Description: In this article, the author talks about the importance of communicating risk correctly.

Description: This article describes risk modeling and how to build a risk intelligence system.

Description: I am pleased to report that the 2008 ERM Symposium Scientific Paper Track continues to push the envelope in terms of both quantity and quality of papers. With 65 abstracts submitted for review--a 40 percent increase over 2007—the level of response again confirms the cross-industry interest in this topic.

Description: Article from Risk Management Newsletter, March 2006, No. 7 on the continuing education activities for 2006 sponsored by the Risk Management Section of the Society of Actuaries.

Description: In this article, the author discusses earthquake modeling.

Description: Book review of Extreme Risk Management: Revolutionary Approaches to Evaluating and Measuring Risk by author Christina Ray.

Description: Article from Risk Management, July 2006, issue 8 that highlights the 4th annual ERM Symposium. The goal of the symposium was to show that ERM principles are the same across different industries and practices, and risk managers need to talk to each other to learn and disseminate the best practices faster.

Description: Listing and description of key articles in August 2011 issue of Risk Management. Topics include Predictive Modeling, Risk management and Solvency II.

Description: From a simple mathematical model of the underlying economics, I first predicted this crisis in July of 2004. An economic dynamic relating very low interest rates to the structure of the demand curve in the housing market made this outcome foreseeable, indeed inevitable. The current crisis had a mathematical cause. Essay on the collapse of the housing bubble.

Description: An introductory article describing how Bayesian statistics has evolved over the past 20 years and why it offers the actuarial discipline powerful tools to better understand and manage risk. Compared with traditional (frequentist) and black-box (machine learning) approaches, Bayesian analysis provides explainable models focused on distributions of estimates instad of simply point estimates.