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: 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: Actuarial concepts of credibility can be extended to encompass additional information provided by new machine learning estimates. New statistical ideas also indicate how to generalize to blend arbitrarily many estimators, while not double-counting common information. For example, historical claim costs and risk scores both leverage diagnosis code information. We introduce two new estimators, one based on partial correlation and the other based on the machine learning-derived Shapley statistic, to address these limitations.

Description: Anders reviews the results of a recent survey of PAF section members and discusses initiatives that are planned in response.

Description: A summary of the Global Trends 2030 report, the fifth such report produced every four years by the National Intelligence Council. The report is based on the research and analysis of the 17 major intelligence agencies in the US. The summary covers the highlights of the report.

Description: The author reviews the book, The Perfect Swarm: The Science of Complexity in Everyday Life, by Len Fisher. He provides his own interpretation of the power of the swarm and touts the book as a straightforward and informative introduction to various aspects of complexity science.

Description: A discussion of what to do in the face of unprecedented change. With so many negative indicators of the economic future, suggestions are made for individual actions.

Description: Unstructured data is often synonymous with Big Data; and the term is used and misused a lot. Richard Xu and his colleague, Dihui Lai, dispel some of the confusion about Big Data in their article “Big Data In Life Insurance—Does It Exist? If So, How Should We Handle It?” They address some of the ways to deal with the mounting challenges of capacity and speed as data scales up rapidly in size. Hadoop was once just the name of a toy elephant; but there is nothing toy-like about how it has been employed to handle very large datasets. Five exabytes supposedly represents all the words ever spoken by human beings; but according to IBM, the new SKA telescope initiative will generate over an exabyte of data every day.1 How will we cope with big data? Read Richard and Dihui’s article for some hints.

Description: Actuaries are Known as highly ethical. Data Science poses ethical challenges for our profession.

Description: Uncertainty of a predictive model is a fact of life that many insurers could be overlooking at their peril without a framework for assessing it. The article provides a framework to understand the prediction uncertainty, which can be critical for the insurance operations using predictive models. Coin toss example explains model uncertainty and randomness inherent in making predictions to the number of heads expected. A mortality analysis case study illustrates how the Bayesian methodology can be used to develop the prediction uncertainty. In particular, the article shows that the Bayesian framework is a way to combine historical data and actuarial judgment, and directly address the model uncertainty through its posterior distribution.