Description: An introductory article describing how Bayesian statistics has evolved over the past 20 years and why it offers the actuarial discipline powerful tools to better understand and manage risk. Compared with traditional (frequentist) and black-box (machine learning) approaches, Bayesian analysis provides explainable models focused on distributions of estimates instad of simply point estimates.

Description: A three-minute neuroscience-based image exercise is able to accurately unlock the emotional state of individual employees, permitting prediction of a host of specific needs. Actuaries can use this quantitative information at scale to target resources, benefits, and supports to increase the objective wellness and subjective well-being of workforces.

Description: CMS has required hospitals to publish their chargemaster rates, cash discounts and negotiated rates as of January 1, 2021. Our panel is comprised of a data strategist, an executive from a large hospital system and an actuary.

Description: "Explore a general introduction to the opportunity of applying a relatively newer area in machine learning called uplift modeling, which has been successfully used in retail (Wayfair etc). The concepts are parallel to the intervention programs in health care, where we are interested in measuring the treatment effects and ultimately directing the right members to the most effective programs, driving the highest value for the programs both from a financial and health outcomes perspective. Uplift modeling can also be viewed in context of causal modeling where we are interested in assigning cause to a treatment relative to a member not receiving the treatment. Check out an introduction on the concept, seehow it relates and builds on existing work, and use a demo to showcase the potential of integrating this ML approach to current work."

Description: This session will examine bias from a social science perspective. It will discuss the three types of systemic biases using the National Institute of Standards and Technology (NIST) model. The NIST model details the three types as historical bias, societal bias, and institutionalbias. The session will also provide several examples of each type to provide a framework for identifying the social structure of bias.

Description: An introductory article describing how Bayesian statistics has evolved over the past 20 years and why it offers the actuarial discipline powerful tools to better understand and manage risk. Compared with traditional (frequentist) and black-box (machine learning) approaches, Bayesian analysis provides explainable models focused on distributions of estimates instad of simply point estimates.

Description: Gather with members and non-members in the China actuarial community to discuss recent topics and related issues in areas such as macroeconomic sharing, pension, investment, product strategy, and more.

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: 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: In this presentation, actuaries and data scientists from Cleveland Clinic and Milliman discuss the development of a sepsis prediction model using machine learning. Presented with a unique challenge of handling massive amounts of data for near real-time risk scoring, the team took their model development to the cloud and delivered a product in less than a week through a unique virtual engagement.