Description: The main focus of this article revolves around pension plans and their funding levels.

Description: Read the February 2020 issue of Predictive Analytics and Futurism published by the Predictive Analytics and Futurism Section. Newsletter provide practical information for the working actuary, articles on trends that impact sections and news about section activities.

Description: Summary of all issue articles, with theme of recognizing limits of classical actuarial methods.

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: This article summarizes the new research paper of "InsurTech: A Guide for the Actuarial Community" by Carrie Kelley and Kiki Wang. It serves as a quick read for our members to learn the latest development in the InsurTech space and explore the opportunities for actuarial practitioners

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: Actuaries are Known as highly ethical. Data Science poses ethical challenges for our profession.

Description: The chairperson of the section council issues call to members asking them to volunteer. He indicates that in particular, with the emergence of complexity science and other underutilized tools, there is plenty of room for volunteers, especially open spaces for new thought leaders.

Description: This article sheds light on the various ways machine learning can uncover patterns within data to add unique value and insights to businesses.

Description: An introduction of predictive modeling techniques that are applicable to insurance industry, including generalized linear model (GLM) and classification and regression tree model(CART). Other models such as data clustering, neural network, and support vector machine (SVM), are also briefly discussed.