Description: This article promotes the concept of long range planning for life insurance companies and notes that actuaries are well equiped for this activity.

Description: The editors preview the articles in the February 2010 issue of Health Watch.

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: A description of the random generalized linear model (RGLM) and how to use it.

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: Market consistent valuations require path-dependent discounting for scenarios. IFRS 17's requirement to discount best-estimate cashflows using locked-in rates could give non-sensical results in many instances. This article describes a technique for calibrating each of the scenario's cashflows so that they can be discounted with other discount factors or curves without distorting the value of underlying options and guarantees.

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: Article from The Actuary, March 1972 – volume 6 - Issue 3 provides a recap of the Conference on Capitalization of Risk Ventures, sponsored jointly by the Graduate School of Business of the University of Wisconsin and by the Society's Committee on Research. The conference was held October 21-23, 1971 in Madison, Wisconsin.

Description: In this paper, chapter III of the 1987 Valuation Actuary Handbook, the authors discuss building a liability model to evaluate the C-3 risk. The article discusses the data and assumptions needed, and the importance of sensitivity testing given that the model results are very sensitive to the assumptions used.