Description: As AI becomes more pervasive, we must consider the potential repercussions and risks associated with its widespread use. The SOA seeks essays that delve into the multifaceted risks posed by (AI) over both the short and long term.

Description: This is the first in a series of calls for essays on the impact of AI on mortality and longevity. For this call for essays, we are interested in the impact of AI on mortality and longevity modeling, forecasting and prediction.

Description: This call for essays explores the impact of Artificial Intelligence/Large Language Models on retirement professionals and retirees. Review background information, research objectives submission guidelines, and the timeline.

Description: This document is a call for essays issued by the Society of Actuaries Research Institute. It seeks submissions that explore the impact of Artificial Intelligence/Large Language Models on retirement professionals and retirees. The document provides background information, research objectives, and a list of illustrative topics and questions that the essays can address. The submission guidelines, timeline, and evaluation criteria are also outlined.

Description: This document is a call for essays issued by the Society of Actuaries Research Institute. It seeks submissions that explore the impact of Artificial Intelligence/Large Language Models on retirement professionals and retirees. The document provides background information, research objectives, and a list of illustrative topics and questions that the essays can address. The submission guidelines, timeline, and evaluation criteria are also outlined.

Description: Come explore an in-depth overview of telehealth to set context and navigate the continuing expansion of telehealth and its integration into the healthcare delivery system. You will examine the forces that shape telehealth offerings and expect a view of the possible futures of this evolving modality.

Description: Creating a predictive model is one thing, but then taking that model and getting full stakeholder buy-in is another. Learn from presenters' experiences and insights on proving model effectiveness in the long-term care (LTC), life and annuity spaces. Explore various topics such as feasibility, model transparency, fairness and equity, ROI measurement, data assets and compliance, infrastructure and more. By the end of the session, you will gain an understanding of how to navigate the challenges of proving the effectiveness of predictive modeling and obtaining management buy-in.

Description: The advent of emerging technologies such as blockchain, decentralized AI, and advanced privacy preserving techniques have created the possibility that billions of individuals may be able to maintain sovereignty and control of their own data. As data becomes increasingly valuable as the means of production for a variety of digital goods and services, this self-sovereignty of data may create a new economic framework of individualized data monetization. This session will explore the key factors, technologies and mega-trends in health and medicine aligning to drive this new economy and explore what the future may look like from a data valuation perspective as the current $120 billion secondary market for health data expands to include consented health and health-relevant data from more individuals, to the benefit of all individuals and the healthcare system as a whole.

Description: Learn more about interpretable machine learning techniques for health insurance fraud detection.

Description: Generative artificial intelligence (AI) tools like ChatGPT will redefine the nature of work across industries in coming years, What does this mean for the actuarial profession? Delivering generative AI tools to actuaries capable of leveraging them effectively will dramatically modernize how actuaries work and contribute to organizational growth. In this educational session, explore how generative AI can accelerate actuarial model development. Gain an introduction to generative AI and its implications for actuarial modeling. Learn how it specifically helps actuaries improve accuracy, reduce errors and save time in model development. Then explore use cases, including how generative AI can improve data cleaning, model building, as well as simulate scenarios and test model robustness. Close with a discussion on how to integrate generative AI into actuarial modeling workflows effectively, including common challenges actuaries may face during implementation and tips to overcome them. You will come away with a deeper understanding of how generative AI can accelerate actuarial model development.