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.

我们构建了汽车保险的推荐系统，通过选择最有可能订阅额外保险的客户，使代理商可以优化销售业绩。我们的推荐系统的独创性应适合保险环境。尽管为在线平台（例如，电子商务，视频）设计的传统推荐系统是建立在庞大的数据集上并旨在建议次优报价的，但是保险产品具有特定的属性，这意味着我们必须采用不同的方法。我们的推荐系统结合了XGBoost算法和Apriori算法，分别选择应该推荐的客户和推荐的客户。它已经在大约150条建议的试验阶段进行了测试，