基于机器学习方法的失效风险管理中的经济度量
Applying economic measures to lapse risk management with machine learning approaches
摘要：
保单持有人失效行为的建模对寿险公司来说是非常重要的，因为失效会影响定价、准备金、盈利能力、流动性、风险管理以及保险公司的偿付能力。根据欧洲保险和职业养老金管理局的量化影响研究QIS5，失效风险确实是最重要的人寿保险风险。在本文中，我们介绍了两个先进的机器学习算法的延时建模。然后，我们评估不同的算法的性能，通过经典的统计精度和盈利能力的措施。此外，我们采取了创新的角度对失效预测问题，来自流失管理。我们将分类问题转化为回归问题，然后进行优化，这是失效风险管理的新方法。我们将不同的算法应用于一个大型的真实世界的保险数据集。我们的研究结果表明，XGBoost和SVM优于CART和逻辑斯谛回归，特别是在经济验证度量方面。改造后的优化带来了经济效益的显着和一致的增加。
英文摘要：
Modeling policyholders lapse behaviors is important to a life insurer since lapses affect pricing, reserving, profitability, liquidity, risk management, as well as the solvency of the insurer. Lapse risk is indeed the most significant life underwriting risk according to European Insurance and Occupational Pensions Authority's Quantitative Impact Study QIS5. In this paper, we introduce two advanced machine learning algorithms for lapse modeling. Then we evaluate the performance of different algorithms by means of classical 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 for lapse risk management. We apply different algorithms to a large real-world insurance dataset. Our results show that XGBoost and SVM outperform CART and logistic regression, especially in terms of the economic validation metric. The optimization after transformation brings out significant and consistent increases in economic gains.
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