Description: We're introducing a new “cutting edge” wealth accumulation predictive framework, the predictive equity analytics (PEA) for life products (annuity, insurance, group employer benefits, retirement plan). PEA enables life insurers and policyholders to avoid market downturns and capture returns from equities (stocks, ETFs and mutual funds) predicted to increase in next one to three trade days via fund prediction advisory by insurers, additionally by individual policyholders. The capability does not exist in insurance markets nor in financial intermediary markets.

Description: Reference Based Pricing (RBP) efforts have increased in popularity as tools to reduce the cost of care. Now with the ability to use CMS health plan transparency information, organizations can estimate the impact of savings from the use of RBP. This session will share the results of an analysis of this in the form of a case study.

Description: In this video Jing Lang explores the current and future landscape of AI in the insurance industry, focusing on its impact on actuarial work. She highlights the wide-ranging applications of AI in business, particularly in customer interactions and marketing activities, while also discussing its potential disruptions in the insurance sector. Despite the advancements in AI, Jing emphasizes that certain aspects of actuarial work, such as pricing and reserving, remain largely unaffected for now, but encourages proactive integration of AI tools for future adaptability. Contributors: JIng Lang FSA, FCIA, MAAA, FLMI ; Scott Allen FSA, MAAA; Henry Chen FSA, FCIA, MAAA; Jon Forster ASA, MAAA.

Description: As data continues to become more robust and readily available, there has been a push in the actuarial field to transition away from Excel to SQL and Python native cloud-based platforms. While these platforms are widely used and powerful, they present their own challenges for the actuarial industry, requiring processes to be production-ready from the onset, technical expertise for integration, and robust requirements documentation. Low-code tools provide a unique alternative solution, which works well when the organizations operating model allows for actuarial and IT to work together, giving actuaries the flexibility they need while maintaining a level of control followed by the organization. Make use of demonstrations to showcase low-code tools (such as Alteryx and Dataiku) and their use case in the actuarial space. Learn how these low barrier-to-entry tools can be leveraged to create robust and controlled processes. Discuss how low-code tools can integrate with coding languages such as Python as well as efficiently prepare data for downstream visualization platforms such as Tableau and PowerBI.

Description: The recent global pandemic, coupled with a rising interest rate environment, has emphasized the need for life insurers to be well-prepared for challenges on multiple fronts. Join industry experts in actuarial science and asset-liability management (ALM) as they discuss the significance of incorporating comprehensive pandemic modeling, mortality modeling, lapse modeling, liquidity analysis, and ALM strategies into the risk management framework of life insurance companies. Through case studies and interactive discussions, learn how to utilize systems and APIs from disparate risk models to integrate into an overall analysis to assess the short and long term impacts of the confluence of risk factors on your business, identify vulnerabilities, and implement proactive risk management strategies to enhance resilience and maintain long-term profitability.

Description: Regression to the mean is a phenomenon that arises when comparing sequential data points. It states that when an initial observation is extreme or an outlier, future observations will be closer to the average. Health care actuaries need to understand and be able to mitigate the effects of regression to the mean to avoid misapplying predictive models or misstating measurements of historical effects.

Description: As actuaries in health care, we spend most of our time focused on reviewing claims, enrollment and clinical data. That information is captured while patients are in health care facilities. Yet most people spend only a small fraction of their time in these facilities. This article explores what types of data are available to actuaries the provide a more holistic view of the member so that a members physical and emotional needs, the social determinants of health can be taken into consideration along with their clinical needs. Two uses cases are presented so actuaries can see how these types of analyses can be leveraged to improve clinical, operational, and financial performance of risk bearing health entities. Understanding the customer (member) not just the patient enables actuaries to provide new insights and values to our stakeholders across a wide range of business and risk applications.

Description: Traditional risk scoring models include only fixed (non-random) demographic and condition-based variables. By including the provider effect as part of the risk scoring model as a random effect, we can get better estimates of provider performance (incorporating practice patterns conditional on patient morbidity profile). This approach can be generalized to study other factors that are now masked in error variance (i.e. area, Social Determinants) but if modeled could give us better estimates of risk

Description: In this session, attendees will understand the role of AI in risk management and underwriting. You will see insightful case studies highlighting AI-driven techniques for enhancing precision and efficiency in these areas. Additionally, the session will cover advanced AI applications in claim management and fraud detection, showcasing cutting-edge strategies to mitigate risks and streamline operations.

Description: The use of data to assess risk is not new to the life insurance industry, but has historically been limited to leveraging structured data. Join us as we discuss the next frontier: unstructured data.