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: High level overview on how to implement a risk adjustment chart chase program.

Description: In 2007, members of the Forecasting & Futurism Section were joined by members of the Technology Section and the Marketing & Distribution Sections to conduct a Delphi study about possible developments in the life insurance industry. It was named it “Blue Ocean Strategies in Technology for Business Acquisition by the Life Insurance Industry.” The goal was to have a panel of experts—actuaries and other financial professionals from across the country—identify and debate possible new approaches to acquiring business by life insurers. Panelists were asked to answer questions about possible “Blue Ocean Strategies.” This article highlights the ideas that resulted from those discussions as well as survey results on the same subject.

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: An introduction to the methods and parameters for Bayesian Hyperparameter Optimization in a Life Insurance context.

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: December 2016 edition of Predictive Analytics & Futurism

Description: A discussion on how to avoid bias when choosing research topics.

Description: A discussion on the myths of artificial intelligence.

Description: Describes the next generation of predictive analytics