Description: Summary of the July, 2013 newsletter with an executive overview of each article.

Description: A description of the random generalized linear model (RGLM) and how to use it.

Description: Overview of regularization and least angle regression approaches.

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: The well-known examples of predictive modeling “successes” are numerous. Perhaps less widely recognized is the myriad of ways in which a predictive model can fail to perform as expected, often due to misconceptions or misrepresentations on the part of the analyst. This article, which is intended for a general actuarial audience, focuses on three such pitfalls. By understanding these three common pitfalls, readers may guard against them within their own work, teams, and organizations.

Description: Editorial for the December 2019 print edition of Predictive Analytics & Futurism newsletter describing the large number of informational resources available to PAF Section members.

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: Machine learning models are not black-boxes; these models can easily be interpreted using tools like SHAP, LIME and Anchor. These tools can facilitate the understanding of the model and the business problem. We can used these tools to build better models and communicate how predictions are generated. This article demonstrates their use in a mortality model.

Description: This article explores the use of Generalized Additive Models and Multivariate Adaptive Regression Splines to model nonlinear relationships in a linear response model. These models use supervised methods to determine the optimal shape of the predictors in the model. Users are provided an example use case so that they may get started right away building models to explore complex relationships in new ways.

Description: This article discusses how to handle semisupervised data. This type of data might have a large number of observations, yet a small number of labels.