Description: Discussion of the merits of solvency versus going concern funding targets for pension plans, and the connection with different risk-sharing deals.

Description: This article provides insights from the 2017 PSCA annual meeting and the Pension Research Council symposium on current pension system issues and solutions.

Description: It cam oftentimes be difficult to determine the easiest way for an SOA section member to volunteer. With our section's new focus, we will need a lot of help. This article talks through some of the more accessible places that section members can get involved.

Description: The article gives a brief history of the development of the Delphi method, and describes the use of the Delphi method in studies by the Society of Actuaries over the past 25 years.

Description: Even the best algorithms are only effective if they are trained on appropriate data. This article discusses common pitfalls in creating a training data set.

Description: This article describes how to model variable annuities using a GPU along with some of the stumbling blocks.

Description: Chairperson Address to the Predictive Analytics and Futurism Section

Description: The article is about how current advancements in AI can both help and potentially replace actuaries. We focus on three types of AI as defined by Numenta.org: Classic AI, Machine Learning, and Machine Intelligence. We present how Machine Learning and Machine Intelligence can affect actuarial analysts and actuaries alike. We then show how Machine Intelligence can be used to help regulators possibly construct a system or model that dynamically learns the behavior of insurance companies to predict failing insurance companies.

Description: A look into how machine learning models implement classification and regression at a deeper level. Covers topics such as multi-regression, softmax, and ROC charts.

Description: The newer predictive modeling tools are powerful but they work best when supported by a lot of computing power. Parallel processing is very powerful but often very difficult to implement. Dihui and Richard take us through the maze of options with H2O, SparkNet/MLib and Mahout. Which do you use for a generalized linear model (GLM), and which is better for high-volume data manipulation?