Description: Given the limits of classical actuarial modeling and forecasting techniques, this newsletter contains articles discussing several ways to supplement your toolset.

Description: The article is about how current advancements in AI can both help and potentially replace actuaries. We focus on three types of AI as defined by Numenta.org: Classic AI, Machine Learning, and Machine Intelligence. We present how Machine Learning and Machine Intelligence can affect actuarial analysts and actuaries alike. We then show how Machine Intelligence can be used to help regulators possibly construct a system or model that dynamically learns the behavior of insurance companies to predict failing insurance companies.

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

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

Description: Feature article providing a brief introduction to Cython.

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

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

Description: This article gives a brief overview of ways to extend clustering analysis beyond basic methods such a k-means