Description: This report presents the results of individual life insurance lapse experience in the United States between observation years 2009 and 2013. Lapse experience is analyzed for whole life, term life, universal life, and variable universal life plans issued between 1918 and 2012. Results for most key policy and product factors are examined.

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: Milliman was engaged by the SOA to conduct two Accelerated Underwriting (AU) surveys, one of direct companies and another of reinsurers. The surveys explored actual results from 2019–2021 and expected results from 2022.

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

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: 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: A short article looking at the results from an annual assumption survey of VA writers with a focus on changes companies observed and planned to make due to COVID-19. In the article we discuss what companies should be considering as they approach the 2020 year end assumption setting and audit season.