Description: Why Actuaries Should Welcome Automation

Description: Reviewing a text covering multiple statistical modeling topics: e.g., generalized linear models, decision trees, clustering, using the statistical language R.

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: Feature article describing how exponential technologies will affect the actuarial profession.

Description: This article explores the use of Generalized Additive Models and Multivariate Adaptive Regression Splines to model nonlinear relationships in a linear response model. These models use supervised methods to determine the optimal shape of the predictors in the model. Users are provided an example use case so that they may get started right away building models to explore complex relationships in new ways.