Description: This paper discusses some powerful uses of recursive functions, including: 1. Universal life, both for the calculation of target premiums and reserves. 2. Paid-up Rider. Recursive functions can directly determine a premium that precisely matures the rider. 3. Traditional Whole Life. Reserves can be efficiently calculated when needed instead of referring to a table of stored factors. From Actuarial Research Clearing House 1993, VOL. 3.

Description: This article reviews the topics discussed at the December 2004 meeting of the Life and Health Actuarial task force. The covered topics include Actuarial Guideline 38 and Reserves for Variable Annuities C-3 Phase 2 reserves.

Description: Reworking The ABC’s of XYZ by Tom Kalmbach from Small Talk Newsletter, November 2002, Issue No. 19.

Description: Report summarizing the results of the 2009 Policyholder Behavior in the Tail working group with respect to UL policies with Secondary Guarantees Survey.

Description: This article addresses the expected impact that the 2001 CSO table will have on pricing and product structure for UL plans.

Description: The Actuary Vol. 22 , No. 7. This articles explores the dangers posed by many guaranteed-return life insurance and annuity products.

Description: The purpose of this paper is not to present a revolutionary new concept, instead, it is to reexamine a familiar concept in the light of new market circumstances and, to consider the consequences to the life insurance industry if the concept were adopted.

Description: Read the Quo Vadis Reinsurance.

Description: 1988 Valuation Actuary Symposium. The speakers in this session addressed the relative volatility of participating whole life versus universal life and the annual-premium versus single- premium basis.

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.