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This article discusses Society of Actuaries research focusing on the challenges of middle Americans as they reach retirement, discusses the key issues facing middle Americans, and looks at directions for the best solutions. It focuses on market segmentation, planning issues and strategies, risk identification, and risk management, including solutions for addressing various risks. It also discusses key decisions and relates them to the middle market. [ABSTRACT FROM AUTHOR]

This research uses a survey mechanism to query actuarial science programs as to the usage of industry advisory boards at such programs in the U.S. The study’s methodology employs a questionnaire to examine the formation, governance, and utilization of such entities. The data collected identify the most widespread purposes and composition of industry advisory boards. The highest rated outputs of board members, in order, are addressing curriculum issues, guest speaker suggestions, and interactions with students via mock interviews and student presentations. A comparison of results to a similar survey of advisory boards at risk management and insurance programs is conducted as well. [ABSTRACT FROM AUTHOR]

In 2007, the B.S. in Applied Mathematics program consisting of five concentrations, including Actuarial Science, began at West Chester University of Pennsylvania, and we graduated our first class (of one) that December. We describe our program, some ideas to consider when planning your own program, and share some of the successes of our program which have led to our department having close to 40 Actuarial Science majors in six short years! We will focus on our courses and exam preparation, our internship program, how we built connections with businesses and organizations, and our plans for the future. [ABSTRACT FROM PUBLISHER]

The problem of clustering climate data observation sites and grouping them by their climate types is considered. Machine learning–based clustering algorithms are used in analyzing climate data time series from more than 3,000 climate observation sites in the United States, with the objective of classifying climate type for regions across the United States. Understanding the climate type of a region has applications in public health, environment, actuarial science, insurance, agriculture, and engineering. In this study, daily climate data measurements for temperature and precipitation from the time period 1946–2015 have been used. The daily data observations were grouped into three derived data sets: a monthly data set (daily data aggregated by month), an annual data set (daily data aggregated by year), and a threshold exceeding frequency data set (threshold exceeding frequency provides the frequency of occurrence of certain climate extremes). Three existing clustering algorithms from the literature, namely, k‐means, density‐based spatial clustering of applications with noise, and balanced iterative reducing and clustering using hierarchies, were each applied to cluster each of the data sets, and the resulting clusters were assessed using standardized clustering indices. The results from these unsupervised learning techniques revealed the suitability and applicability of these algorithms in the climate domain. The clusters identified by these techniques were also compared with existing climate classification types such as the Köppen classification system. Additionally, the work also developed an interactive web and map‐based data visualization system that uses efficient big data management techniques to provide clustering solutions in real time and to display the results of the clustering analysis. [ABSTRACT FROM AUTHOR]