Description: The article provides a high-level, non-technical, introduction to artificial neural networks (ANNs) and deep learning with an application to risk management. The method relies on typical ANN components such as nodes, activation functions, and optimization through gradient descent.

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

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: 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: 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.

Description: The author explains how he used judgmental forecasting techniques to develop a dynamic lapse assumption. He defines the techniques and provides readers with a step-by-step process.