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: This publication presents an exploratory framework that applies volatility signals—specifically the VIX and MOVE indices—to asset allocation decisions. Rather than prescribing a definitive model, the work highlights a potential pathway for linking equity and bond market risk expectations to portfolio construction. The analysis demonstrates how relative volatility between asset classes can inform dynamic weighting, opening the door to more adaptive strategies in shifting market environments. Importantly, this is not intended as a finished methodology, but as a foundation on which more experienced professionals can build. Readers with advanced backgrounds in finance, risk management, or quantitative analysis are encouraged to refine, stress-test, and extend the framework to suit their own investment contexts. By positioning the concept as a starting point rather than a conclusion, the publication invites collaboration and deeper exploration into how volatility-based metrics may enhance long-term allocation models.

Description: Virtual - Session 8D: An Application of Clustering Approaches in Value-Based Care

Description: In this session, we will discuss best practices in ALM and Strategic Asset Allocation (SAA) modeling approaches to account for the complex relationships between insurance liabilities and the portfolios backing them. We will highlight how robust modeling techniques not only result in optimal asset decisions from a long-term value perspective but can also result in better capital efficiency. At the conclusion of this session, attendees will be able to identify the key aspects of a successful ALM and SAA framework for liabilities subject to PBR.

Description: A comprehensive webcast designed to bring together industry practitioners to discuss the current state of Model Risk Management (MRM) frameworks. This event will delve into the advancements in MRM, the challenges faced by professionals in the field, and emerging trends such as machine learning model validation that are set to shape the future of MRM. Engage with experts, share knowledge, and explore innovative solutions to enhance the effectiveness of your MRM framework.

Description: "Can you answer the question 'what are the chances we will lose more than $1 million next year?' without saying 'it depends'? If you miss a projection, can you quantify the chances that the miss was due to random variation vs. an incorrect projection? ten 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, apply total risk analysis to many actuarial functions."

Description: In this presentation, actuaries and data scientists from Cleveland Clinic and Milliman discuss the development of a sepsis prediction model using machine learning. Presented with a unique challenge of handling massive amounts of data for near real-time risk scoring, the team took their model development to the cloud and delivered a product in less than a week through a unique virtual engagement.

Description: In several parts of the world, LDTI which had substantial changes to U.S. GAAP and also IFRS-17 have been implemented in the last two years. There have been learnings and also challenges for companies subject to these new financial reporting standards. These include enhancements to modeling, improvements in end-to-end processes, and development of robust management reporting and analysis frameworks.

Description: This study presents a comprehensive approach of real option analysis to climate adaptation policies aiming to mitigate flood risk. The framework focuses on pricing catastrophic risk in coastal regions, allowing for economic growth and uncertainty in the mean sea level rise.

Description: Open source programming languages have become a common part of the actuarial toolkit over the years. Using tools like R and Python, highly technical actuaries can automate onerous processes, easily work with massive amounts of data, create models and generate reports. Although these tools excel at turbo-charging analysts, it can be difficult for benefits to inure to the rest of the organization given the learning curve (“Can’t you just put this in Excel?”), control risks (“So your entire process could break overnight if some random person in Nebraska changes something?”), and internal IT environments. Listen in on a panel discussion where presenters share their personal stories of working with open source in a production setting. Hear about real world business problems, how open source tools were used, what risks were present, how they were overcome and the final business result.