Description: This article sees enterprise risk management as a specific application of knowledge in Order to control deviations from strategic objectives, shareholders’ values and stakeholders’ relationships. Before and after a modeling process it is necessary to find insights into how the application of knowledge management processes can improve the understanding of risk and the implementation of enterprise risk management. The article presents a proposed methodology to contribute to providing a guide for developing risk modeling knowledge and a reduction of knowledge silos, in order to improve the quality and quantity of solutions related to risk inquiries across the organization.

Description: This research report is on the incorporation of flood and other catastrophe model results into pricing and underwriting.

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

Description: Review of The Improbability Principle: Why Coincidences, Miracles, and Rare Events Happen Every Day, by prominent statistician David J. Hand.

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: 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.