Description: Modeling Approaches and Challenges focuses on approaches to real world simulation and the challenges and considerations in real world simulation. Topics include: A frequency domain approach to build realistic multi-horizon scenarios. (Ortec approach) Cascade models structure linking models for major asset classes and economic variables. (Moody’s approach) Overview of models, calibrations & approaches within the context of different use cases. Deterministic & stochastic approaches. Importance of model re-calibration flexibility. We will also discuss specific insights into considerations and challenges in real world simulation, which include topics like: Setting capital market assumptions and/or calibration targets. Assumptions about correlation and causality. Calibrating the model to capital markets assumptions. Validating the model against capital market assumptions. Role of history, market data and stylized facts. Importance of back testing. Calibrating the model to multiple investment horizons. Inclusion of alterative asset classes such as private equity, infrastructure, and direct real estate. Associated issues of limited data, illiquidity and greater uncertainty about calibration targets.

Description: A brief introduction to a new SOA research on liability-driven investment benchmark model.

Description: On October 4–5, 2025, actuarial educators and researchers gathered in New Orleans for the Actuarial Mathematics and Actuarial Education Special Session at the American Mathematical Society (AMS) Fall Southeastern Sectional Meeting. The event brought together seventeen speakers from universities, professional societies, and industry to discuss the future of actuarial education, practical applications of actuarial methods, and advances in actuarial modeling. The session featured rich discussions on artificial intelligence in actuarial education, experiential learning through case studies, competency-based assessments, and industry-integrated research courses. Applications-focused talks addressed mortality shocks from COVID-19, disaster aid modeling, queuing systems, health and longevity metrics, healthcare reserving, flood risk in real estate, and value-based insurance contracts. The final segment highlighted advanced actuarial models, including optimal insurance with incentives, variable annuity hedging, extreme risk inference, AutoML for insurance, and privacy-preserving data collaboration. Overall, the session emphasized collaboration, innovation, and adaptability as essential for advancing actuarial science in an era of rapid technological and societal change.

Description: There is value in a framework that allows a smooth transition from a qualitative assessment to a quantitative analysis as entities mature in their understanding of climate risk. In this report, a case study on climate change impact on home prices is used to illustrate a Bayesian network iterative approach to building a model for scenario analysis.

Description: This session will examine bias from a social science perspective. It will discuss the three types of systemic biases using the National Institute of Standards and Technology (NIST) model. The NIST model details the three types as historical bias, societal bias, and institutionalbias. The session will also provide several examples of each type to provide a framework for identifying the social structure of bias.

Description: Article describing content and key takeaways from experience completing the SOA PA Certificate

Description: The Society of Actuaries (SOA) is pleased to present historical U.S. population mortality rates by sex and single year of age for calendar years 2000-2021.

Description: Come dive into the Introduction to Modeling Summer Boot Camp series, which will provide an overview of modeling topics and techniques aimed at the beginner modeler. This interactive Zoom series will be comprised of five 90-minute sessions (August 24, 25, 30, 31 and September 1), with opportunities to discuss the topics in small break-out groups.

Description: Introduces the concept of total risk analysis, a methodology used to answer questions like "what are the chances we will lose more than $1 million"

Description: Session 1D: Meeting the Mark - Predicting MA Stars Outcomes with Monte Carlo Simulations