Description: ERM may exist in name only without a risk-aware culture. The article uses battle-tested approaches & behavioral insights to drive change and embed risk culture.

Description: Cyber threats and their costs are a continuously evolving, moving target and a source of uncertainty.

Description: This article discusses the current state of the IAIS' initiative on their group Insurance Capital Standard.

Description: An abbreviated version of a larger chart produced by the International Actuarial Association.

Description: Hear from the editor’s on the August 2018 issue of Risk Management published by the JRMS.

Description: Building on the Part 1 article, Introduction to the Representative Scenarios Method, this article describes testing the methodology with respect to four product types, fixed indexed annuities with GLIBs, level term life insurance, universal life with secondary guarantees, and variable annuities with GLWBs. This Part 2 article describes testing results, refinements to the RSM methodology, and next steps.

Description: This article explores the methods of determining the optimal timing for risk management projects.

Description: This article discusses the use of mortality catastrophe bonds as a risk mitigation tool.

Description: Update on research activities.

Description: Recently researchers, practitioners, and regulators had intense debates about how to treat the data collection threshold in operational risk modeling. There are several approaches under consideration—the empirical approach, the “naive” approach, the shifted approach, and the truncated approach—for fitting the loss severity distribution. Since each approach is based on a different set of assumptions, different probability models emerge. Thus, model uncertainty arises. When possible we investigate such model uncertainty analytically, otherwise we use Monte Carlo simulations. For specific parametric examples we consider exponential and Lomax distributions which are special cases of the generalized Pareto family. Our primary goal is to quantify the effect of model uncertainty on risk measurements. This is accomplished by evaluating the probability of each approach producing conservative capital allocations based on the value-at-risk measure. These explorations are further illustrated using a real data set for legal losses in a business unit (Cruz, 2002).