Description: Actuaries have the opportunity to transform to a new role that utilizes predictive analytics and new data sources such as epigenetics, to better humankind.

Description: Chairperson's column for the May 2019 issue of Predictive Analytics & Futurism.

Description: "A crucial importance in forecasting is to be able to distinguish cases when temporary slowdowns in health care utilization are followed by a “rebound” period of higher utilization. Come explore an analysis of a new mathematical model describing the return-of-care process. By means of simulation and case studies using the R programming language, the relationship between the visibility of these effects and the endogenous variance of the utilization time series is explored. After attending this session, you will be able to: Describe the parameters and mathematical form of the model Apply the model to real-world utilization data"

Description: Even the best algorithms are only effective if they are trained on appropriate data. This article discusses common pitfalls in creating a training data set.

Description: The author reviews a book the debunks some of the hype we read about using quantity at the expense of quality. She also points out some unrealistic outcomes when data scientists lack business knowledge.

Description: Predictive models have disrupted the traditional underwriting process. In this session, we will review a decade in the life underwriting of a disruptor – the confluence of actuaries, underwriters and clinicians. In addition, perspectives of other stakeholders will be explored.

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

Description: Gather with members and non-members in the China actuarial community to discuss recent topics and related issues in areas such as macroeconomic sharing, pension, investment, product strategy, and more.

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 artcle looks at the most important features necessary to conduct a meaningful quantitative analysis. The article also discusses how predictive analytics should be used as a tactic to anwer question and not an end in of itself.