Description: An overview of guaranteed lifetime withdrawal benefits (GLWB) on registered index-linkedannuity (RILA) products and a stochastic analysis of market risks.

Description: Python has become the fastest-growing major programming language in the world. It has surged in popularity over the last decade, following the development of its industry-leading capabilities in data science, machine learning and artificial intelligence, and it is now one of the most globally popular and widely used languages. In this article, I discuss some of the reasons for its popularity and explain why many actuaries and insurers have started using Python.

Description: This article is the final installment of our three-part series on economic scenario generators (“ESGs”). Part I and II were published in the November 2019 and August 2020 issues of The Modeling Platform. Part III contextualizes the framework for selecting, building, and validating ESGs and focuses on the Academy Interest Rate Generator (“AIRG”), the most commonly used real-world ESG for US actuaries

Description: In the United States, there has been much industry discussion of the challenges associated with a sustained low interest rate environment, which impacts both the asset and liability sides of the balance sheet in unfavorable ways. The intent of this article is not to focus on these strategic issues. Rather, we attempt to shine a spotlight on some of the modeling challenges that practicing actuaries may experience when incorporating negative interest rates into their models.

Description: The article and guidebook of the same name use a stochastic simulation model to examine potential impacts of public pension plan risk-sharing policies on government employers and plan members. Policies examined include simple policies such as COLAs and employee contributions that are contingent upon investment returns or funded ratios, and more-complex policies similar to those used in the Wisconsin Retirement System.

Description: The direct and indirect effects of the pandemic have impacted many actuaries’ work since the spring of 2020. This article addresses three considerations actuaries should account for when using risk evaluation models which are focused on projecting remote, highly unlikely losses: model effectiveness, asset risk, and insurance (mortality and mortality) risk.

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: Discussion on modeling Medicare Supplement insurance and the considerations when choosing an Actuarial Modeling Software (AMS). Focuses on model risk management, key modeling assumptions and inputs, and the complex interactions between modeling assumptions and why its critical to have a model that can handle that complexity. This article also touches on the incorporation the of accounting and regulatory regimes into the model, including Statutory, GAAP (Legacy and LDTI), and Tax regimes, and the modeling considerations for sensitivity testing and capital planning. Lastly, the article highlights the capabilities to look for when choosing an AMS for Medicare Supplement