Description: Read how the world’s largest provider of specialty insurance for classic vehicles uses its media department’s YouTube videos to its full advantage.

Description: An introductory article for the December 2023 issue of the General Insurance Insights e-newsletter.

Description: Updated Auto Loss Costs for 2019.

Description: March 2017 issue of Reinsurance News

Description: The document is a Request for Proposals (RFP) for a research study on offshore life and annuity jurisdictions' reinsurance landscapes, commissioned by the Society of Actuaries Reinsurance Section. It outlines the background, purpose, research objectives, proposal requirements, selection process, submission details, and other related information.

Description: The Society of Actuaries (SOA) Research Institute commissioned a study to understand how insurers are responding to the framework used for climate disclosure practices, attempting to identify current practice and best and emerging practice. Used by firms to ensure they manage climate risk appropriately, the framework from the Task Force on Climate-related Financial Disclosures (TCFD) focuses on disclosure of governance, risk management, strategy, and metrics.

Description: Description of general tax consequences of purchasing an insurance company, either through buying the assets or buying the stock.

Description: The personal auto insurance industry is constantly changing. There are small and steady changes over time (e.g., inflation affecting the cost of repairs or general progress in the safety of vehicles) and sharp spikes (e.g., disasters and legislation). That change has never been more apparent with the Covid-19 pandemic, civil unrest, and driverless cars all presently having a significant impact on personal auto insurance.

Description: Reserving and ratemaking calculations are vital to how insurance companies perform financially. Current-day practices for predicting future loss ratio or pure premiums for reserving and ratemaking rates are not based on low prediction errors—mean squared error (MSE) criterion. This report provides mathematically optimal weights that lead to minimum MSE prediction errors, and the formulas are validated using retro-testing with real-world datasets. In this report, two main theorems are discussed, each addressing prediction of future loss ratio or pure premium and improving upon current practices for how rates and reserves are calculated.