Description: This introduction to Captive Insurance Companies will provide an overview that includes the history of captives, different types of captives, common applications, how captives work, why employers form or join a captive, and a Medical Stop Loss Group Captive case study.

Description: As the insurance industry continues to evolve due to a pandemic and rapidly changing economic and regulatory conditions, the role of data scientists and actuaries has become increasingly important. In an interview with the Atidot data science and actuarial team, we discuss what it is like to work at an InsurTech, how data scientists and actuaries can collaborate, and look at some concrete technical examples of where the innovation is having an impact. This article explores what collaboration can look like and how the role of data science and actuary may evolve in the InsurTech industry in the future.

Description: The barrier between humans and machines continues to blur, but curiously, this may have been prediction thousands of years ago in ancient myths. This article looks to how some of the early legends are becoming manifest in modern technology, particularly in the area of elder care. From automation, to humanoid assistants, to mind clones will the age of technology bring a new utopia?

Description: Join us for an insightful webcast as we delve into the far-reaching implications of the recent Supreme Court decision to overturn Chevron deference. This landmark ruling, which previously granted federal agencies significant leeway in interpreting ambiguous statutes, is set to reshape the landscape of healthcare policy.

Description: Risk Adjustment continues to be a hot topic in Health Insurance since its emergence. This mechanism came into being as a means of reducing the risk of selection, particularly in marketplaces where underwriting is not allowed, and rating factors are significantly restricted. Risk adjustment serves to adjust premiums to reflect the risk of individual members or overall members as a whole within a particular cohort of a health plan. Risk adjustment is not perfect, as it does not perfectly predict future costs or perfectly adjust for the differences in expected costs. Different marketplaces have different challenges with risk adjustment.

Description: Demographic challenges due to longevity increases and low fertility rates will lower economic growth as measured by GDP. By deconstructing GDP growth between population and productivity growth the article describes the problem and presents potential solutions. The article builds off an SOA research paper published in November 2023, Demographics and Productivity: Drivers of Economic Growth.

Description: This article provides a summary of the research paper and Excel-based tool that can be found on the SOA’s website at Regulatory Capital Adequacy for Life Insurance Companies: A Comparison of Four Jurisdictions | SOA.

Description: The SOA Research Institute tasked the Workforce Science Institute to investigate how retirement benefit plans can affect a plan sponsor’s workforce and business results. The following is an interview with Rick Guzzo and Haig Nalbantian co-founders of the Workforce Science Institute who are the researchers on this project. The interview gives a helicopter view of the project as well as provides some “food for thought” on how retirement plans can be designed to produce an effective workforce.

Description: In this article, we summarize inflationary factors that may result in a sharp increase in interest rates and implications for life insurer convexity risk exposure, most notably to dynamic policyholder lapsation. We further provide insights into potential ALM strategies to mitigate this convexity risk. Due to the prolonged low-interest rate environment and low minimum interest rate guarantees offered to policyholders, liability convexity is higher than prior historical periods, which results in life insurers’ ALM positions being extremely vulnerable to short-term interest rate spikes. Convexity mismatches cause issues for life insurers under both rising and declining interest rate scenarios. This means that life insurers are not compensated for taking convexity risk and, therefore, convexity matching should always be considered in the asset optimization process. There are mixed signals from policymakers and financial markets regarding potential inflationary and sharply rising interest rate scenarios. The key takeaway is that life insurers are not compensated for taking convexity risk and it is the right time for insurers to review convexity exposure. There are different approaches available to mitigate convexity exposure through rebalancing of asset positions and hedges within each life insurer’s risk limits.