Modeling policyholders’ lapse behaviors is important to a life insurer, since lapses affect pricing, reserving, profitability, liquidity, risk management, and the solvency of the insurer. In this paper, we apply two machine learning methods to lapse modeling. Then, we evaluate the performance of these two methods along with two popular statistical methods by means of statistical accuracy and profitability measure. Moreover, we adopt an innovative point of view on the lapse prediction problem that comes from churn management. We transform the classification problem into a regression question and then perform optimization, which is new to lapse risk management. We apply the aforementioned four methods to a large real-world insurance dataset. The results show that Extreme Gradient Boosting (XGBoost) and support vector machine outperform logistic regression (LR) and classification and regression tree with respect to statistic accuracy, while LR performs as well as XGBoost in terms of retention gains. This highlights the importance of a proper validation metric when comparing different methods. The optimization after the transformation brings out significant and consistent increases in economic gains. Therefore, the insurer should conduct optimization on its economic objective to achieve optimal lapse management.

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通过机器学习方法将经济措施应用于失效风险管理

对保单持有人的失效行为建模对人寿保险公司来说非常重要，因为失效会影响定价、准备金、盈利能力、流动性、风险管理和保险公司的偿付能力。在本文中，我们将两种机器学习方法应用于失效建模。然后，我们通过统计准确性和盈利能力衡量这两种方法以及两种流行的统计方法的性能。此外，我们对来自客户流失管理的失效预测问题采用了创新的观点。我们将分类问题转换为回归问题，然后执行优化，这对于失效风险管理来说是新的。我们将上述四种方法应用于大型现实世界保险数据集。结果表明，极端梯度提升 (XGBoost) 和支持向量机在统计准确性方面优于逻辑回归 (LR) 和分类回归树，而 LR 在保留增益方面的表现与 XGBoost 一样好。这突出了在比较不同方法时正确验证指标的重要性。转型后的优化带来经济收益的显着和持续增长。因此，保险公司应对其经济目标进行优化，以实现最佳的失误管理。转型后的优化带来经济收益的显着和持续增长。因此，保险公司应对其经济目标进行优化，以实现最佳的失误管理。转型后的优化带来经济收益的显着和持续增长。因此，保险公司应对其经济目标进行优化，以实现最佳的失误管理。