Augusto Meira, Geoffrey Nichil, et al.. A Recommendation System For Car Insurance. European Actuarial Journal, 2020, 10, pp.377-398. ⟨10.1007/s13385-020-00236-z⟩. ⟨hal-02420954v2⟩
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