Description: A three-minute neuroscience-based image exercise is able to accurately unlock the emotional state of individual employees, permitting prediction of a host of specific needs. Actuaries can use this quantitative information at scale to target resources, benefits, and supports to increase the objective wellness and subjective well-being of workforces.

Description: This is the second article in a two-part series examining the potential value to companies of implementing a company profitability and risk dashboard.

Description: The predictive analytics application growth in the Asia-Pacific region has been gaining momentum.

Description: A review of the Johns Hopkins Data Science Specialization courses with special emphasis on how they may be useful to actuaries.

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: 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.