Description: Strategic Risk Management was given lower priority in a recent survey of ERM Objectives. This article tells how the author believes that recent events have caused many to shift to a more short term focus and in the future there will be a shift back to a priority for the longer term view that stands behind Strategic Risk Management when the environment settles down and things again seem to be more predictable.

Description: This article updates the reader on the activities of the recently formed International Committee of the Joint Risk Management Section. With global webinars and more activities, this committee is ensuring the risk management word is spread throughout the world.

Description: The author suggests that true actuarial method is a blend of all three modes of thinking, clinical, statistical and heuristic, and using all three early in our processes will make us smarter modelers. From The Actuary, February 2012, Volume 9, Issue 1.

Description: This article discusses four approaches to managing the enterprise-wide risk: Diversification, Loss Controlling, Risk Trading and Risk Steering.

Description: Feature article discussing the four main perspectives of ERM.

Description: Feature article describing more realistic stages of risk and how to manage each stage. From The Actuary Magazine, Dec 2009

Description: The first of two parts of a panel discussion, by conference call, on the the evolution of Enterprise Risk Management at life insurance companies, and actuaries' roles in that evolution.

Description: Feature article discussing how ERM needs to expand to cover the many different attitudes toward risk.

Description: In this article, the author examines five common perspectives on risk and see how they affect not just each participant’s own choices but everyone else’s choices, as well.

Description: People naturally observe risk in the form of the range of experienced gains and losses which is usually represented in statistical terms by standard deviation. In most cases, there are not enough observed values to reliably discern the true amount of losses that are possible in extreme situations. Statistical techniques that have long been applied to insurance company risks are being used to extrapolate from observed risk to extreme tails of the distribution of gains and losses. These processes are essentially an extrapolation from the “known” risk of volatility near the mean to “unknown” risk of extreme losses. “Fat tails” is a term often applied to risks where the likely losses in rare situations significantly exceeds the predictions of a Gaussian (“thin tailed”) distribution. This paper will look at the relationship between the observed risk and the extreme risk in statistical insurance r isk models and discuss how we might create a discussion among model users about the appropriateness of that relationship for specific types of risks.