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

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: Enterprise risk management (ERM) needs to be seen in perspective of other management priorities and is not first in the hierarchy of corporate needs.

Description: A new reason for the inherent instability of the insurance business can be found in the literature of anthropology. The theory of plural rationalities, also known as Cultural Theory of Risk or simply Cultural Theory, posits that the world is populated by people with four fundamentally different attitudes toward risk. Presented at 2010 Enterprise Risk Management Symposium, Society of Actuaries, April 12-15, 2010.

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.

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.