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

Description: What is a "super" model? How do we develop, maintain, and visualize them? What is the difference between models and assumptions? Most importantly, how can we use all of this to quantifiably improve risk management?

Description: Machine learning models are not black-boxes; these models can easily be interpreted using tools like SHAP, LIME and Anchor. These tools can facilitate the understanding of the model and the business problem. We can used these tools to build better models and communicate how predictions are generated. This article demonstrates their use in a mortality model.

Description: This article will be focused on maximizing the reuse of code using features available within Moody’s Axis specifically targeted at using Formula Link.

Description: This paper outlines how an actuarial student studied to comprehend the stationary population. It outlines several articles her liked particularly well.

Description: In estimating mortality rates from the experience of a life insurance company or employee-benefit plan, it is expedient, because of the movement into and out of the covered group on dates other than birthdays or policy anniversaries, to make some assumption about mortality over a fractional part of a year of age. This paper describes and develops the concept of 'exposure' with regard to the more commonly known methods of estimating the rate of mortality, and proposes a fourth methodology. From the Actuarial Research Clearing House 1978, Vol.2.

Description: This article provides an overview of the commonly used structural credit risk modeling approach that is less familiar to the actuarial community.

Description: Book Review from The Actuary, September 1989 – Volume 23, No. 8. Actuarial Mathematics by N. Bowers. H. Gerber. J. Hickman, D. Jones and C. Nesbitt. Published by the Society of Actuaries, 1986.

Description: This paper derives limited fluctuations credibility results for the Generalized Linear Model GLM and the extended case of generalized linear mixed model GLMM. It shows how credibility depends on the sample size, the distribution of covariates, and the link function. This provides a mechanism to obtain confidence intervals for the GLM and GLMM estimators.