Description: The objective of this report is to provide benchmarking information that will help companies address the needs of internal stakeholders in cost-effectively conducting and implementing an ORSA process. In order to obtain this benchmarking information, a cross-disciplinary survey was conducted to find out what insurance companies understand and expect from an ORSA, the effort required to complete an ORSA, and the benefits from doing so. This survey was undertaken on a global basis, with participants that included life, property and casualty (P&C), often referred to as "non-life" or "general" insurers in territories such as Europe, and health insurance companies in North America (the United States and Canada), Europe, and Asia.

Description: Report on the activities of the Academy’s health practice council and its work groups.

Description: Enterprise risk management ERM is for the measurement and the management of all significant risks of the business holistically irrespective of types and sources. The study offers significant advances in the current debate on the performance of enterprise risk management, in particular, minimizing the existing gap between the academic understanding and practitioners’ experience on the topic.

Description: Methodology for identifying principles based economic capital.

Description: Abstract: An introduction to the Goals-Progress-Strategy (GPS) strategic risk management framework: an original synthesis of scenario based ERM, the Logical Framework Approach, and new metrics and methods. GPS improves strategic planning and execution and enables practical management of the risks a company faces in pursuit of its business goals. The framework provides a portfolio view of a company’s strategic objectives, a return on risk-adjusted capital measure, risk-intelligent decision making, and risk-adjusted performance measurement/compensation.

Description: Measurement of the Volatility Risk of Traditional Actuarial Risks for use in pricing and reserving

Description: ERM has methodology for quantifying the risk to capital, but to determine optimal capital, the impact of capital level on firm value is needed. This gets into the realm of valuation. Also capital allocation provides risk quantification for business units, but comparing return among units is fundamentally a risk-pricing exercise. When considering risk pricing, optimal returns on allocated capital are not necessarily constant across business units. The current ERM methodology, as well as actuarial literature on valuation and pricing, are reviewed, and possible directions for application of pricing and valuation methods to risk management problems are outlined.

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: From a session at the Spring meeting of the Society of Actuaries held in San Antonio, Texas, June 14-15, 2004 Discussion of risk and pricing issues of particular concern with respect to life insurance and annuity products in times of relatively low interest rates

Description: This paper provides an overview of those cognitive biases most often responsible for flawed risk assessments and provides practical techniques to mitigate them. Through a combination of mathematical rigor, case study, metaphor, and logic, the author highlights systematic errors in our thinking about risk and provides ways to avoid them.