Description: Using the Cox proportional Hazards Model to improve underwriting debits and credits.

Description: An introduction to the methods and parameters for Bayesian Hyperparameter Optimization in a Life Insurance context.

Description: This article shows how to use a RBF Neural network to predict if a breast tumor is malignant. The network is trained using a genetic algorithm that uses C# multi-threading.

Description: This article is about how insurance companies are deploying insurance technologies.

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: In 2007, members of the Forecasting & Futurism Section were joined by members of the Technology Section and the Marketing & Distribution Sections to conduct a Delphi study about possible developments in the life insurance industry. It was named it “Blue Ocean Strategies in Technology for Business Acquisition by the Life Insurance Industry.” The goal was to have a panel of experts—actuaries and other financial professionals from across the country—identify and debate possible new approaches to acquiring business by life insurers. Panelists were asked to answer questions about possible “Blue Ocean Strategies.” This article highlights the ideas that resulted from those discussions as well as survey results on the same subject.