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.

A large part of the production ofLaminaria hyperboreakelp forests is not directly consumed by grazers, but is exported during storm events or natural annual blade erosion. Drifting kelp fragments are transported and can accumulate temporarily over subtidal benthic habitats. The decay process is particularly slow (>6 mo for complete decay during spring-summer) andL. hyperboreafragments are able to maintain their primary production function for several months. If they accumulate in low subtidal habitats, fragments can have a long residence time, thus modifying habitat structure. Based on a 6 mo cage experiment, we investigated macrofaunal colonization and community succession within accumulations ofL. hyperboreafragments on a low subtidal (-10 m) sandy bottom ecosystem. Stable isotope (δ¹³C and δ¹⁵N) measurements were carried out to describe the structure and development of the trophic food web and the role of detritus as a food source. Kelp tissues were rapidly and abundantly colonized by macrofauna, and a classical ecological succession occurred, with changes in species dominance and increase in diversity during decay. The food web was based on 2 main sources: particulate organic matter from the water column and decaying kelp tissues. Kelp contributed significantly to the diet of numerous species that are commonly consumed by local predators (fish, shrimp). Following community succession, diets diversified and the food web became more complex during the decay process. Our results indicate that drift kelp accumulations structure their associated communities and food web during the whole decay process.