Description: Read the November 2020 issue of Predictive Analytics and Futurism published by the Predictive Analytics and Futurism Section. Newsletters provide practical information for the working actuary, articles on trends that impact sections and news about section activities.

Description: Read the August 2020 issue of Predictive Analytics and Futurism published by the Predictive Analytics and Futurism Section. Newsletters provide practical information for the working actuary, articles on trends that impact sections and news about section activities.

Description: This article gives a brief overview of ways to extend clustering analysis beyond basic methods such a k-means

Description: Read the June issue of Predictive Analytics & Futurism published by the Predictive Analytics & Futurism Section. Newsletters provide practical information for the working actuary, articles on trends that impact sections and news about section activities.

Description: Description of methods for extracting and reformatting the existing mortality tables using R. Methods and formats for storage are discussed in the second article. Intent is to generate discussion regarding the format of our stored mortality tables. Value is in the demonstration of task automation in R and in the reduction of duplicate work across companies related to reformatting tables.

Description: Contrary to what you might infer from the article title, the author provides counterpoint from some data science skeptics and suggests Big Data sometimes leads to sloppy analysis.

Description: Our firm has developed the first publicly available insured lives mortality table for Cambodia using data science methods. This essay provides a brief background, our approach and the impact of our work.

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: Principal component analysis is an unsupervised linear transformation mainly used for dimension reduction. Determine key numerical variables with maximum variances in a dataset, identify the correlation and removal of redundant variables are the key aspects of this exploratory data analysis.