Description: Behavioral economics research has shown that small details can have a large impact on how a person behaves. As life insurance continues its evolution into the digital domain, life insurers now have an almost infinite number of ways to tweak their product offering, positioning, and delivery to the end consumer. Behavioral science is becoming increasingly more important to optimize these product design decisions and improve outcomes for both insurers and their customers. Discover a brief overview of key behavioral economic concepts and dive into two SOA research projects that are exploring how behavioral economic techniques can be used in different parts of the life insurance value chain. First, you'll start before a policy is even sold, by exploring how behavioral economics, through techniques such as the messenger effect, can be used to increase consumers' comprehension of policies and life insurance's value. Second, you'll look at how underwriting question design can impact applicant behavior and lead to higher quality application data. At the conclusion, you'll enjoy having a better understanding of how behavioral economic techniques can be applied in a life insurance context.

Description: Come and learn about one of the long-term care (LTC) industry's hottest trends: Wellness programs. Discover key insights from Oliver Wyman's 2022 and 2023 industry surveys on LTC wellness programs, focusing on the evolution and expansion of wellness programs over the last few years. In addition, listen in as a panel of industry representatives share their perspectives on challenges faced, lessons learned and next steps in their wellness journey. Gain impactful insights and takeaways from our panel of experts (with wide-ranging familiarity of LTC and wellness programs), who will pull from their first-hand knowledge.

Description: Join a discussion on the evolution of asset and liability projection processes in the life insurance industry. Familiarize yourself with the broad set of inputs, stakeholders, and financial planning and analysis use cases. Notable areas of focus will include, but are not limited to, historical background, modeling needs, interactions between stakeholders, industry pain points, and a composite of an ideal future state informed by perspectives gleaned from leading companies within the industry.

Description: The life insurance industry has a lot going on. Come discover some key highlights of the life insurance market mixing quick hits with some in-depth conversations about the headlines and hard lines happening in the life insurance sector. Topics include underwriting, regulation, product changes and distribution to name a few. If you enjoy the PTI show for sports, you will enjoy hearing about the life insurance industry in this format and hosts will run down the headlines of the insurance industry in PTI fashion.

Description: This session will bring together the results of SOA work focusing on what employees prefer in retirement. The work includes a survey on the provisions that employees prefer in retirement plans, the results of a series of focus groups conducted with individuals prior to retirement and at various stages of during retirement, and highlights of 20 years of SOA sponsored surveys on retirement risk. The panel will discuss major findings and their implication for retirement resource program structures, design and planning.

Description: Market experts with backgrounds in economics, investments, and insurance will speak to macro trends and their impacts in the market. Panelists will explore topics such as “what are markets saying about the likelihood and severity of a recession?” and “how are interest rates impacting investment decisions and ALM?" You'll have the opportunity to join the discussion by asking questions throughout the presentation.

Description: As insurers search for ways to optimize the value of their actuaries, cloud-based data architecture has emerged as a leading solution to deliver quality data that enables the unlocking of actuarial potential. Hear an introduction to cloud based data architecture, explore cloud-based data strategies and view a live demo. Learn about different data “zones”, piping data between the zones, transforming data, and a collection of cloud-based tools that could be used for each one of these steps.

Description: Arun Paul, FSA MAAA Pricing Actuary for Canada Life Re-Insurance discusses morbidity as it relates to Long-Term Care Insurance. Arun is the current President of SANA The South Asian Network of Actuaries and has over a decade worth of actuarial experience. He provides insights into how morbidity is defined in Long-Term Care insurance and explains the process of filing and processing claims in this engaging discussion with Jon Forster, ASA MAAA Professional Development Learning and Content Design Director at the Society of Actuaries. They also touch upon key factors affecting Long-Term Care insurance costs, such as care duration, benefits administration, and the impact of geographic location and prior claim history on claim costs. Contributors: ARun Paul FSA MAAA, Julite Spector, FSA, MAAA Matt Morton FSA MAAA, RAchael Marsiglio FSA MAAA , Jon Forster ASA MAAA

Description: "It's your first full day of productive learning! Start it off right with a delicious breakfast and warm welcome. Hear keynote speaker Dr. Ali Mokdad Professor of Health Metrics Sciences at the Institute for Health Metrics and Evaluation (IHME) and Chief Strategy Officer of Population Health at the University of Washington speak about the Global Burden of Disease research and post-pandemic healthcare and mortality impacts. Please note, our previously announced speaker Dr. Anupam Jena had canceled due to unforeseen circumstances.Breakfast is available for all meeting attendees as part of the registration fee. If you plan to attend, please register in advance."

Description: Review a recently completed research project for a multi-stakeholder data consortium on social determinants of health (SDOH). In this project, claims, external SDOH data, geospatial analysis, and machine learning models were used to examine several questions: How to identify SDOH vulnerabilities? How sensitive are the findings with respect to provider coding pattern variations? How much variability in cost can be explained by SDOH vs. morbidity? What are the key risk drivers revealed by machine learning models? The session will review the methodology and main findings from this project.