  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.
