Description: This session will include examples of healthcare claims and market data to be used for educational purposes to highlight trends in the insurance industry. It will also highlight opportunities to enforce existing laws around fraud and abuse, and areas for regulators and legislators to focus to address this growing problem. Finally, it will discuss recent law changes and highlight areas requiring additional regulatory intervention. Expect discussions of recent claim issues and laws in the healthcare market as well as emerging issues in healthcare. The presentation will be beneficial to healthcare professionals working for health insurers, healthcare providers, auditors, regulators and healthcare providers and will also cover professionalism related topics for actuaries.

Description: This Q&A article discusses the use of machine learning to analyze unstructured data. Supriya Ramachandra, a data scientist at SIKKA, explains how her team uses natural language processing techniques, technical aspects of neural networks, LSTM modeling, and natural language processing. Machine learning allows for the classification of large amounts of unorganized data, providing actionable information for actuaries.

Description: Actuarial projects have become more intricate, involving external resources and requests beyond the Actuarial team’s purview. Whether a task appears minor or exceptionally complex, adept project and change management play a pivotal role in successful execution. In this session, we explore practical strategies for tailoring implementation management approaches. Our presenters draw from both corporate and consulting experiences, sharing valuable lessons. Additionally, they highlight effective collaboration with the Project Management Office, model developers, and model users

Description: "The following video Dave Friesen provides a comprehensive overview of the Artificial Intelligence and Data Science learning track. It covers the role of AI in actuarial work, ethical considerations, and the challenges and limitations of AI. The document also discusses the importance of cloud ecosystems, AI tools, and automation trends, emphasizing the evolution of platforms like Python and low-code solutions. Additionally, it highlights the criticality of lifecycle management, governance, and data foundations in AI applications.

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 report discusses the use of federated learning in the insurance industry and explores the benefits and challenges of this approach.

Description: Bias Awareness in Model Governance

Description: To remain relevant, insurers need to understand and adapt to changing consumer behaviors. There is no doubt about it, millennials have different expectations around searching, buying and servicing, and insurance is no exception! Come hear from four different insurance startups who are designing solutions for this future generation of insurance buyers, followed by a panel discussion with the opportunity to ask questions. You'll learn and perhaps even be inspired to propose solutions in your own company. You may even start calling yourself a Millenial Maven too!

Description: This article summarizes five key lessons we have learned in implementing LDTI and shares our insights on practical ways to address them. This is not an exhaustive list of implementation challenges, but companies that can successfully tackle the issues we describe should have a smoother data and systems implementation transition.

Description: This primer paper seeks to introduce and update the actuarial profession to the topic of insurance policies and genetics and study the impacts on insurance and potential responses.