Description: The SOA, with SCOR as the researcher, completed an experience study of post-level term (PLT) lapse and mortality. In the first part of this session, the presenters will cover the key findings of the traditional analysis regarding the shock lapse, the PLT lapse and mortality experience. The remainder of the session will focus on the results of the predictive modeling, which will provide further insights into policyholder behavior in the post-level term period.

Description: The Individual Life Experience Committee has numerous research projects underway. Take a look at three current areas of research: the analysis of the interaction of lapse and mortality experience using data from mandatory submissions; a deep dive into old age mortality; and an update on the committee's work to establish a predictive analytics framework that will be open for all actuaries and data scientists to use. By attending the session, you will: - Better understand the research conducted by the Individual Life Experience Committee. - Better understand the link between lapse and mortality experience, appreciate some of the nuances of older age mortality, and gain knowledge about a predictive analytics framework that will be released in the future.

Description: As the speed of technological innovation increases, actuarial leaders are trying to determine how to use emerging technologies to unlock more business value and improve competitive positioning. This includes enabling new capabilities, improving process efficiency, and increasing the effectiveness of human capital. The session will discuss what we are hearing from clients who are in the process of transforming their business, how they are approaching these transformations, and what they are focusing on. This will include different aspects of transformation, including visualization dashboards, data science applications, no-code platforms, automation, data, and the cloud. Find out how organizations are thinking of human capital and collaboration between actuaries, IT, and other professionals.

Description: Regression to the mean is a phenomenon that arises when comparing sequential data points. It states that when an initial observation is extreme or an outlier, future observations will be closer to the average. Health care actuaries need to understand and be able to mitigate the effects of regression to the mean to avoid misapplying predictive models or misstating measurements of historical effects.

Description: An introductory article describing how Bayesian statistics has evolved over the past 20 years and why it offers the actuarial discipline powerful tools to better understand and manage risk. Compared with traditional (frequentist) and black-box (machine learning) approaches, Bayesian analysis provides explainable models focused on distributions of estimates instad of simply point estimates.

Description: We're introducing a new “cutting edge” wealth accumulation predictive framework, the predictive equity analytics (PEA) for life products (annuity, insurance, group employer benefits, retirement plan). PEA enables life insurers and policyholders to avoid market downturns and capture returns from equities (stocks, ETFs and mutual funds) predicted to increase in next one to three trade days via fund prediction advisory by insurers, additionally by individual policyholders. The capability does not exist in insurance markets nor in financial intermediary markets.

Description: As data continues to become more robust and readily available, there has been a push in the actuarial field to transition away from Excel to SQL and Python native cloud-based platforms. While these platforms are widely used and powerful, they present their own challenges for the actuarial industry, requiring processes to be production-ready from the onset, technical expertise for integration, and robust requirements documentation. Low-code tools provide a unique alternative solution, which works well when the organizations operating model allows for actuarial and IT to work together, giving actuaries the flexibility they need while maintaining a level of control followed by the organization. Make use of demonstrations to showcase low-code tools (such as Alteryx and Dataiku) and their use case in the actuarial space. Learn how these low barrier-to-entry tools can be leveraged to create robust and controlled processes. Discuss how low-code tools can integrate with coding languages such as Python as well as efficiently prepare data for downstream visualization platforms such as Tableau and PowerBI.

Description: This year’s Life Meeting was unable to take place in person as planned in New York City. We are looking forward to the Life Meeting returning to New York City in 2024!

Description: Gather with members and non-members in the China actuarial community to discuss recent topics and related issues in areas such as macroeconomic sharing, pension, investment, product strategy, and more.

Description: Second article in a series on how to use predictive analytics to improve the development of long-term care projection assumptions.