Description: An autobiographical journey from actuarial student to data scientist.

Description: In An Introduction to Incremental Learning, by Qiang Wu and I describe how machine learning is a natural enhancement to predictive analytics through its many useful tools to derive meaning from a lot of otherwise unusable data such as handwriting. Again, there are formulas involved; but we have tried to keep them understandable to the actuary who isn’t already familiar with stochastic gradient descent, perceptrons, and principal component analysis. Incremental learning can help you refine the estimations you derive from more traditional actuarial methods.

Description: The article introduces the concept of overfitting in predictive modeling and illustrates how it hinders our ability to predict new data well.

Description: A summary of a survey the Society of Actuaries recently conducted of health payer and provider executives to glean insights into predictive analytics trends.

Description: An exploration of moving predictive modeling capabilities in R to real time operations.

Description: The new chairperson of the SOA's PAF section, Anders Larson, discusses the variety of ways in which the section helps actuaries learn about PAF.

Description: A discussion on machine learning and how it can be used for forecasting.

Description: A discussion on how to use data visualization for model controls.

Description: This article discusses techniques used to create risk scores to help determine appropriate primary care panel sizes for a provider organization.

Description: Read the August 2018 issue of Predictive Analytics and Futurism newsletter published by the Predictive Analytics and Futurism Section.