Description: A paper demonstrating how to obviate the need for market timing strategies in variable annuity products by judicious use of product design. The paper presents the premise behind the product design, provides numerical examples, then explores possible extensions of the concept to produce more sophisticated variable annuity plans based on guarantees of investment outcomes.

Description: All things considered, caution needs to be applied to the use of so-called “standard” mortality tables. Sharpening up these blunt estimates by using individual assumptions reveals material differences in the structure of longevity, both between plans and within plans.

Description: Provides an update, as of September 2022, on Financial Reporting Section sponsored research projects in process and those recently completed.

Description: In this article, a numerical comparison of two commonly used FIA valuation methodologies is conducted to help insurers decide on the best methodology to use under GAAP LDTI and VM-22. We compared two modeling approaches to project the future index credits with the annual point-to-point crediting formula: Tthe stochastic equity crediting model and the deterministic option budget crediting model. The analysis is then extended to different levels of option budgets and the monthly average crediting formula. The advantages and disadvantages of the two equity return projecting approaches are also highlighted and will be discussed later in the report.

Description: Milliman was engaged by the SOA to conduct two Accelerated Underwriting (AU) surveys, one of direct companies and another of reinsurers. The surveys explored actual results from 2019–2021 and expected results from 2022.

Description: The article discusses the continued popularity of indexed UL insurance and possible reasons why a company's actual indexed UL earnings may be below their original pricing expectations for the product. The article discusses several key items that may be the source of that underperformance (e.g., hedge ineffectiveness, policyholder behavior) and the need to monitor earnings and perform analyses to better understand those potential sources.

Description: An overview of bias and fairness in machine learning models, as they apply to use cases in life insurance.

Description: A look at the near future and far future of various elements of Actuarial Models as defined through ASOP 51. What is driving the complexity for inputs, calculations, and outputs. Howe will actuaries handle these challenge?

Description: This article provides an overview of activities that should be considered by an actuary when validating a predictive model. It describes several components to consider when validating a predictive model and provides an examination of various methodologies that can be implemented in the validation process.

Description: Technology has streamlined countless processes in our daily lives, and insurance is no stranger to this transformation. Technology can make insurance simpler and more personal for consumers, and can enable actuaries to better analyze unstructured data, make good decisions, create new products, and find channels to enhance profitability, provided they can tame the flood of information pouring in from across the digital ecosystem.