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: 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: 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: 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.

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: 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: Volatile interest rates, repositioned investment portfolios, and stress in the banking sector are driving insurers to manage market risk and counterparty credit risk. Our experts in investments, derivatives, and hedge accounting will discuss this new market environment and its impact on hedging. The conversation will include practical case studies, including hedging future bond purchases, hedging floating-rate CLOs, and counterparty credit risk management.

Description: Model design is an important part of being an actuary, and the SOA highlights the importance of the actuarial control cycle of model design, evaluation and monitoring. What tools are at our disposal for these steps? When should we be concerned or excited about model performance? Can we, and should we, use machine learning tools to assist with model improvement or development?

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.