Description: This article discusses the Modeling Actuarial Standard of Practice. Intent and questions related to a model across all practice areas is discussed

Description: This article introduces the reader to probabilistic methods used to model interactions in the financial market place. Three topics are specifically discussed, two concern modeling methods and the other concerns data analysis methods.

Description: This article discusses scenario generators to price or value insurance products.

Description: In this session, we will delve into the application of survey data analytics to extract valuable insights from public survey datasets. The discussion will start with the basics of survey design, sampling practices, and estimation of key statistics. Three case studies will be presented: one in the realm of population health and another in medical economics, both demonstrating the use of either SAS or R to perform the survey analytics.

Description: The National Association of Insurance Commissioners (NAIC) project for the transition to a new real-world economic scenario generator (ESG) prescribed for principle-based reserve and capital calculations selected a vendor in the fall of 2020. The American Academy of Actuaries and other interested parties have been assisting the NAIC in this transition. Over the past four years, extensive analysis, model validation, and comparison work has been done. There have been many updates on the project but few opportunities to obtain an in-depth understanding of the new features of the new ESG and its impact on reserves and capital. Practitioners are very interested in the questions: - How different are the scenarios from the Academy/Society of Actuaries ESG and the new NAIC ESG? - How different are the models and techniques used to create the scenarios in both generators? - Is there a significant difference in capital and reserve requirements for model office tests between the two ESGs? - Are there concerns with the new NAIC ESG? The session addresses each of these questions in detail. At the conclusion of the session attendees will appreciate the key differences between the model structure and scenario performance of the two ESGs and will understand the differences in capital and reserve requirements for model office tests across the two ESGs. Attendees will also learn how the initial yield curve fitting differs between the models, about fund mapping considerations, and the interpretability of parameters in the new NAIC model.

Description: The SOA Research Institute has been publishing its annual Getzen Model of Long-Run Medical Cost Trends for the past decade. The Health Care Cost Trends Strategic Research Committee is looking for a researcher to build on the legacy of the Getzen model and provide annual updates to the SOA’s long-term health care trend projections.

Description: Come discuss modeling costs and its governance, as well as introduce some platforms for more granular asset modeling.

Description: Model design is an important part of being an actuary, and the SOA highlights the importance of the actuarial control cycle of model design, evaluation and monitoring. What tools are at our disposal for these steps? When should we be concerned or excited about model performance? Can we, and should we, use machine learning tools to assist with model improvement or development?

Description: Analyze the market volatility behavior in context with historical extreme events during this webcast. There will be a focus on understanding volatility clustering and relationships, both contemporary and temporal, in addition to key economic and capital market variables. You'll also learn from a comprehensive attribution analysis, which uses economic data, asset data, retail investor data and event data as explanatory variables. Dive into investment-related Reddit comments used to capture features of retail investors and tree-based models, including Random Forests and Gradient Boosting Machines to justify changes in volatility modeling. Expect this webcast to end with an example of liability-driven investment optimization that illustrates the potential impact of increased volatility assumption, how to adopt stochastic volatility models, how to reflect volatility clustering and return jumps.