Description: The Society of Actuaries (SOA) published its first COVID-19 research brief on March 10, 2020, followed by regular updates and expansions through June 12, 2020. The comprehensive report included a section of statistics that highlight the pandemic’s impact on economies in the U.S. and Canada. This report shows updates through July 29, 2020, to selected graphs from the Economic and Asset Impact section of the comprehensive report.

Description: Results of the PBITT Working Group annual Variable Annuity Survey.

Description: This report evaluates the impact and evolution of societal risk management due to projected future changes in frequency, severity, and variety of weather-related catastrophes.

Description: The Spatial Hazards Events and Losses Database for the United States (SHELDUS) provides county-level data for direct economic losses due to natural hazards from 1960 to the present.

Description: Underwriting has continually evolved throughout the history of insurance. Technology advancements over time have opened, and will continue to open, new underwriting possibilities from both process automation and content standpoints.

Description: The Long-Term Care Valuation Work Group of the American Academy of Actuaries and the Society of Actuaries Research Institute have released a report to replace the mortality and lapse bases for statutory minimum reserves. The Work Group defined the base mortality table to be the 2012 Individual Annuity Mortality Table. Development of mortality margins and the lapse assumption were based on the 2000-2011 SOA/LIMRA LTC Voluntary Lapse and Mortality Experience Study.

Description: The Society of Actuaries Catastrophe/Climate Strategic Research Program engaged Risk & Regulatory Consulting LLC (“RRC”, “we”, “our”, or “the researchers”) to conduct research on the projected changes in insurability and affordability of insurance coverages due to climate change.

Description: Actuaries have a long history with stochastic modeling. Nested stochastic modeling is often used when the modeling components under each economic scenario are themselves determined by stochastic scenarios in the future. In this paper three methods have been suggested that can be added on top of existing enhancement methods to improve NSM efficiency.

Description: Machine learning algorithms fit models based on patterns identified in data and can be very complex. In this report, we describe and illustrate a range of methods for interpreting machine learning models from the growing field of Interpretable Machine Learning (IML).

Description: Peer-to-peer (P2P) insurance is one such business model where individuals or economic agents join together and pool their resources for mutual aid. The aim of this project is to showcase the development of a P2P insurance model and analyze it from both technological and product perspectives.