Description: In this article, we will describe EY’s five-pillar approach that balances practicality with comprehensiveness, and how this applies to LTC model validation.

Description: Actuaries are accustomed to considering how much conservatism is appropriate in their estimates, whether made for pricing or for mea- surement of liabilities. In the proposals for a new standard on accounting for insurance contracts, this conservatism is referred to by the International Accounting Standards Board IASB and the Financial Accounting Standard Board FASB of the United States as the risk adjustment.

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

Description: View the complete issue for the August 2014 Reinsurance News newsletter.

Description: The author describes the 29th. edition of the International Summer School, held in Lausanne, Switzerland, organized by the Swiss Association of Actuaries.

Description: Description of an ERM workshop delivered in Nigeria by Actuaries Without Borders in collaboration with the College of Insurance and Financial Management.

Description: This article presents recent perspectives on the understanding and measurement of pandemic risk.

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: Written from a regulatory perspective, the article covers the purpose of regulation and some regulatory expectations. The topics include the appointed actuary's involvement in corporate governance, reasonability of results when compared to prior results, data quality, familiarity with the company's annual statement and applicable statutes, and areas where appointed actuaries can add value though services provided to smaller life insurance companies.

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.