Description: This report predicts farm-level crop yield distributions in the absence of farm-level yield data.

Description: On June 17, 2019, the Internal Revenue Service published Final Regulations implementing changes to loss reserve discounting under section 846 of the Internal Revenue Code made by the Tax Cuts and Jobs Act. This note addresses changes made in the Final Regulations, particularly modification of rules for determining the interest rate used for discounting, in response to comments from taxpayers on the proposed regulations.

Description: Read the July 2018 issue of Reinsurance News published by the Reinsurance Section.

Description: Review the findings of an expert panel discussing current Generative AI issues impacting the insurance industry.

Description: Updated Auto Loss Costs for 2019.

Description: March 2017 issue of Reinsurance News

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.