Description: This paper employs a static-component projection methodology as a first step in a more complex research agenda focusing on developing detailed population projections in which mortality, disability, and disease are represented as linked phenomena with changes in any one impacting the other two. From Retirement Needs Framework, M-RS00-1.

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: Research report sponsored by SOA to model and compare projected costs of different proposals for a Medicare prescription drug coverage plan. 3 types of costs are studied: net benefit cost estimates per member per month, continuance table data, and projected annual costs to the government over 10 years for two selected plan designs.

Description: Jeff Sagarin is one of the most well-known figures in the sports analytics world, with his sports ratings appearing in USA Today for over 30 years. Few may know that he started his career in the actuarial world, even passing three exams before his sports ratings took off. This article explores the parallels between the challenges of developing sports rating systems and the challenges that actuaries face every day.

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

Description: Article provides a market scan of recent state proposals for Medicaid work requirements as well as measures the impact to enrollment in different states.

Description: Results of survey, conducted recently by the Taxation Section of the Society of Actuaries, on how federal income tax is reflected in the projection work being performed.

Description: Explores the open vs. closed systems debate with regards to actuarial modeling and exposes the over-simplification. Valuable for anyone who uses models.

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: Summary of NAIC's Winter National Meeting