首页|APPLYING ECONOMIC MEASURES TO LAPSE RISK MANAGEMENT WITH MACHINE LEARNING APPROACHES

APPLYING ECONOMIC MEASURES TO LAPSE RISK MANAGEMENT WITH MACHINE LEARNING APPROACHES

扫码查看
原文链接
  • NETL
  • NSTL
  • Cambridge Univ Press
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.

Lapselife insurancemachine learningeconomic measure

Loisel, Stephane、Piette, Pierrick、Tsai, Cheng-Hsien Jason

展开 >

Univ Claude Bernard Lyon 1, Univ Lyon, Inst Sci Financiere & Assurances ISFA, Lab SAF EA2429, F-69366 Lyon, France

Univ Claude Bernard Lyon 1, Univ Lyon, Inst Sci Financiere & Assurances ISFA, Lab SAF EA2429, F-69366 Lyon, France|Seyna, 58 Rue Victoire, F-75009 Paris, France

Natl Chengchi Univ NCCU, Dept Risk Management & Insurance Risk, Coll Commerce, Taipei, Taiwan|Natl Chengchi Univ NCCU, Insurance Res Ctr, Coll Commerce, Taipei, Taiwan

2021

10.1017/asb.2021.10

Astin bulletin: The journal of the International Actuarial Association

Astin bulletin: The journal of the International Actuarial Association

SCI
ISSN：0515-0361
年,卷(期)：2021.51(3)
  • 3
  • 56