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: While intended to assist health plan actuaries responsible for stress testing and ORSA filings, this session will also be informative for actuaries interested in communicating the impact of potential catastrophic events for health plans to their management and board. It will begin with a discussion of event thresholds for stress tests and their impact on capital and financial statements. Presenters will take a detailed look at several catastrophic event case studies (historical and hypothetical) in the following areas: healthcare specific (e.g. pandemic, specialty Rx), financial institution/credit market, operational/business, strategic and multi-event scenarios.</

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: Companies need to learn to plan for uncertainty by taking into consideration downturns, not just an environment of moderation and occasional booms.

Description: We can do a better job of communicating the degree of &quot;riskiness&quot; that is inherent in our models of risk. The &quot;Coefficient of Risk&quot; is one way to present fatness of model tails in simple terms using widely understood basic concepts.

Description: ICP 16 and its requirement for an Own Risk and Solvency Assessment (ORSA) will offer opportunities for the actuarial profession to provide uniquely well-educated and professional assistance to this process.

Description: In this article the author discusses creating a common language for ORSA, capital and stress.

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: 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: 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.