Description: Research report that explores key factors associated with an insurer’s reputational risk and conditions that would generate incentives for insurers to behave in a way that causes loss of their positive reputation.

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

Description: Regression to the mean is a phenomenon that arises when comparing sequential data points. It states that when an initial observation is extreme or an outlier, future observations will be closer to the average. Health care actuaries need to understand and be able to mitigate the effects of regression to the mean to avoid misapplying predictive models or misstating measurements of historical effects.

Description: This chapter introduces a few probability distributions that play an important role in understanding and analyzing healthcare data. While theoretical in nature, these distributions model real life if appropriately chosen. Probability distributions are mathematical representations of how statistical data behave.

Description: An exploration of the seasonality of mortality and how total population mortality is related to flu and pneumonia mortality.

Description: Second article in a series on how to use predictive analytics to improve the development of long-term care projection assumptions.

Description: This abstract describes a paper in which several issues surrounding the 2011 paper “Copula Regression”, Variance volume 5-issue1, by Rahul A. Parsa and Stuart A. Klugman, are discussed.

Description: An introduction of predictive modeling techniques that are applicable to insurance industry, including generalized linear model (GLM) and classification and regression tree model(CART). Other models such as data clustering, neural network, and support vector machine (SVM), are also briefly discussed.