Description: This article discusses data mining and the statistics of coincidence, and provides “guidelines for backtesting integrity” to help ensure results have value beyond mere coincidence. Editor’s Note: The following article originally appeared in the Winter 1998 issue of the Horizon, a publication of BARRA, Inc., and is reprinted with permission.

Description: This predictive modeling for life insurers paper by a Deloitte research team is one of a collection of essays that examine how existing or new methods or models can be applied to the life insurance industry.

Description: A review of the latest of a series of reports from Statistics Canada, covering data from various Canadian social benefits programs for 1971-1977.

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: This article describes the history and in some respects, the future of Big Data, and the tools we can use to handle it for PM and for machine learning. One such tool is Vowpal Wabbit, which sounds like something from a Bugs Bunny and Elmer Fudd cartoon (“Dwat that wabbit”), but it really is a popular approach to process a dataset of any size, as there is no need to load all the data into memory. Big data is forcing us to enhance many of our tools. So is the increased actuarial usage of stochastic-in-stochastic analyses (nested stochastic processes), which can result in unacceptably long program run times. Many of our popular computer languages are not inherently well suited for parallel computations.