Description: This session meets the needs of life insurance actuaries who are focused on experience studies, assumption setting, and/or product development. The presenters will use industry data to illustrate how policyholders react to product design, such as premium jumps in the PLT period and UL with high interest rate illustration, and trends in the life insurance market, including the introduction of smoking distinguished rates and the shift towards an increasing number of preferred underwriting classes.

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: What is a "super" model? How do we develop, maintain, and visualize them? What is the difference between models and assumptions? Most importantly, how can we use all of this to quantifiably improve risk management?

Description: The Triple Aim is a vision to improve the health of populations, create a better quality of life, and reduce per capita spending for the benefit of communities.(http://www.ihi.org/Topics/TripleAim/Pages/default.aspx) While much public attention on wellness has focused on topics such as obesity and smoking, it would be beneficial if greater emphasis was placed on raising the bar of oral health. The mouth plays an important role in overall health and wellness. Routine oral care helps prevent dental problems and appears to positively affect chronic conditions before they develop into complex, more expensive ones. This purpose of this session is to increase awareness of the importance of oral health, the consequences of poor oral health, and the link with socioeconomic characteristics. This session will explore and highlight studies completed in this space by both researchers and payers. Actuaries interested in learning more about the impact of oral health and overall health, relevant interventions that have been undertaken, areas for potential financial savings, and issues relating to public policy applicable to oral health would benefit from attending this session.

Description: The presenters will explore policyholder experience data for long-term products, risks of over-fitting data in developing behavioral models, and ways to mitigate these risks and develop a sustainable and coherent framework to translate complex policyholder behavior experience data to behavioral models using company- and industry-level data. At the conclusion of the session, attendees will be able to: - Identify key drivers of policyholder behavior for long-term product - Understand the risks of over-fitting data and a false sense of precision in developing behavioral mode - Understand techniques that can be used to mitigate these risk

Description: Apart from their operating environment and the core risks they help others mitigate, organizations are largely impacted by behaviors--particularly those of their policyholders. This session builds on the first session in this series by considering how medical advances might influence policyholders, while also considering how agents/brokers, product design, and the economy can also be influencers. Weigh whether and to what extent behavioral uncertainties have been adequately integrated into the firms' assessments of business performance, both past and future. The presenters will also highlight activities being undertaken by firms to influence behaviors, in part building on the second session in this series by considering these activities through a regulatory lens. This session is for anyone interested in enhancing their understanding of uncertainty due to behavioral factors.

Description: As more term insurance policies move to the post-level period, actuaries are evaluating policyholder behavior and expected mortality. Enough experience has now emerged to provide a spectrum of outcomes that can model the relationship between premium increases, persistency and mortality. The presenters will share findings and consider how different post-level premium structures produce different results.

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.

Description: The Excel spreadsheet contains the supporting source lapse rates for figures within the U.S. Individual Life Insurance Persistency report. The data for this report, and the Excel spreadsheet, represent experience years 2009-13.

Description: Deep learning neural network applied to experience analysis for post-level term lapse and mortality.