Description: Decentralized insurance alternatives is a rapidly expanding sector of Decentralized Finance (DeFi), an emerging and growing financial ecosystem powered by blockchain technologies. The purpose of this report is to review and discuss the business models and operating designs of decentralized insurance alternatives, which may have significant implications for traditional insurance businesses, as well as the actuarial profession.

Description: This report presents results from an online survey from January 2021 of Millennials, Gen Xers, Late Boomers, Early Boomers, and the younger portion of the Silent Generations. An additional oversample of Black/African American, Asian American, and Hispanic/Latino respondents was also collected.

Description: Chinese translation of Financial Perspectives on Aging and Retirement Across the Generations.

Description: Non-guaranteed elements are parameters of life insurance and annuity polices that are not fixed when the policy is issued but may be changed from time to time by the insurance company. They emerged in the United States and other countries as features of Universal Life and similar products designed to make insurance more competitive in an environment where fully guaranteed products did not have sufficient flexibility to respond to rapidly changing conditions. This overview covers professional guidance, regulations and practical issues with reviewing and revising non-guaranteed parameters.

Description: View the Data Challenges in Building a Facial Recognition Model and How to Mitigate Them

Description: This report captures expert insights from a March 2025 roundtable on how generative AI is shaping actuarial work and insurance industry practices.

Description: View the Income Optimization Objective Model.

Description: The SOA’s Artificial Intelligence Working Group explored the role of AI in actuarial practice, focusing on risk management, regulatory compliance, and ethical considerations. The discussion highlighted the importance of the NIST AI Risk Management Framework and Generative AI Profile in ensuring responsible AI development and deployment in insurance and healthcare.

Description: This article series highlights the growing impact of climate change on health, property, and financial institutions, emphasizing the need for actuaries and risk analysts to evaluate and adapt their practices to manage evolving climate-related risks.

Description: Consumer groups fear that the use of genetic testing information in insurance underwriting might lead to the creation of an underclass of individuals who cannot obtain insurance. Thus, these groups want to ban insurance companies from accessing genetic test results. Insurers contend that such a ban might lead to adverse selection that could threaten their financial solvency. To investigate the potential effect of adverse selection in a term life insurance market, a discrete-time, discrete-state, Markov chain is used to track the evolution of 12 closed cohorts of women, differentiated by family history of breast and ovarian cancer and age-at-issue of a 20-year annually renewable term life insurance policy. The insurance demand behavior of these women is tracked, incorporating elastic demand for insurance. During the 20-year period, women may get tested for BRCA 1/2 mutations. Each year, the insurer calculates profits/losses to date and expected future benefit payouts which determines the following year’s premium schedule. At the end of each policy year, women can change their life insurance benefit, influenced by their testing status and premium changes. Adverse selection could result from differentiated: 1. benefits following test results, 2. lapse rates according to test results, and 3. reactions to price increases. It is concluded that with realistic estimates of behavioral parameters, adverse selection should be a manageable problem for insurers. From Actuarial Research Clearing House 2004, Vol. 1.