Description: 2021 Life Meeting - Session 2B: Ethical Implications of Big Data, Predictive Models and Artificial Intelligence 8.30.21 Recording

Description: Discover the impact of COVID-19 on member costs post diagnosis and the future of mortality in this Emerging Long Term COVID-19 Trends webcast. This webcast will be split up into Health and Life portions. During the Health portion of this webcasts, you will learn more about the impact on risk scores due to recent findings. Enjoy this discussion and further dive into the emerging COVID-19 trends that may affect how you conduct business practices. At the Life portion, the SOA Research Institute assembled a panel of mortality experts who will express opinions on the effects of the U.S. mortality rate for general, insured and pension populations in the short-term.

Description: ALM and hedging can reap significant benefits from innovation in computation and operations research. Explore applications of static and dynamic optimization in two different ALM and hedging use cases. Insurers are increasingly moving offshore by establishing Bermuda captive reinsurers. In calculating the Bermuda Best Estimate Liability ('BEL'), reinsurers will need to segment assets to support liabilities in each treaty. A Linear Programming ('LP') problem can be formulated to tactically select assets in a way that minimizes the BEL and consequently the total asset requirement. Learn more about what US Variable Annuity Insurers face with the regulatory requirement to reflect 'future hedging strategy' in their statutory funding calculation. The traditional numerical method of finding the optimal hedging target and then allocating assets may encounter the curse of dimensionality with expanding product and economic environment complexity. A Reinforcement Learning algorithm may well fit this problem and provide insurers a solution for dynamic hedging.

Description: Interview with John Houston, Advanced Analytics and Predictive Modeling practice leader at Deloitte.

Description: Possible usage of tools that can automate time consuming processes, and lead to a different team structure and organization for predictive analytics team.

Description: Read the November 2020 issue of Predictive Analytics and Futurism published by the Predictive Analytics and Futurism Section. Newsletters provide practical information for the working actuary, articles on trends that impact sections and news about section activities.

Description: Read the August 2020 issue of Predictive Analytics and Futurism published by the Predictive Analytics and Futurism Section. Newsletters provide practical information for the working actuary, articles on trends that impact sections and news about section activities.

Description: The need for LTC services is projected to increase as the general population ages. Many LTC carriers have helped meet this demand but are looking for new ways to mitigate underpriced risks and to improve policyholders' quality of life. Aging-in-place interventions can be an effective method to mitigate financial risks and improve health outcomes. However, many carriers face challenges to prove financial benefits/ROI, quantify program effectiveness, or simply don't have clarity on how to start and implement these initiatives effectively or operationally.

Description: There has been a shift in finance and actuarial modernization in the insurance industry over the past 2 to 3 years. Discuss the latest trends in the marketplace and the key changes we see actuaries and actuarial employers needing to make in the future of work. Learn how actuarial work is evolving and about the changing manner in which work is performed. Brainstorm specific ways actuaries can thrive in the future, including performing more strategic work.

Description: Highlight the importance of assigning granular policyholder behavior assumptions to cohorts of a block that behave differently for universal life products. The case study assumption testing will focus on potential cash flow impacts of surrender, lapse and premium persistency behavior— individually and combined. The case study will be based on testing data related to typical demographics for a ULSG product and not on any specific company data.