Description: The Future of Insurance is described, as seen by some heads of start-up FinTech companies.

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: Given the ubiquity of judgmental forecasting, it is important to understand the potential biases inherent in human judgment—the traps that can snare us—and to learn methods and best practices to avoid them. The aim of this article is to help readers with these goals.

Description: July 2011 issue of the Forecasting and Futurism Section newsletter.

Description: The author discusses the two-part presentation on complexity science tools at the SOA Annual Meeting October 2010. He explains that presentations went back to basics with explanations about the difference between modeling tools used to employ deterministic methods for classical actuarial models.

Description: This article discussed the NEAT family of neural network algorithms. NEAT automatically determines the number of hidden neurons, as well as their structure. Additionally HyperNEAT can scale to very large problem domains.

Description: Introduction to the topics of the issue; and anecdote regarding the value classical versus newer techniques.

Description: Futurism section members participated in this Delphi study, which had multiple choice questions on a range of life, health, economic and professional issues. The article provides background on the study and comments on the predictions of participants for 2010 (and 2050) in light of experience to date.

Description: The author, Mary Pat Campbell, participated in the 4th season of the Good Judgment Project, a prediction tournament. She explains the basic metric used for evaluating predictions in the contest and some of the lessons learned from her participation.

Description: Matrix decomposition techniques are used to find main features of a dataset. These features can be used to describe the data or to find clusters of objects. The decompositions have problems when the matrices have missing data or are from different exposures. This article examines the issue and some papers dealing with it.