Description: The dental and dental insurance industries continue to experience significant innovation advancements in a variety of areas including claims, customer acquisition, teletechnology applications, AI for dental disease progression and more. These advancements are occurring at the dental office as well as within dental insurance companies. Hear about some of the latest innovations in the dental and dental insurance spaces andl gain an understanding as to how cutting-edge developments will impact dental actuarial.

Description: Learn about the latest developments in hospital and payer price transparency data. Hear experiences working with the data and attempts to gain meaningful insights for actuarial and other purposes. Discover challenges and workarounds associated with this data, including file size, comparability of prices, duplication of prices, aggregating provider NPIs into meaningful groupings, and missing payer information.

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: 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: 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: 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: Are you searching for an overview of the SOA Research Institute's Mortality & Longevity Strategic Research Program? Join us for an introduction into studies focusing on the historical U.S. population and insured mortality trends. Two research projects are observed in this webcast, one examining the impact of COVID-19 on mental illness and the impact of mental illness on U.S. Mortality & Longevity. The second study looks into the Group Life COVID-19 Mortality Survey and the high level view of U.S. group term life insurance mortality results during the pandemic. Come explore the research goals and the future for the Mortality & Longevity Strategic Program.

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: 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.