Description: An exploration of Canada’s federal regulator of financial institutions (OSFI) and its more formal assessment of the actuarial function of insurers.

Description: The editorial cautions that while the development of more sophisticated models may increase efficiency, it is important to consider the very real risk management costs associated with the increased complexity, efficiency and overconfidence in the predictive power of models.

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: Article is a detailed discussion of the Risk Management and Own Risk and Solvency Assessment (RMORSA) Model Act that was recently approved by the NAIC and is being adopted by legislatures across the country.

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: When companies engage in risk management activities, they can be seen as modifying their distributions of future outcomes. Often, this takes the form of trading off a sure cost for a decrease in some potential risk of loss. Companies may carry out these activities with several goals in mind, such as reduction in earnings volatility or preservation of capital. This paper will demonstrate a method for evaluating possible strategies by dividing the effects on costs and risks into three tranches: earnings, impact and estate. The tranches can be associated with different interests of the various stakeholders. This trifurcation analysis can help to explain the risk-reward trade-offs of different risk treatment alternatives and can aid in risk-related decisionmaking.

Description: Statistics were invented to describe heterogeneity and are now applied to tackle alea. It may not be appropriate. Let's clarify their field of relevance.

Description: Insurers can leverage information from their required Own Risk Solvency Assessment (ORSA) and use it internally to improve the likelihood of solvency and gain a competitive advantage.

Description: Some of the uncertainties related to catastrophe models are explored, leading to the suggestion that the actuary is well suited to understand and use these in the context of an ORSA exercise. Part of the essay collection "Risk Metrics for Decision Making and ORSA"

Description: Essays advocating for incentive compensation linked to the level of risk in an organization.