Description: The Society of Actuaries Product Development Section, the Modeling Section, the Reinsurance Section and the Financial Reporting Section announce the release of a new research report that introduces a novel framework for leveraging the "architecture" of an industry mortality table within a company's predictive analytics-based insured mortality analysis.

Description: The Society of Actuaries Product Development Section, the Modeling Section, the Reinsurance Section and the Financial Reporting Section announce the release of a new research report that introduces a novel framework for leveraging the "architecture" of an industry mortality table within a company's predictive analytics-based insured mortality analysis.

Description: The Society of Actuaries Product Development Section, the Modeling Section, the Reinsurance Section and the Financial Reporting Section announce the release of a new research report that introduces a novel framework for leveraging the "architecture" of an industry mortality table within a company's predictive analytics-based insured mortality analysis.

Description: This article examines the impact of a changing smoking rate on population-level mortality improvement and the importance of taking this driver into account.

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: The well-known examples of predictive modeling “successes” are numerous. Perhaps less widely recognized is the myriad of ways in which a predictive model can fail to perform as expected, often due to misconceptions or misrepresentations on the part of the analyst. This article, which is intended for a general actuarial audience, focuses on three such pitfalls. By understanding these three common pitfalls, readers may guard against them within their own work, teams, and organizations.

Description: This research paper is intended to be 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. This paper explores extreme events from the perspective of a global insurance company, with an emphasis on market risk, credit risk, insurance risk, liquidity risk, and business risk. Extreme risk concepts and models are introduced from a practitioner’s perspective without unnecessary theoretical details. This report is not intended to be all-encompassing, but rather to focus on the key aspects of extreme risk analysis and its application. The input data and R programs underlying the numerical examples herein are available in the supporting files accompanying this report

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