Description: The Mortality and Longevity Strategic Research Program Steering Committee (MLPSC) released this report, which examined the latest trends in obesity and developed an estimate of the impact of obesity on mortality and morbidity costs in the U.S. and Canada.

Description: Supplemental Appendices for Using Social Determinants to Identify Profiles of U.S. Children with Low Dental Expenditures and High Medical Expenditures with Clusters

Description: Supplemental Appendices for Using Social Determinants to Identify Profiles of U.S. Children with Low Dental Expenditures and High Medical Expenditures with Clusters

Description: Actuarial weather extremes, February, 2021

Description: Supporting Excel file for Getzen Model 2022.

Description: This study and report have the following primary purposes: 1. Evaluate recent mortality experience relative to standard industry mortality tables, at a broad level. 2. Observe general trends in mortality experience by key policy characteristics. Where possible, provide insights into the industry changes contributing to the observed trends. 3. Provide the underlying data in spreadsheet pivot table format for further investigation by qualified actuaries. Provide data also in a delimited text file and Tableau dashboards for use with other software tools.

Description: Machine learning algorithms fit models based on patterns identified in data and can be very complex. In this report, we describe and illustrate a range of methods for interpreting machine learning models from the growing field of Interpretable Machine Learning (IML).

Description: This primer paper seeks to introduce and update the actuarial profession to the topic of insurance policies and genetics and study the impacts on insurance and potential responses.

Description: The monograph walks the reader through the hindsight, insight and foresight building process while discussing theoretical and practical issues supporting the building of valid forecasts with provenance for each of these schema examples. That is, the analyst can document the iterative ‘profiling change processes’ mindscape and landscape elements by which and through which the successful forecast is built. This kind of forecast construction ameliorates the ‘black box’ problem of generating pattern output without an understanding of the pattern’s creation or contextual meaning that is often seen when using other methods, particularly machine learning and artificial intelligence. This issue is discussed, referencing appropriate thought leaders.

Description: Sam Gutterman's winning essay.