Description: Mortality improvement assumptions have typically been focused on all-cause mortality improvement. However, as evidenced in recent years, the cause of death has a large impact on mortality improvement. Join our panel of experts as they discuss mortality improvement through the lens of different causes of death. This discussion will include a review of the historical cause specific mortality improvement trends seen in the U.S. population and how this relates to the insured population. The panelists will then discuss how to use these trends to inform future mortality improvement assumptions. Key learning objectives include: - Identifying the trends in mortality improvement for select causes of death - Understanding how to adapt these for an insured population - Applying these learnings to mortality improvement assumptions

Description: Session 5A: Creating Continuity in Behavioral Health for Medicaid & ACA Beneficiaries

Description: Session 10A: In Lieu of Services - Addressing Health-Related Social Needs in Medicaid

Description: Actuaries too often get pulled into data management activities, taking them away from the analysis work they enjoy and were hired to do. Without a good relationship between actuaries and IT, this can persist even at companies that have been through finance transformations. Gain insight as we focus on the important relationship between actuaries and IT, and how they can work together on best practices for extracting, managing and using the policy data needed for actuarial models. Join our discussion on a variety of data governance and control practices used when extracting policy data from administration systems. Actuaries and IT must work together to create clear data dictionaries that fully document all steps taken as policy data runs through the exchange, transform and load (ETL) processes prior to being used in actuarial models.

Description: June and July 2023 were the warmest months on record, both on land and ocean, and July was likely the hottest month since global record keeping started in 1850. This session will delve into emerging data showing the impact of climate change on healthcare. Through data, attendees will learn about the direct impact of climate change on the risk of disease burden of populations, and the indirect impact on access to healthcare.

Description: Stay current with the latest mortality trends and practices in this wide-ranging session, covering research performed by the SOA and their collaborators, including recent research from the SOA's Individual Life Experience Committee. Topics will include mortality improvement, mortality by socioeconomic category, cause of death analysis, and insurance company life experience quintile analysis.

Description: Come check out opportunities and challenges associated with using predictive analytics for LTC. You'll learn how carriers are using predictive analytics to support a range of business functions, including: - Pricing - accelerate underwriting without jeopardizing the risk profile for your new products - Experience studies - enhance your understanding of block performance and capture more granular experience trends in your assumptions with less subjectivity - In-force management - facilitate insight-driven interactions that improve policyholder satisfaction and optimize operational costs Expect panelists to discuss the infrastructure required to deploy predictive analytics and the ways in which predictive analytics can help drive key outcomes, including, improved market competitiveness, enhanced understanding, improved efficiency, and better policyholder outcomes. They will also talk about key hurdles to adoption, and considerations for overcoming these hurdles.

Description: Virtual - Session 4D: Actuaries Transforming Healthcare - Improving Childbirth Outcomes and Population Health

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: 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.