Description: This article from A Year in the Life of the Strategic CRO series explains how to enhance the risk appetite to better support business planning discussions.

Description: This short note discusses creativity in the context of actuarial science, and how to cultivate it. We first attempt to define creative thinking as opposed to procedural thinking or memory recall. We then describe concrete examples of actuarial projects that promote deeper thinking. We also address why creativity is needed and how to make sure our students are able to communicate their creative ideas effectively.

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 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: In PLR 202109001, the IRS ruled that a proposed assumption reinsurance transaction between related foreign parties changing the counter party obligated to a domestic corporation (“Taxpayer”) under an existing agreement did not give rise to Taxpayer being treated as making a base erosion payment (“BEP”), as Taxpayer did not receive a deduction under §832(b)(4)(A) from the amount of gross premiums written on insurance contracts during the taxable year for premiums paid for reinsurance.

Description: TAXING TIMES recently sat down with a group of retired tax actuaries to hear about how they got involved in actuarial tax matters and how their tax experience impacted their careers. Their insights will be useful for actuaries who are just starting out in the profession, as well as more seasoned actuaries.

Description: This SOA e-course shows you how to use financial modeling to assess or mitigate risk. It covers derivatives pricing, range of outcomes and more.

Description: Supplemental Health, DI & LTC Conference

Description: The 2023 Professionalism in Practice Course will study, discuss and explore the professionalism practice, code of conduct and application of the actuarial profession. Our speakers are seasoned presenters from mainland China. Experts will cover several issues encountered in daily actuarial working environments. Content will include business cases and interactive discussions, which aim to enrich the practical learning experience of attendees. Audience attendance will be limited in order to provide an interactive, collaborative learning experience. Early registration is highly recommended to secure a seat.

Description: This chapter focuses on creating practical models that aid in the prediction of the risk of readmission to a specific hospital within 30 days of discharge for Medicare patients, to aid in the identification and stratification of at-risk patients for hospitals considering building an intervention program in a cost-effective manner. For readers who wish to develop their own predictive model, a dataset is provided on the Actexmadriver.com website, and R code is provided in the appendix to this chapter.