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

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

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

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

Description: Recent advances in mortality modeling have enabled applicant-specific quantification of mortality risk in life insurance applicants using laboratory and physical measurements. Relative to more conventional underwriting, this approach identifies a small population of significantly under-priced policy and a much larger, cross-subsidizing group whose risk is presently overestimated. Applicants placed in the highest percentile by this scoring methodology are more than 10X as likely to die in a given period as their demographic peers, and represent approximately 5% of short-term claims. Implications for product design are substantial in both the short- and long-term, as the possibilities for adverse selection grow, and the opportunity for fully individualized pricing raises the more distant prospect of products with as many rate classes as applicants.

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: 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.