Description: An autobiographical journey from actuarial student to data scientist.

Description: In Complexity: A Guided Tour, readers are given a short history lesson on the roots of dynamical systems theory, chaos and prediction.

Description: A summary of a new feature in the newsletter: a forecasting application contest. The Forecasting and Futurism Section will award a 16GB iPad to the section member who develops the best actuarial application of a judgmental forecasting method, incorporating best practices outlined in this issue’s article titled “Best Practices in Judgmental Forecasting.” In addition, we will feature the winning application in an issue of the newsletter.

Description: Big data, predictive analytics, machine learning and AI will likely transform the insurance market and the actuarial profession. The actuarial profession is seeing changes many markets, including health and property and casualty. This article describes these trends and provides an introduction into what companies and the Society of Actuaries (SOA) are doing to adapt and capitalize on these trends.

Description: Can the aggregate results from a group be used to forecast something as subjective and judgmental as one single person’s opinion? Forecasting of this type commonly uses a set of techniques known as collaborative filtering which is discussed at length in this article.

Description: Spark offers a way to get some dramatic speed and scalability advantages over the Map-reduce methodology whenever you are doing iterative data processing and particularly when you want interactive data analysis capability. Find out why “lazy execution” can be a desirable characteristic when dealing with big data.

Description: A discussion on the starting point of building a predictive model.

Description: A summary of how the Forecasting and Futurism Section is networking with other SOA sections and with external organizations to collaborate on various projects. Also highlights new events and projects happening on the forecasting and futurism front.

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.

Description: The author explains how he used judgmental forecasting techniques to develop a dynamic lapse assumption. He defines the techniques and provides readers with a step-by-step process.