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: This seminar is designed to give life and annuity practitioners an in-depth view of the important topics and fundamentals of reinsurance, from the perspective of a U.S. life insurance company. Reinsurance industry experts will present on the various types of reinsurance, contract issues, financial implications and other considerations. Attendees will leave this seminar with a stronger understanding of life and annuity reinsurance, and current topics. Keep in mind this seminar is designed for participants with a moderate level of experience. Educational Objectives • Provide attendees with a stronger understanding of life and annuity reinsurance, and current topics • Give attendees an in-depth view of important topics and fundamentals of reinsurance

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: 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: 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: Feature article describing a simple and ready solution that allows a hedging program to operate effectively without risk of mismatch due to mortality fluctuations.

Description: Proactive identification and active risk management of policyholder behavior risks within annuity products.

Description: Summary of upcoming seminar, scheduled for August 14-15, 2017.

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.