Description: Data requirements for the Society of Actuaries cancer experience study, for those submitting exposure and claim information.

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?

Description: While many know that machine learning is a big deal and even have a general idea of what it is, many do not know what machine learning can do for them. As an alternative to citing achievements or explaining algorithms, this article outlines the challenges that occur in statistics and the solutions that machine learning offers. Some of those challenges include correlated predictors, missing data, data interaction, and prediction intervals. Corresponding solutions include regularization technics, missing value imputation with KNN, tree based models, and cluster analysis for backtesting.

Description: The following case study walks through the steps of a predictive modeling exercise to illustrate one way analytics can streamline the underwriting paradigm.

Description: An economic analysis of bariatric surgery and health care costs supports the case for health plans to cover these procedures.

Description: Description of the vast variety of predictive analytics topics covered by the Forecasting & Futurism section and introduction to the July 2015 issue of the newsletter.

Description: A study of disability experience examines cost burden and degree of disability, and how these measures vary across different subpopulations. This article summarizes a presentation given at the Society of Actuaries’ Living to 100: Survival to Advanced Ages International Symposium held on Jan. 7–9, 2008 in Orlando, Florida. An abstract of the author's paper was presented in the September 2008 issue of Long-Term Care News.

Description: Looks at free and inexpensive resources for the novice in predictive analytics, covering the data analysis theory as well as the most commonly used languages/software used in the field.

Description: The authors explain big data and the potential uses of this information, especially as it is beginning to be adopted by the life insurance industry.

Description: A closer look at family tier factor rating assumptions, and how well they reflect actual claim cost data.