A retention strategy based on an enlightened lapse model is a powerful profitability lever for a life insurer. Some machine learning models are excellent at predicting lapse, but from the insurer’s perspective, predicting which policyholder is likely to lapse is not enough to design a retention strategy. In our paper, we define a lapse management framework with an appropriate validation metric based on Customer Lifetime Value and profitability. We include the risk of death in the study through competing risks considerations in parametric and tree-based models and show that further individualization of the existing approaches leads to increased performance. We show that survival tree-based models outperform parametric approaches and that the actuarial literature can significantly benefit from them. Then, we compare, on real data, how this framework leads to increased predicted gains for a life insurer and discuss the benefits of our model in terms of commercial and strategic decision-making.

基于开明的失效模型的保留策略是人寿保险公司强大的盈利杠杆。一些机器学习模型在预测失效方面表现出色，但从保险公司的角度来看，预测哪个保单持有人可能失效不足以设计保留策略。在我们的论文中，我们定义了一个失误管理框架，其中包含基于客户终身价值和盈利能力的适当验证指标。我们通过参数化和基于树的模型中的竞争风险考虑因素将死亡风险纳入研究中，并表明现有方法的进一步个性化可以提高性能。我们表明，基于生存树的模型优于参数方法，并且精算文献可以从中受益匪浅。然后，我们根据实际数据进行比较，