Description: Editorial Introduction to the issue.

Description: December 2016 edition of Predictive Analytics & Futurism

Description: This article goes through some basic data exploration techniques to show how different approaches look.

Description: July 2012 issue of Forecasting & Futurism Newsletter

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

Description: A significant fraction of insurance risk is characterized by the tail of the claims distribution, where we have much less data to model probabilities accurately. Ignoring the tails can lead to underpricing of tail risk and mispricing. Estimation of the tails can be complex due to a bias-variance tradeoff--the further out we go into the tail the more accurately we can model the extreme tail (lower bias) but the less data we have (increased variance). The statistical tools of Extreme Value Theory can be leveraged to more accurately estimate both the incidence and the severity of extreme events given that they occur. using the Generalized Pareto Distribution.

Description: This paper explores how health care complexity challenges us to expand our repertoire of actuarial methods—and even recast our trend driver paradigm—in order to do our jobs better and maintain our credibility.

Description: This article describes the use of artificial society modeling to gauge the effect of insurance agent population and effectiveness on individual life insurance sales. This is part of an ongoing effort to extend the use of one type of agent-based modeling beyond health care, but is far from a practical application at this point.

Description: A discussion on how to avoid bias when choosing research topics.

Description: A summary of a survey the Society of Actuaries recently conducted of health payer and provider executives to glean insights into predictive analytics trends.