Description: Join us for an in-depth discussion of the Society of Actuaries’ Quarterly Global Warming Report, which tracks accelerating global temperature trends and their implications for risk modeling. We’ll also highlight findings from the Actuarial Weather Extremes 2025 research series, which examines catastrophic floods, rising sea levels, and wildfire risks—events that are reshaping exposure and challenging traditional assumptions. This session will explore how these developments affect actuarial practice, pricing, and capital planning, and why actuaries are uniquely positioned to lead in managing climate-driven uncertainty.

Description: Guidance and a model for actuaries to use when performing their professional services related to Medicaid (Title XIX) and Children’s Health Insurance Program (CHIP or Title XXI) managed care capitation rate setting.

Description: The Society of Actuaries (SOA) is pleased to present historical U.S. population mortality rates by gender and single year of age for calendar years 2000-2020. To develop these rates, the SOA relied upon data furnished by the Centers for Disease Control and Prevention, Centers for Medicare and Medicaid Services, the U.S. Census Bureau, and the Social Security Administration. A document summarizing key observations and the process used to develop the rates, along with a table of the rates themselves, can be found below.

Description: Every patient leaving the hospital has personal life-context circumstances that impact their chances of successful recovery and complications can be costly. Complex care management impacts over 50 percent of health care spend, with the admitted commercial population driving 37 percent of spend or $177M per 100,000 lives and the Medicare population driving 53 percent of spend or $600 million per 100,000 lives. The stakes are high, but existing care management solutions are mostly clinical and navigation call centers provide fragmented and outdated patient management systems, and do not address individual life-context. Discharged patients therefore remain underengaged and undercompliant, leading to costly complications. Innovative technology can help enable and integrate systems and existing infrastructure to leverage evidence-based contextualized care that will enhance care management, improve efficiency, and drive a better experience and outcomes for patients. This technology helps codifies life context, dynamically stratifying and engaging members digitally and telephonically helping virtual care teams to be more effective and efficient. In a recent randomized clinical trial, this technology and approach has shown a 50 percent reduction in the total cost of care, cutting the average and total cost of readmission, increasing virtual care team impact and member engagement and trust.

Description: Amid macroeconomic uncertainty, proactive lapse management is becoming a growing strategic focus for Life and Annuity carriers. Interest rates have been increasing rapidly during the last eighteen months and insurers, especially annuity writers, have started to see higher lapses and withdrawals. After a prolonged low interest period, many insurers will have to (re)build capabilities to enable proactive lapse management.

Description: Walk through real-life examples of how artificial intelligence (AI) can be used to drive insights and aide actuaries in improving the management of their company's inforce book of business. Hear a panel of experts go step-by-step through case studies, showing how practitioners leveraged predictive analytics for solutions to tap further into their data and draw improved insights.Leave the session with knowledge of how to partner advanced machine learning techniques with the actuarial skills and insights to better understand policyholders' behavior to more efficiently enable accelerating revenue and further profit realization.

Description: Artificial intelligence (AI) models have become increasingly popular in various industries, including finance. Such models can help lenders assess the risk of loan applicants, identify potential defaults and make better decisions about granting credit. However, the complexity of many machine-learning and, particularly, deep-learning models makes it difficult to understand how they arrive at their decisions. This lack of interpretability can be a significant problem in the legal and ethical realms, as it hinders transparency and accountability. Join us to learn interpretability techniques that are essential to address the challenges of using complex AI models in credit risk modeling. These techniques aim to provide a better understanding of how an AI model arrived at a particular decision or prediction. Techniques include surrogate models, which aim to provide a simpler model that approximates the behavior of the original model, and decision trees, which show the decision-making process in a more transparent way. However, applying interpretability techniques to AI models can be a challenging task. For example, some techniques can reduce the performance of the model, which can be unacceptable in applications where high accuracy is critical. Additionally, some techniques may only be effective for certain types of models or datasets, which limits their applicability. Despite these challenges, interpretability techniques are becoming increasingly important in the field of credit risk modeling. Financial regulators have recognized the importance of interpretability, and are starting to require financial institutions to use models that can be explained and validated. In addition, interpretability techniques can be used to improve the performance of credit risk models. By understanding how a model makes decisions, it is possible to identify areas where the model can be improved or where additional data may be needed.

Description: The SOA Research Institute’s Mortality and Longevity Strategic Research Program is pleased to make available a research report that quantifies differences in mortality and disease prevalence by health status. Additionally, period life tables by health status, sex, and age are available in Appendix D.

Description: This study quantifies the range of intercompany variability in mortality. Mean actual-to-expected (A/E) ratios based on amount were calculated in each quintile for every combination of six key variables.

Description: This report is a study. It discusses the objectives, scope, methodology, and limitations of the study, focusing on how diagnosis affects disability income claim recovery and mortality rates.