Description: Description of the vast variety of predictive analytics topics covered by the Forecasting & Futurism section and introduction to the July 2015 issue of the newsletter.

Description: This article describes methodologies for detection and deterrence of Medicare fraud. It also describes the validation of PRIDIT for fraud detection in Medicare.

Description: Neural networks have fallen only to rise again twice. The latest incarnation borrows much from the past but adds elements to support deep learning. This article demonstrates the state of the art in deep neural network programming.

Description: This predictive modeling for life insurers paper by a Deloitte research team is one of a collection of essays that examine how existing or new methods or models can be applied to the life insurance industry.

Description: We highlight a new approach leveraging predictive analytics to identify high risk and rising risk opioid users for more effective screening and follow-up care.

Description: Hear from Michael Xiao about how actuaries are combining business knowledge and data science skills to excel in Kaggle competitions.

Description: This article describes a method that can be useful when re-fitting a risk score model to a population of only moderate size. Learn how to use concepts like ridge regression to train a model with partial credibility.

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: Charles Cadman and his team may be new to Kaggle’s data science challenges, but that didn’t stop them from achieving a top-ten finish.