Description: The author discusses the use of genetic algorithms as they apply to actuaries, specifically the application of characteristics and their potential to solve life insurance asset and liability management problems. He also highlights various ways genetic algorithms can be used to solve a variety of actuarial problems.

Description: An introduction to Hidden Markov Models, which are used extensively in speech and facial recognition technology. They are a useful tool in forecasting time series and in developing categorizations of data based on the values of the data themselves.

Description: A general introduction about predictive modeling in insurance applications. Some basic models are discussed, such as Generalized Linear Model(GLM), Calssification and Regression Tree Model. Other models are also briefly introduced, including data clustering, neural networks and support vector machine.

Description: Presented at April 1986 Spring Meeting. This session discussion is about the techniques for understanding and analyzing trends in medical care, including: utilization and cost components, analysis of cyclical behavior, use of econometric models, and aggregate trend vs. employer-specific needs.

Description: Describes what a Delphi study is and why actuaries might want to use one.

Description: Identifies current trends and considerations when setting the mortality improvement assumption for individual life insurance in the US.

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: 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.