Description: This session series is sponsored by Zelis Shifting utilization of health services to efficient and high-value providers presents an opportunity to reduce harmful cascades of wasteful spending, ultimately freeing up funds for additional high-value services that will improve quality and member satisfaction. Identifying the right providers to put at the center of networks is challenging for any health care organization. How can you objectively determine which providers are delivering the greatest value when compared to their peers? And once those providers are identified, what’s next? Join Zelis and their partner HealthCorum to review a case study on a shared services organization where they leveraged statewide multi-payer claims data to produce provider level analytics, allowing for transparency into provider practice patterns at a granular level. By attending this session, you will be able to: - Create a fair methodology to benchmark providers. - Develop metrics and scores to identify high-value providers. - Produce actionable data to be used for network optimization strategies.

Description: Bias Awareness in Model Governance

Description: Come gain insights into the techniques used in Machine Learning and its applications. You'll discover the key technological advances in recent years which were behind and ultimately facilitated the breakthroughs including ChatGPT/GenAI. It's time to uncover real-world use cases where AI models and their transformative impact on our lives.

Description: This session on Innovations in Underwriting will explore the latest advancements in underwriting technology and methodologies. It will focus on how these innovations are transforming the insurance industry, making underwriting processes more efficient, accurate, and customer-friendly. Topics will include the integration of artificial intelligence (AI), machine learning, and big data analytics into underwriting workflows. Additionally, we'll discuss emerging trends in risk assessment, automation of underwriting tasks, and the implications of these changes for underwriters and insurance professionals. Attendees will have the opportunity to learn from industry experts and participate in discussions on how these innovations are reshaping traditional underwriting practices. Learning Outcomes: - Gain an understanding of the latest technological advancements impacting underwriting, including AI, machine learning, and data analytics. - Learn how these innovations can streamline underwriting and pricing processes, improve accuracy, and reduce costs. - Explore new risk assessment methods and discover how automation is changing the underwriter's role. - Identify best practices for integrating innovative technologies into existing underwriting and actuarial pricing frameworks.

Description: Who we are as people and who we become over time is entirely determined by the habits that we practice. And, habits are the fabric from which company culture is made. However, habit are hard to change, because we misunderstand what it really takes to change habits. Why? Because human beings don't have habits - habits have human beings. If you're curious about what this means, have an interest in the science of Context Design, and want to learn how to change your own habits, as well as create cultures of high performance, join this session. Learning outcomes include: - Understand the 'golden rules of habit change' and the many myths around habits (e.g., that they take a certain number of days to create). - Learn about the science of Context Design: how to create habits, at scale, over time. For yourself and your team. - See why 'human have habits' misrepresents reality and why it's more accurate to say 'habits have human beings!'

Description: To help familiarize actuaries in industry with statistical methods for imputing or modeling race and ethnicity in the U.S., the Institute’s Diversity, Equity and Inclusion Strategic Research Program is seeking a researcher to outline the various methods, including their strengths and weaknesses. The project intent is a methodology review rather than original thinking. The review should assume that the user will be working with structured datasets in an insurance context, for example, data associated with an in-force life or health insurance product.

Description: A call for proposals for research projects exploring recent insurance product innovations and their impacts on the industry. It outlines the background, purpose, objectives, proposal requirements, selection process, submission details, conditions, and conflict of interest disclosures.