Description: This is an Excel workbook that can be used as a tool for health insurance companies to model their potential risk exposure from an influenza pandemic. It is based on a model developed for a Society of Actuaries study examining the potential implications of a pandemic on the U.S. health insurance industry. The spreadsheet is saved with many of the same assumptions used in this research and reproduces the results of the exhibits contained in the June 2010 report, 'Potential Impact of Pandemic Influenza on the U.S. Health Insurance Industry.'

Description: A priori, one would expect In Home Assessments (IHA) and Telehealth (TH) Encounters to identify some conditions that lead to actions that keep a member or two out of the Emergency Room. Similarly, one would expect Care Management and Disease Management engagement to ultimately influence some improvement in documentation that leads to more accurate Risk Adjustment. The business cases for these activities are often siloed to only the primary activity. The relationship between risk adjustment and care management is often more recognized as competing interests - one generally relying on utilization and the other trying to reduce it. Business cases can change dramatically when profiling has a more complete picture. This session will explore some techniques and share some observations - positive and negative - of the interactions between CoC and Risk Adjustment.

Description: This report covers the latest emerging trends in U.S. population mortality. The SOA relied upon data furnished by the Centers for Disease Control and Prevention (CDC). Observations are based on the CDC’s recent release of 2021 mortality experience, along with prior mortality experience data from 1999 through 2020.

Description: LIMRA, Reinsurance Group of America (RGA), the Society of Actuaries (SOA) Research Institute, and TAI have collaborated on an ongoing effort to analyze the impact of COVID-19 on the individual life insurance industry’s mortality experience and share the emerging results with the insurance industry and the public. This report documents a high-level analysis of the claims that have been reported.

Description: The MACRA/QPP Payer Revenue Model is designed to provide Health Plans an estimate of the expected claim cost increase/decrease due to MACRA.

Description: Can you answer the question, 'What are the chances we will lose more than $1 million?' without saying, 'It depends'? If you miss a projection, can you estimate the chances that the miss was due to random variation versus an incorrect projection? Listen in as presenters demonstrate how to answer these questions and others using total risk analysis. Total risk analysis includes quantifying both the random variation risk and the projection risk associated with actuarial functions like pricing and reserving. After attending this session, you will be able to describe total risk analysis and to apply to many health actuarial functions.

Description: A multi-author collection exploring how catastrophe modeling can be adapted to life and health insurance, particularly in response to the 2025 Los Angeles wildfires.

Description: Watch as presenters highlight the SOA's work on an updated Medicaid underwriting margin model. This research project includes the model along with a set of instructions and a paper describing the key components of underwriting margin, including cost of capital and risk margin. The model was created as a tool for Medicaid actuaries representing both MCO and state principals to determine appropriate underwriting margin components within Medicaid managed care rates. In addition, explore a brief description of the motivation for an underlying theory behind the model. Walk through different model inputs and run through some realistic scenarios that Medicaid actuaries may encounter in states where they operate.

Description: "A crucial importance in forecasting is to be able to distinguish cases when temporary slowdowns in health care utilization are followed by a “rebound” period of higher utilization. Come explore an analysis of a new mathematical model describing the return-of-care process. By means of simulation and case studies using the R programming language, the relationship between the visibility of these effects and the endogenous variance of the utilization time series is explored. After attending this session, you will be able to: Describe the parameters and mathematical form of the model Apply the model to real-world utilization data"

Description: It goes without saying that the analysis of data is a key aspect of actuarial practice, but do traditional actuarial approaches allow us to see the whole picture? Effective data analysis is also key to the identification of health disparities and measuring progress on health equity. Actuaries and others analyzing data for health equity purposes find themselves looking at data differently.