Description: Reviewing a text covering multiple statistical modeling topics: e.g., generalized linear models, decision trees, clustering, using the statistical language R.

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

Description: Description of SOA's initiative in predictive analytics

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

Description: Read the April 2019 issue of Predictive Analytics & Futurism published by the Predictive Analytics & Futurism Section of the Society of Actuaries.

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: Describes what a Delphi study is and why actuaries might want to use one.

Description: A significant fraction of insurance risk is characterized by the tail of the claims distribution, where we have much less data to model probabilities accurately. Ignoring the tails can lead to underpricing of tail risk and mispricing. Estimation of the tails can be complex due to a bias-variance tradeoff--the further out we go into the tail the more accurately we can model the extreme tail (lower bias) but the less data we have (increased variance). The statistical tools of Extreme Value Theory can be leveraged to more accurately estimate both the incidence and the severity of extreme events given that they occur. using the Generalized Pareto Distribution.

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