Description: This is presentations slides summarizing the many concepts presented in the research paper. “When Black Swans Aren’t: Holistically Training Management to Better Recognize, Assess, and Respond to Extreme Events”, by Guntram Fritz Albin Werther and Thomas Herget.

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: The paper argues that Risks Evaluation at its core is a psychological process that can lead to the special type of chaos described in modern Chaos Theory. It will argue that quantification and scientific modeling does not exempt risk assessment from the potential for deep biases. Presented at 2010 Enterprise Risk Management Symposium, Society of Actuaries, April 12-15, 2010.

Description: Essays advocating for incentive compensation linked to the level of risk in an organization.

Description: The article provides an update on the 2019 ERM Symposium with regard to date, location and content (General Sessions and Concurrent Sessions).

Description: This abstract describes a paper that studies Gerber-Shiu functions and dividend payments in an insurance risk model driven by a spectrally negative Levy process of bounded variation.

Description: This research report that provides a literature survey of methodologies applying machine learning to insurance claim modeling.

Description: The mismatch between how interest rate impacts the value of assets and liabilities could have material negative impact to companies’ earnings and surplus. To quantify and mitigate the impact of such mismatches, companies have used matching a combination of duration and convexity. I will review these measurement techniques and their limitations.

Description: The article provides a high-level, non-technical, introduction to artificial neural networks (ANNs) and deep learning with an application to risk management. The method relies on typical ANN components such as nodes, activation functions, and optimization through gradient descent.

Description: Wavelet models are used to analyze risk by time and frequency and provide richer information than time series models.