Description: "This session will explore the critical challenges and solutions surrounding algorithmic fairness in the life insurance industry. The presenters will delve into the fundamental mechanisms of model bias, examining how predictive algorithms can yield outcomes that systematically discriminate against certain groups through flawed machine learning processes, erroneous assumptions, or biased data. Participants will gain insights into the various manifestations of bias in insurance models and understand their profound implications for both consumers and insurers. Real-world case studies and examples from the insurance industry will illustrate these concepts, while a discussion of recent regulatory developments will provide context for compliance requirements. This session is essential for actuaries, data scientists, and insurance professionals committed to developing and maintaining ethical, unbiased predictive models."

Description: The Individual Life Experience Committee continues to unlock valuable insights from the VM51 data as the National Association of Insurance Commissioners (NAIC) releases it. In this session, attendees will get a preview of individual life mortality patterns during COVID-19 for more than 100 companies in the NAIC data from 2020 to 2021, the first years of the pandemic. Attendees will also see early findings from the Committee's latest initiatives. These include integrating predictive analytics into its reporting products, assessing the impact of simplified and accelerated underwriting, exploring joint life mortality, evaluating company differences, and continuing its research into old-age patterns. Attendees will leave the session armed with additional knowledge for assessing best-estimate mortality for valuation.

Description: This session guides actuaries through the key steps of building a principles-based valuation model for a life product, starting with a brief overview of key requirements in LDTI and VM-20.

Description: Dive into the released enhancements of MIM-2021, including a run through of its purpose and evolution, during this webcast. The Mortality and Longevity Strategic Research Program plans to release these updates in October. You will leave this webcast with an understanding of how to create a mortality improvement scale using the necessary steps and tools. Learn how to use MIM-2021 through instruction and demonstration that will properly prepare you to utilize this tool. There will be time for questions where you can ask about the projection process or provide feedback.

Description: This session will examine bias from a social science perspective. It will discuss the three types of systemic biases using the National Institute of Standards and Technology (NIST) model. The NIST model details the three types as historical bias, societal bias, and institutionalbias. The session will also provide several examples of each type to provide a framework for identifying the social structure of bias.

Description: This paper applies the research in Prospect Theory of Kahneman and Tversky to explain why sales of annuities lag sales of life insurance products. The author theorizes that life insurance is easier to sell because customers perceive themselves to be in the domain of losses, while potential annuity customers perceive themselves to be in the domain of gains. Kahneman and Tversky showed us that people are mostly risk averse in the domain of gains and risk seeking in the domain of losses. Purchase of an annuity or life product is a risky investment. Customers will find it easier to take the risk in the life insurance domain of losses than the annuity domain of gains. The author then proposes a hybrid life annuity product that might address both mortality and longevity risk in one product that would allow both products to be more competitive than stand-alone products.

Description: The Society of Actuaries Research Institute, in collaboration with Reinsurance Group of America, Incorporated, a leading global life and health reinsurer, announced findings from new research on behavioral science for the life insurance sector. This research simulated online term life insurance purchasing to test behavioral science techniques.

Description: The Canadian Institute of Actuaries and Society of Actuaries Committee on Life Insurance Research, the Product Development Section and the Financial Reporting Section, sponsored this study investigating how the Canadian life insurance industry is utilizing predictive modelling.

Description: The paper describes a cost-benefit analysis to determine the value of electronic health records (EHRs) in life insurance accelerated underwriting programs. Sample cases from a carrier were underwritten pre-EHR and post-EHR and the mortality savings estimates from risk class changes using EHRs was compared to procurement costs. Results were developed to cover a range of scenarios. Factors to consider for carriers evaluating the cost-effectiveness of using EHRs in their own accelerated underwriting programs are also described.

Description: 1991-1995 individual life experience studies' table.