Description: Read the December 2017 issue of Predictive Analytics & Futurism published by the Predictive Analytics & Futurism Section.

Description: Discussion about the need for, and approach to, measuring organizational expertise ton ensure the learning investment is resulting in expertise growth.

Description: A concise overview of Generative Adversarial Networks.

Description: History of Big Data, and approaches to cope with it. The author suggests that actuaries have been using similar methods for a long time; but need to get up to speed on the newer terms and variations.

Description: In this continuation of our July 2013 article we explore an algorithm for evaluating Hidden Markov Models in detail. A preferred method in speech recognition technology, Hidden Markov Models are a promising technique when dealing with data you suspect may be driven by a change in a unobservable state. The example in the article is provided in an Excel workbook for the reader to follow along with.

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.

Description: Discussion of several short stories by featured author Steve Mathys. Mathys was the winner of the 10th Speculative Fiction Contest Forecasting and Futurism Section prize.

Description: Deep learning is a method that allows neural networks to be constructed with more than just one or two hidden layers. These layers can then be trained independently and simultaneously of each other. This article provides an introduction to deep belief neural networks.

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: A discussion on using the Cox Proportional Hazards Model in developing a predictive underwriting model that produces a mortality multiplier for each individual.