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: This article takes the reader on a speculative trip to the not too distant future to explore a hypothetical new life insurance company that has completely reinvented the way it does business, and explores some of the potential benefits to consumers and the company’s stakeholders.

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.

Description: The purpose of this paper is to define a predictive model, based on known system attributes, for assessing risk associated with information systems.

Description: It talks about the major areas of data science and how to learn data science with the actuarial background.

Description: The article focuses on the use of predictive analytics in the development of experience studies, pros and cons of using predictive analytics versus traditional techniques, and the implications for setting assumptions and improving profitability.

Description: How actuaries can become comfortable accessing, integrating and building complex data sets from big and broad sources of information to study the value of multidimensional drivers of change.

Description: Reflections on SOA delphi study released Oct 2005