Description: This article describes how different types of regression models used for fund modeling can affect the variable annuity's risk exposure report and attribution report.

Description: Summary of the reasons behind our section name change; and a short description of each of the articles in this issue.

Description: December 2016 edition of Predictive Analytics & Futurism

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: A discussion on how to avoid bias when choosing research topics.

Description: Up until recent past, the artificial intelligence portion of data science was looked upon cautiously due to its history of booms and flops . In the latest stream of events, major improvements have taken place in this field and now deep learning, the new leading front for Artificial Intelligence, presents a promising prospect for overcoming problems of big data.

Description: This article goes through some basic data exploration techniques to show how different approaches look.

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.