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

Description: A discussion of what to do in the face of unprecedented change. With so many negative indicators of the economic future, suggestions are made for individual actions.

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: Reflection on three years on the PAF section council and where we're headed next.

Description: This is an Editorial describing the need for actuaries to upgrade skills or risk minimization from artificial intelligence.

Description: The author reviews a book the debunks some of the hype we read about using quantity at the expense of quality. She also points out some unrealistic outcomes when data scientists lack business knowledge.

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: Actuaries can empower their understanding of policyholder behavior with third party data. Modeling policyholder behavior should start from developing knowledge of who they are, instead of actuaries assuming what a rational behavior should be.