Description: An overview of the basic tooling and packages available for the Julia programming language, with a focus on why this is of interest to the actuarial profession. It covers some of the built-in language features as well parts of the Julia package ecosystem that make Julia ideal for modeling and data analysis. This is the second installment in a series of article aimed at actuaries. The first installment, Julia for Actuaries, was published in the October 2020 edition of Actuarial Technology Today, and covered the base language itself, its high-level, math-friendly syntax, and why the actuary of the future should consider its adoption.

Description: This is a summary of a multi-faceted research project focusing on how to help people better manage money when they no longer can. Article includes interviews with the three co-researchers. Research is focused on understanding when people need help and what actions will encourage them to make proper arrangements. Research includes focus on words and actions that lead to implementation of plans. The team is multi-disciplinary and the research included interviews with experts and individuals, focus groups, and a survey. The research was sponsored by the Society of Actuaries Aging and Retirement strategic research initiative.

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: Discover the key components of value-based care (VBC) contracts and their impact on provider credibility. Learn about benchmarks, measurement period results, and the unique challenges providers face in managing insurance risk. Gain insights into reducing costs and aligning financial incentives in the US healthcare market.

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

Description: The traditional defined-benefit (DB) plan is a type of plan structured to provide the greatest degree of retirement income security, designed to pay out a guaranteed retirement income for the life of the retiree (and, when applicable, a beneficiary). However, over the last several decades, the number of private-sector employees covered under these traditional plans has decreased dramatically and been replaced by defined contribution (DC) plans. In contrast, public-sector retirement plans, which are exempt from ERISA, have seen a much smaller decline in DB plans. There are many differences in the private and public sector that likely contributed to this phenomenon, but ERISA and the many amendments to it has been a major factor.

Description: S&P Global released its initial version of proposed changes regarding its Insurer Risk-Based Capital Adequacy methodology and assumptions on Dec. 6th, 2021. Unlike prior guidance notes or criteria releases, the proposed changes comprehensively updated the current S&P rating criteria released in 2010. These updates would directly impact many insurers. S&P Global anticipates that 10 percent of existing ratings in the insurance sector would be impacted and the change would lead to more positive rating implications than negative impact. This article focuses on the proposed changes on the S&P risk-based capital (RBC) vs. the current model, then simulate impacts to a representative U.S. life insurer through an illustrative case study.

Description: Actuaries are leveraging predictive modeling and machine learning more and more as they strive to provide greater value to their stakeholders. Actuarial standards of practice provide guidance around modeling and data. The world has changed significantly over the last few years, so it is important these models be tested for fairness as well as any statistical biases. This article addresses some of the key issues actuaries need to be aware of as Corporations and society work through the responsible us of this technology and underlying data.

Description: The article introduces a cashflow matching strategic asset allocation (SAA) framework for life insurers. Given the recent shift to higher interest rates after two decades of persistent low interest rates, insurers have an opportunity to enhance their asset liability management by rebalancing asset portfolios to narrow duration gaps and attain higher risk-adjusted yields. The framework aims to align investment objectives with economic, regulatory, and rating agency requirements. It prioritizes cashflow matching as the primary optimization goal and incorporates security-level asset information to assist portfolio managers in executing SAA seamlessly. The article highlights the benefits of the cashflow matching SAA framework, including greater attention to cashflow matching, unbiased results, readily executable strategies, and stable economic reserves. A case study is included to demonstrate the effectiveness of the framework in improving risk-adjusted return and cashflow matching for a representative liability block.

Description: Overview about the information covered in the Predictive Analytics Certificate and how that is useful for actuaries.