Description: The insurance industry has a lot to learn from academia's missteps. With predictive analytics assuming an ever-expanding role in risk modeling, carriers would be well-served to establish practices that mitigate the creation of faulty models. Hear practical advice on how to mitigate the risks of overfitting, with a focus on how to translate complex policyholder behavior experience data for long-term products into behavioral models using company- and industry-level data.

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: How can actuaries innovate solutions to the challenges posed by policyholder behavior risks?

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.

Description: While our ability to access and analyze large quantities of data is better than ever, many long-term products suffer from sparse data in critical risk areas, such as deep in-the-money lapse experience and long-term utilization rates for income guarantees. The presenters will explore industry-level annuity experience data for these types of situations, and discuss quantitative approaches to addressing them in pricing and other applications.