Description: This report is a technical paper on a Predictive Analytics Framework and Reusable Workflow for Experience Analysis in actuarial studies. It covers data analysis, machine learning models, methodologies, and model interpretation techniques.

Description: The purpose of this paper is to define a predictive model, based on known system attributes, for assessing risk associated with information systems.

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

Description: How actuaries can become comfortable accessing, integrating and building complex data sets from big and broad sources of information to study the value of multidimensional drivers of change.

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: LTC insurers can proactively identify policyholders more likely to go on claim in the near future, and methods to potentially delay or prevent claims, benefiting company and customer.

Description: Feature article describing how exponential technologies will affect the actuarial profession.

Description: We compare two predictive analytics methods - GLM and GBM in terms of predictiveness vs. interpretability.

Description: Feature article discussing the ways artificial intelligence will influence the actuarial field.

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