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: 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.

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: This abstract describes a paper that supplements actuarial literature in which probability distributions have been proposed, by adding the log-folded-normal and log-folded-t families.

Description: This paper sets out to demonstrate that the economic capital, risk drivers, and business model are intricately integrated, and that this has broad implications on enterprise risk management for financial institutions. From 2007 Enterprise Risk Management Symposium, M-AS07-1.

Description: Presented at June 1996 Spring Meeting. Inability to quantify the volatility in individual disability income insurance is one of the key reasons for loss experiences in the industry. Discussion covers: volatility measures for reserving, application of reserve adequacy criteria under various plan designs, application of volatility measures to pricing, and a test of the effectiveness of volatility measures.

Description: In this article, the author explains what a CAT model is.

Description: European insurance regulation pretends to capture and rely on the 1-year 1-in-200 VaR. But there cannot exist any scientific calibration of such measure. Why?

Description: The article is devoted to risk management in the business process of materials supply for industrial enterprises. The goals of the business process are considered, and the risks of the failure to achieve goals are identified. Probabilities and influence of negative events are estimated. The risk treatment is considered on critical risks, including forming of insurance supplies of materials. The calculations of insurance supplies of materials, taking into account risks, are conducted on the basis of statistical information.

Description: We demonstrate an integrated enterprise risk management (ERM) solution to optimize the risk portfolio, identify natural hedges, create an optimal risk treatment plan, enhance risk culture, and facilitate risk reporting throughout the organization.