Description: The first annual SOA Predictive Analytics Symposium was held in Chicago and exceeded all expectations in attendance, presentation quality, and relevant content.

Description: A general introduction about predictive modeling in insurance applications. Some basic models are discussed, such as Generalized Linear Model(GLM), Calssification and Regression Tree Model. Other models are also briefly introduced, including data clustering, neural networks and support vector machine.

Description: The editor of the newsletter provides a running list of articles included in the issue highlighting such topics as the applicability genetic algorithms, the power of group dynamics, judgmental forecasting, improvement in mortality and complexity science.

Description: Anders discusses how skills in PA techniques don't have to become the focal point for all actuaries, but they can be another tool in the toolbox for all of us.

Description: Ruminations on changing skill sets and professions, with a focus on actuaries and data scientists

Description: Meet “Julia,” a free programming language licensed by MIT that may help you with parallel computing. It may be an alternative tool for those who are interested in nested stochastic processes for actuarial research (if not for regulatory compliance).

Description: Can the aggregate results from a group be used to forecast something as subjective and judgmental as one single person’s opinion? Forecasting of this type commonly uses a set of techniques known as collaborative filtering which is discussed at length in this article.

Description: A discussion on artificial intelligence.

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