Description: The June/July 2014 edition of The Actuary magazine—the Society of Actuaries’ flagship publication.

Description: An overview of the basic tooling and packages available for the Julia programming language, with a focus on why this is of interest to the actuarial profession. It covers some of the built-in language features as well parts of the Julia package ecosystem that make Julia ideal for modeling and data analysis. This is the second installment in a series of article aimed at actuaries. The first installment, Julia for Actuaries, was published in the October 2020 edition of Actuarial Technology Today, and covered the base language itself, its high-level, math-friendly syntax, and why the actuary of the future should consider its adoption.

Description: The August/September 2014 edition of The Actuary magazine—the Society of Actuaries’ flagship publication.

Description: This article explores the use of Generalized Additive Models and Multivariate Adaptive Regression Splines to model nonlinear relationships in a linear response model. These models use supervised methods to determine the optimal shape of the predictors in the model. Users are provided an example use case so that they may get started right away building models to explore complex relationships in new ways.

Description: The paper demonstrates that the most popular rationalization for evolving glide paths is flawed and should be discarded. The paper also demonstrates that glide paths should generally evolve for investors that endeavor to optimize outcomes and reevaluate their portfolios regularly.

Description: Regulatory change and increased focus on internal risk management are driving a renewed interest in model efficiency. In this session, we will review several new and existing tools, that show significant promise for advanced modeling of equity linked products. These include: • Clustering • Use of unique--and potentially a different number of--scenarios for each policy • Semi Monte Carlo (SMC) – this is a proprietary technique that is similar to a combination of Monte Carlo and numeric integration • Using a neural network approach to optimize some of the more complex and manual aspects of clustering and improve the fit of the clustered model • Combining SMC with the use of unique scenarios per policy

Description: Read the Quo Vadis Reinsurance.

Description: This is the first of two articles discussing technologies used to control data. this article covers data management strategies.

Description: This article discusses the risk management approaches of insurers. The author described some of the current practices of the total return approach, how the total return approach is used in managing risks as a process, and a model that takes future sales into consideration and determines the appropriate fair valuation method of an ongoing business.

Description: Complete issue of the August 2014 Risk Management newsletter.