Abstract
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 suggest 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 recommend, 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.
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Foyer Assurances is leader of individual and professional insurance in Luxembourg.
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Acknowledgements
We developed this work in a strong collaboration with Foyer Assurances, Luxembourg, which provided the domain specific knowledge and use cases. We would like to thank the anonymous reviewers for the helpful remarks and advice that helped us improve the paper. We also gratefully acknowledge the funding received towards our project (number 13659700) from the Luxembourgish National Research Fund (FNR).
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Lesage, L., Deaconu, M., Lejay, A. et al. A recommendation system for car insurance. Eur. Actuar. J. 10, 377–398 (2020). https://doi.org/10.1007/s13385-020-00236-z
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DOI: https://doi.org/10.1007/s13385-020-00236-z