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

Description: Ways of closing the mortality protection gap, by leveraging some of the new technology, data and analytics to reduce the friction in the buying process.

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

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: Mining Electronic Health Records using a Natural Language Processor.

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 newer predictive modeling tools are powerful but they work best when supported by a lot of computing power. Parallel processing is very powerful but often very difficult to implement. Dihui and Richard take us through the maze of options with H2O, SparkNet/MLib and Mahout. Which do you use for a generalized linear model (GLM), and which is better for high-volume data manipulation?

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