Description: Is Risk/Return a sound tool for decision-making? No. It is not consistent, not objective, distorts decision and degrades governance. Why? What shall we do then?

Description: The author recommends the use of "minimally destructive scenarios" as part of an ORSA process, in order to deal with cognitive bias that can occur in risk decision-making. Part of the essay collection "Risk Metrics for Decision Making and ORSA"

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

Description: background history of the insurance industry and the role of actuaries within it, followed by the current environment and anticipated future environment.

Description: Update on issues, priorities and key messages from the Joint CAS / CIA / SOA Risk Management Section Council.

Description: A discussion of the need to connect the interrelated requirements of reserve requirements, capital requirements, risk management, IASB requirements and so forth.

Description: This article deals with enterprise risk management and how the techniques contained within the discipline can help prevent unforeseen large mistakes in strategy, product design, and product management

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: The author constructed the efficient frontier of new business sales by modeling the embedded value and economic capital against a universe of possible growth rates of the three lines of business in a typical three-year planning horizon. The ERM function can use the efficient frontier of new business to provide a more rigorous analysis of the impact of growth rates on the firm’s overall capital efficiency and value creation, hence forming a feedback loop with the businesses in the planning process to optimize the growth strategy.

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.