Description: Chairperson's column for the May 2019 issue of Predictive Analytics & Futurism.

Description: Chairperson's column for the May 2019 issue of Predictive Analytics & Futurism.

Description: The Possible Role of Convolutional Neural Networks in Mortality Risk Protection

Description: Editorial for the May 2019 issue of Predictive Analytics & Futurism newsletter.

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

Description: Read the December 2018 issue of Predictive Analytics and Futurism published by the Predictive Analytics and Futurism Section.

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: 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: Actuarial concepts of credibility can be extended to encompass additional information provided by new machine learning estimates. New statistical ideas also indicate how to generalize to blend arbitrarily many estimators, while not double-counting common information. For example, historical claim costs and risk scores both leverage diagnosis code information. We introduce two new estimators, one based on partial correlation and the other based on the machine learning-derived Shapley statistic, to address these limitations.

Description: A description of Data Science best practice tools and how they can apply to general actuarial work.