Description: Feature article discussing model monitoring

Description: Feature article discussing futurism and a study that contains in-depth descriptions of the Delphi and TIA methods; listings of the rationales and thought processes, the plausible future developments that could influence the values of these four economic variables and the resulting “fan of possibilities” for the values of these variables

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

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

Description: Feature article discussing the usefulness of Lorenz and Lift curves.

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: An introduction to creating Shiny web applications, with sample code available to reproduce the interactive application displayed in screenshots in the article.

Description: Impact investing is investments in “companies, organizations and funds with the intention to generate social impact alongside a financial return”.

Description: Overview and review of books to teach yourself about deep learning.

Description: It is about using actuarial judgement to validate the predictive model.

Description: Read the December 2017 issue of Predictive Analytics & Futurism published by the Predictive Analytics & Futurism Section.

Description: In this article, we are going to discuss the AR model by making connections to time-dependent ordinary differential equations.

Description: Short descriptions of the various articles in the June issue.

Description: This article describes how to model variable annuities using a GPU along with some of the stumbling blocks.

Description: Up until recent past, the artificial intelligence portion of data science was looked upon cautiously due to its history of booms and flops . In the latest stream of events, major improvements have taken place in this field and now deep learning, the new leading front for Artificial Intelligence, presents a promising prospect for overcoming problems of big data.

Description: A review of the Bridging the Gap Series: Application of Predictive Modeling in VA/FIA Risk Management at the Equity Based Guarantees Conference in Chicago.

Description: Looks at free and inexpensive resources for the novice in predictive analytics, covering the data analysis theory as well as the most commonly used languages/software used in the field.

Description: December 2013 issue of Forecasting & Futurism newsletter

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: The July 2013 issue of the SOA’s Forecasting & Futurism section’s newsletter, Forecasting & Futurism.

Description: A book review by Scott McInturff of The Signal and the Noise: Why So Many Predictions Fail - But Some Don't authored by Nate Silver.

Description: The author discusses the various components that may have an effect on the future of mortality such as the extinction of numerous species of flora and fauna the growth of agribusiness genetically modified foods, better public health institutions and programs, etc.

Description: The author discusses expectations for incoming actuarial science students, emphasizing that a solid grasp of algebra and traditional mathematical skills is not enough. In addition, the author states that today’s business environment dictates that students be well versed and that their backgrounds include familiarity with complexity science.

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.

Description: This article provides a brief introduction to forecasting methods that are potentially relevant to actuarial work.