Description: This is the full research report for the thirteenth Emerging Risk Survey which summarizes the results of the survey.

Description: 1999 SOA Annual Meeting, San Francisco. The panel discussed how the actuarial profession may contribute to the quantification and management of various types of risks that insurers and other financial service providers assume.

Description: This article discusses the causes of the 1918 influenza pandemic, the reasons it occurred and the likelihood that it could happen again. In addition, the author discussed ways, in general, to model and manage risks that occur infrequently, yet have a significant impact.

Description: Research report describing the results of an Emerging Risks Survey conducted in 2015. The report includes comparisons to the previous surveys.

Description: The Society of Actuaries released research in 2007 that develops a model for a U.S. individual life insurance company to consider its potential liability exposure as a result of a pandemic. Sponsored by the Mortality and Longevity Strategic Research Program Steering Committee, this paper updates several model assumptions including mortality in light of COVID-19.

Description: The panel at this session of the SOA 2001 Annual Meeting discusses the basics of embedded value [EV], how EV can be used to measure an insurance company's results, and current best practices.

Description: 1996 Valuation Actuary Symposium Proceedings, Session 11, discusses health financial projections and projection modeling in general.

Description: French language version of Risk Management, August 2014 newsletter.

Description: Complete issue of the August 2014 Risk Management newsletter.

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.