Description: Summary of what the section has accomplished over the past year.

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: Process of building a predictive model and related considerations.

Description: It talks about the major areas of data science and how to learn data science with the actuarial background.

Description: Defines Accelerated Underwriting programs as a transformational trend, and outlines a process for evaluating mortality implications of such programs.

Description: In this article, we will overview some popular tree-based models and understand how such models may be applied to the insurance industry.

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

Description: Introduction to PMML and how to use of it in the R programming language. PMML is an interchange format for predictive models.

Description: Second article in a series on how to use predictive analytics to improve the development of long-term care projection assumptions.

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.