Description: Actuaries have the opportunity to transform to a new role that utilizes predictive analytics and new data sources such as epigenetics, to better humankind.

Description: This article discusses how to handle semisupervised data. This type of data might have a large number of observations, yet a small number of labels.

Description: Feature article discussing the ways artificial intelligence will influence the actuarial field.

Description: Chairperson's column for the May 2019 issue of Predictive Analytics & Futurism.

Description: An application of GLM trees to mortality modeling using the Individual Life Experience Committee 2009-2015 dataset. GLM trees are a hybrid of GLMs and decision trees which are analogous to the way actuaries have analyzed data. The major benefits include speeding up actuarial analytic workflows, improving data understanding, and improving data visualization.

Description: Actuaries can empower their understanding of policyholder behavior with third party data. Modeling policyholder behavior should start from developing knowledge of who they are, instead of actuaries assuming what a rational behavior should be.

Description: The Delphi Method is discussed in detail. The method attempts to filter out the consensus bias that can result from societal forces by employing a technique whereby an anonymous collection of opinions is used, supplemented by feedback loops that can be used to influence outcomes.

Description: This is an article that discusses Nassim Nicholas Taleb’s work and its significance for actuaries.

Description: This article provides a brief introduction to forecasting methods that are potentially relevant to actuarial work.

Description: Introduction of the new Forecasting and Futurism Section – helps members stay current, and facilitates professional development by providing information about and training in forecasting and futurism methods and tools.