Description: Alternative sources of learning available for free or at low cost if you wish to round out your F&F education about machine learning, artificial neural networks, genetic algorithms and the millennia of biological advances we draw from when we use these techniques.

Description: This article describes how different types of regression models used for fund modeling can affect the variable annuity's risk exposure report and attribution report.

Description: Contrary to what you might infer from the article title, the author provides counterpoint from some data science skeptics and suggests Big Data sometimes leads to sloppy analysis.

Description: Editorial for the December 2019 print edition of Predictive Analytics & Futurism newsletter describing the large number of informational resources available to PAF Section members.

Description: There is a growing amount of “off-balance sheet” talent in the US and Global workforce. This spans from consultants and contractors, to gig workers and the crowd (both curated crowd and open crowd). One such example is Uber and Lyft workers. Insurance companies and the Actuarial profession have started to experiment with platforms such as Kaggle and this will continue to grow and evolve. This article explores these new sources of talent for companies, the implications to the actuarial function, benefits and challenges of the evolving workforce.

Description: Overview of the seven trends affecting the Actuarial profession .

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: Summary of the July, 2013 newsletter with an executive overview of each article.

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: This is an article that discusses Nassim Nicholas Taleb’s work and its significance for actuaries.