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

Description: Feature article providing a brief introduction to Cython.

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

Description: In Complexity: A Guided Tour, readers are given a short history lesson on the roots of dynamical systems theory, chaos and prediction.

Description: Discusses the three complexity science sessions at the Annual Meeting that were sponsored by the Section.

Description: High level overview on how to implement a risk adjustment chart chase program.

Description: Actuaries can empower their understanding of policyholder behavior with third party data. Modeling policyholder behavior should start from developing knowledge of who they are, instead of actuaries assuming what a rational behavior should be.