Description: This article discusses the need for more coordination and consistency in assumption setting between the pricing actuary, illustration actuary, appointed actuary, and financial reporting actuary.

Description: This paper describes an approach to the problem of long range planning that a group insurance writing company might use in attempting to determine the most desirable rate of growth. These techniques may also be used for testing premium and dividend scales. Discussions of this paper are included.

Description: Article Disparate Origins Of Life And Non-Life Insurances by Beard in The Actuary, February 1979, Volume 13, Number 2

Description: Using the recent West Africa Ebola outbreak as an example, the authors discuss several modeling approaches that can be useful for estimating pandemic risk.

Description: The next evolution of actuarial systems in the U.S. will likely be a move toward a single system solution. This article defines a single system solution and illustrates how models can be shared across actuarial functions, the potential benefits of a single system solution, and describes potential mitigation strategies to address typical actuarial concerns.

Description: The article describes the key components of actuarial model, leading practices in designing them, and characteristics of well-built models.

Description: Discussion of Living to 100 Session 6A: Projection and Statistical Modeling of Mortality at Late Age

Description: The comments of DxCG about their participation in the SOA study A Comparative Analysis of Claims-Based Tools for Health Risk Assessment.

Description: This paper uses a mortality surge model to estimate the total financial impact on the U.S. insurance industry of a flu pandemic. It does not attempt to determine the probability of such a pandemic.

Description: The article provides a high-level, non-technical, introduction to artificial neural networks (ANNs) and deep learning with an application to risk management. The method relies on typical ANN components such as nodes, activation functions, and optimization through gradient descent.