Description: In this video, Joe Long ASA, MAAA, a consulting actuary and data scientist at Milliman, explains the concept of the bias-variance trade-off and its connection to actuarial credibility theory. He demonstrates how machine learning techniques like penalized regression can help actuaries balance bias and variance in their projections, using long-term care experience studies as an example. He also discusses the broader applicability and future directions of integrating advanced analytics into actuarial practice. Contributors: Joe Long ASA, MAAA; Jess Calafell, ASA, MAAA; Jon Forster ASA, MAAA

Description: This session will cover the current and potential use of artificial intelligence (AI) in the health care space. Participate in this interactive session and learn how AI is being used today and discuss its potential to reshape and revolutionize health care. You will get an opportunity to learn how AI can influence patient experience in every phase of healthcare delivery and talk about the challenges and limitations that arise with the use of this technology.

Description: Walk through real-life examples of how artificial intelligence can be used to drive insights and aide actuaries in improving the management of their company’s inforce book of business. A panel of experts will walk through case studies of how, as practitioners, predictive analytics provided solutions to tap further into their data and draw improved insights.

Description: This session is an in-depth interview of two actuaries turned Artificial Intelligence (AI) experts. We aim to have a conversation that gleans into each of their journeys and what led them to the path they are now on. We will open up the last ten minutes for audience questions. At the conclusion of this session, attendees will know Eric and Joe much better, have an understanding of what AI/machine learning/data scientist work is like for actuaries, and, for those who are curious to get more involved with AI, some encouragement to pursue their interests.

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: 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: 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: 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.