Description: Report summarizing the results of the 2013 PBITT UL with Secondary Guarantees Survey.

Description: This article discusses the the 1998 alternartive XXX model regulations and the potential tax impact on the definition of life insurance and on reserve deductions associated therewith.

Description: Discusses PLR 201046008, regarding addition of a rider to a variable life policy that provides a minimum annual amount of withdrawal or policy loan. Includes discussion of impact on NSP under CVAT, and Guideline Premium.

Description: Read the Quo Vadis Reinsurance.

Description: The April 2016 Issue of The Modeling Platform

Description: Mandatory implementation of life principle-based reserves (PBR) is just around the corner and there is no shortage of work to do, as most products have yet to be moved to PBR. Oliver Wyman recently completed its 2019 PBR survey, with more than 40 participants covering 85 percent of the individual life market, including 23 of the top 25 life writers and five reinsurers. This article will expand on the key survey findings, including implementation trends, analysis to date, model robustness and common simplifications.

Description: Report summarizing the results of the 2018 PBITT UL with Secondary Guarantees Survey

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 the insurance industry, machine learning models are increasingly used to make decisions that affect people's lives. In order to ensure that models are fair, they should be audited for bias. There are no hard and fast rules for bias audit; metrics that can be used to evaluate fairness vary depending on the context in which the model is deployed. The presenters in this session present a flexible framework that highlights key considerations in bias evaluation that can be adjusted to suit your process.

Description: A discussion of Universal Life products with shadow accounts including a summary of some of the key communications and administration issues