Description: A primer on extreme event modeling and mitigation for practitioners, it covers the complete cycle of extreme event risk management, from identifying experience data sources, to extreme event detection, to modeling and dependence analysis, and finally to monitoring and mitigation.

Description: This paper explores areas where fuzzy logic models may be applied to improve risk assessment and risk decision-making. It discusses the methodology, framework and process of using fuzzy logic systems for risk management. With the help of practical examples, it is hoped that it will encourage wise application of fuzzy logic models to risk modeling.

Description: The Society of Actuaries announces the release of a new report that will provide a resource for the insurance industry to learn about image recognition technology. Authored by Kailan Shang, the report explores the current status of image recognition technology, models for image recognition, and various possible applications of image recognition to insurance.

Description: This research report studies the social media response to three extreme tornado outbreaks: May 22, 2011 in Joplin, MO, November 17, 2013 in Washington, IL and April 9, 2015 in Rochelle, IL. It uses Twitter data with a 14‐day period for each tornado event with a focus on a 24‐hour period surrounding the event. It starts with an overview of the data followed by analysis of geolocation, insurance related user influence, social network, and sentiment.

Description: This is the R programs used to develop the examples for the research report “Extreme Events for Insurers: Correlation, Models, and Mitigation by Kailan Shang and Marc Vincelli