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

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: 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.</