Description: Certain valuation assumptions are difficult to set with a high degree of accuracy, whether because the underlying experience is in different economic or interest rate environments, in addition to other reasons. Examples include assumptions for long-term care insurance, annuity options, lapsation and premium persistency on ULSG. This session will share how the actuary can use predictive analytics to augment the assumption-setting process. Some of the benefits to the actuary (along with the company and the regulator) include more accurate assumptions with lower margins for adverse deviations. Of possible interest as well is that applications go beyond valuation and include acquisitions, reinsurance and pricing.

Description: Starting with a review of recent VA and FIA industry policyholder behavior experience, this session will explore several critical elements in developing a sustainable and coherent framework to translate this complex experience data to assumption models, particularly for guarantee features such as GLWBs. The discussion will include the use of industry- and company-level data in building customized predictive models, and integrating models across behaviors such as income utilization and surrenders.

Description: An exploration of drivers, cohorts and dynamics for policyholder behavior for VA and FIA based on industry experience data, including changes in recent years and the emergence of long-term data in key areas. We will also discuss critical elements to developing a sustainable and coherent framework to translate this complex experience data into assumption models.