Description: This article presents an introduction and overview of the R language. From Expanding Horizons, April 2007.

Description: Discusses the graphics capabilities of the computer language R.

Description: As a life reinsurer, understanding mortality risk is mission critical. Superior models of mortality risk lead to more competitive pricing in profitable market segments and more efficient use of capital. Surprisingly, modern statistical tools such as predictive modeling have not previously been used to model mortality data to gain this edge. In this paper, we present the use of a predictive modeling technique called projection pursuit regression PPR to model mortality data. We first describe the process of creating a PPR model using inter-company mortality data collected by the Individual Life Experience Committee of the Society of Actuaries, and present the resulting model. We then discuss the process of using this model as a starting point for creating a more detailed PPR model of internal company mortality data.

Description: This paper demonstrates the use of Quantile Regression in the development and understanding of conditional value at risk (VaR) models for enterprise risk management.

Description: Using the copula package in the R computing language for ERM modeling.

Description: Discusses how to use the Dependent Multivariate Fractional Brownian Motion, or DMFBM, function in the computer language R. The function can be used for, among other things, calculating quantities such as reserve requirements for a block of variable annuities with living benefit riders.

Description: In this article, the author compares risk identification to the computer game, FreeCell.

Description: This is the abstract of a paper in which the authors develop sixteen separate mortality models based on the current SOA mortality data being used for the preferred life study. The models are constructed using projection pursuit regression. The sixteen models arise from all combinations of sex, smoker status, select and ultimate status, and per life vs. per dollar mortality rates. In general the models confirm the following four facts: 1 Mortality rates increase as attained age increases. 2 Mortality rates increase as duration increases. 3 Mortality rates decrease as Face Amount Bands increase. 4. Mortality rates decrease if preferred underwriting is present.

Description: 2011 Enterprise Risk Management Symposium, Chicago. This is the abstract of the entitled paper.

Description: Feature article that introduces readers to the R computer language.