Description: In this article, the author discusses the considerations and advantages of implementing an integrated enterprise risk management program for a health insurance company, and presents a summary of Aetna's experience with ERM as a case study.

Description: Presented at June 1985 Spring Meeting. Futures research is a demonstrated scientific approach to understanding what the future environment will be. Presentation discusses what it is, where did it start, who uses it and why, does it work, who benefits, what are some simple futures research techniques, how to make it work, what are today's key trends for tomorrow's insurance industry, and how to start a simple futures research program.

Description: In this article, the author examines Nassim Taleb’s work and writings and discusses its relevance to the future of the actuarial profession.

Description: The article gives a brief history of the development of the Delphi method, and describes the use of the Delphi method in studies by the Society of Actuaries over the past 25 years.

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: Given the limits of classical actuarial modeling and forecasting techniques, this newsletter contains articles discussing several ways to supplement your toolset.

Description: Book review of: Social Science from the Bottom Up, by Joshua M. Epstein and Robert Axtell

Description: Presented at October 1996 Annual Meeting. This session presents a forecasting model of economic assumptions that are inputs to projections of the Social Security system. Social Security projections are made to help policymakers understand the financial stability of the system.

Description: Author response to Eric Friedman’s comments on ‘The Case for Stochastic Present Values,” by Dimitry Mindlin

Description: While many know that machine learning is a big deal and even have a general idea of what it is, many do not know what machine learning can do for them. As an alternative to citing achievements or explaining algorithms, this article outlines the challenges that occur in statistics and the solutions that machine learning offers. Some of those challenges include correlated predictors, missing data, data interaction, and prediction intervals. Corresponding solutions include regularization technics, missing value imputation with KNN, tree based models, and cluster analysis for backtesting.