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: 2003 Valuation Actuary Symposium, San Diego, CA. In this Teaching Session the instructor discussed the importance of making sure the correct inferences were drawn from stochastic testing exercises. Topics included: 1. Applying the assumption of a normal distribution. 2. Drawing inferences about, and creating confidence intervals around, a population mean. 3. Drawing inferences about, and creating confidence intervals around, tail percentiles. 4. Certifying anticipated mortality under XXX in a mathematically concise way.

Description: 2003 Valuation Actuary Symposium, San Diego, CA. This panel discussion provided an overview of the current risk-based capital RBC requirements for life insurers as well as insight into topical issues and potential changes to RBC. Discussion topics included: 1. Recap of RBC law and RBC calculation rules. 2. Modeling potential impact of alternative actions. 3. The tax impact of the factors. 4. Stochastic capital requirements for variable products. 5. The role of reinsurance and offshore solutions.

Description: Quantitative operational risk assessment is essentially based on stochastic scenario modeling of operational loss sequences. Given the lack of reliable historical data in most cases, mathematical methods should meet even stronger requirements in terms of results received and include specific features, namely: rare event analysis, uncertainty analysis and human factor analysis, etc. In this paper, the analytical-statistical simulation approach ASSA is considered as the most flexible approach to stochastic scenario loss sequence modeling and compared with Markov chain, fault/event tree and Monte Carlo, in terms of scenario loss sequence model adequacy and calculation cost. Probabilistic risk analysis software PRAISE and some applications are given. Presented at 2010 Enterprise Risk Management Symposium, Society of Actuaries, April 12-15, 2010.

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: With the adoption of the C-3 Phase 2 Project of the American Academy of Actuaries, it is now required that stochastic scenarios be developed for establishing reserves for variable annuities. This project has led to the idea of Principle-Based Reserving PBR where many of the same techniques will be used to set life insurance reserves. This article describes the choice of a Random Number Generator RNG to develop the random scenarios.

Description: Article Disparate Origins Of Life And Non-Life Insurances by Beard in The Actuary, February 1979, Volume 13, Number 2

Description: This presentation is a panel discussion, session number 9PD, from the 2000 Valuation Actuary Symposium, held September 14-15 in Washington, DC. A fundamental assumption in stochastic asset/liability modeling is the underlying economic scenario generator. In this session the panelists discuss various scenario generation techniques, assumptions underlying bond and equity scenario generators, and choosing suitable economic scenario generators for different applications. Discussion from participants is included.

Description: A discussion of the possible effort required and impacts resulting from the implementation of C3 Phase II for establishing risk-based capital for variable annuities. This article will describe practical considerations gleaned from actual valuation calculations recently performed by a consulting firm.

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.