Description: This paper demonstrates the calculation of the moments of the distribution of aggregate life insurance claims from seriatim inforce data, assuming that each record represents an independent Bernoulli trial with its own known probability of a claim, q. Continuous distributions matching the first three or four moments are identified.

Description: discussion of the ongoing developments in reserving and risk-based capital RBC requirements for variable annuities including stochastic scenario projections

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 contingency table is a set of counts or frequencies obtained by classifying observations in two or more different ways. Log-linear analysis is applied to disability terminations, linking them to variables such as sex, age, region and maximum benefit period. Application to risk classification and underwriting are outlines.

Description: Panel session exploring a variety of issues connected to long-term disability reserve buyouts, including: · Advantages and risks for buyers and sellers · Use of appropriate reserving assumptions · Possible financial structures for a buyout

Description: A study on stochastic simulation literature as it applies to a health actuary.

Description: Summary of NAIC's Winter National Meeting

Description: Using the recent West Africa Ebola outbreak as an example, the authors discuss several modeling approaches that can be useful for estimating pandemic risk.

Description: The next evolution of actuarial systems in the U.S. will likely be a move toward a single system solution. This article defines a single system solution and illustrates how models can be shared across actuarial functions, the potential benefits of a single system solution, and describes potential mitigation strategies to address typical actuarial concerns.

Description: This paper develops a new technique, which allows the analyst to maximally use all thte historical interest rate information available in forecasting interest rates. Interest rates' daily change do not follow a stationary random process over long time periods. Instead, the process is comprised of stationary periods, each a few years long, with relatively short transitional periods between different stationary periods.