Description: Explore insights to non-traditional actuarial roles from a panelist with experiences in data science, finance and entertainment. The panelist will be sharing their perspectives on their non-traditional actuarial role in relation to the actuarial profession.

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: The session will provide attendees with a framework of what goals to think about when building and utilizing a predictive modeling framework in New Sales and Renewal Underwriting mid-market, level funding, balance funding, and self-funding group health business.

Description: Generative AI has become a hot topic since ChatGPT became one of the most well-known Machine Learning models ever. There are many different generative models out there, and in this presentation, we explore the possibilities, benefits, reservations, and alternative use-cases of using Generative Adversarial Networks to generate health insurance claims data.

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: "Discuss research conducted at the Cleveland Clinic to understand levels of anxiety and depression across Northeast Ohio. Specifically, learn how advanced analytical techniques were utilized with social media data from Twitter and Google to estimate the need for psychological support by geography and overtime. The panel will go further to demonstrate how natural language processing (NLP) and machine learning could be used to make operational decisions at a leading health care provider."

Description: Data science is being used to create more algorithms used across a variety of applications, including underwriting and pricing. As data science gains traction, it is imperative that we as actuaries consider the ethical implications of use of these algorithms. This session will discuss techniques for evaluating whether your algorithms are inadvertently making biased decisions and considerations for ensuring that your algorithms minimize risk of making biased decisions. Learn to identify potential ethical issues with their algorithms and techniques to address them.

Description: The rapid evolution of life insurance underwriting has created pricing and operational challenges and raised new questions about established approaches to assumption development, both of which demand a fresh perspective. As underwriting methods evolve, we find ourselves navigating uncharted territory. In this session, we delve into accelerated underwriting, focusing on two critical areas. First, we explore assumption setting in this new world, where numerous interaction effects can make it feel like we’re asked to consider everything, from every angle, all at once. Considerations for assumption setting must encompass early performance indicators—what signals inform our assumptions? Additionally, we address novel underwriting approaches and how to adapt assumptions for these paradigms. Furthermore, in the context of post-COVID claims experience, we discuss distinguishing meaningful insights from noise. Second, we examine declinable risks and their impact on mortality. In this new world ushered in by accelerated underwriting, previously excluded risks—low-frequency, high-severity declinable cases—are now part of the landscape. Quantifying their mortality impact challenges us, as historical data lacks systematic tracking. To quantify declinable risks, we draw upon research findings from analyzing 27 million life insurance applicants, providing valuable insights for attendees to navigate this dynamic landscape and make informed assumptions for pricing and risk management.

Description: In this session, we will do a deep dive into the potential of large language models; we will explore how new AI tools can be combined with traditional modeling to automate aspects of risk assessment. Industry experts focused on operationalizing this technology will talk through their approaches, lessons learned and demo use cases. We will also share insights around creating a robust AI strategy based on business objectives and regulatory considerations. Learning Outcomes: • Applications of large language models in risk assessment use cases • Regulatory considerations around the use of generative AI

Description: How are regulation and innovation intertwined? How to regulate artificial intelligence/machine learning in the insurance industry? We will cover how regulators are striking the balance between protection of their constituents and supporting innovation. We will zoom into the case of predictive analytics and the regulatory attempt at avoiding undue discrimination. Let's discuss what this really mean.