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: The recent wave of innovation in GenAI has unlocked an entirely new category of AI applications. Whereas AI has traditionally focused on automation and predictions, LLM’s and Generative AI give us the opportunity to create AI applications that act as copilots. This emphasis on contelligence and empowering knowledge workers has already begun to change the way work is done. High expertise knowledge workers such as actuaries will be able to use this wave of AI Apps to make better decisions and dedicate more of their attention to the parts of the role that require domain expertise and judgement. Join Charles Morris, Microsoft’s Chief Data Scientist for Financial Services, as he talks about the latest advancements in this space and the opportunities it creates for expert knowledge workers. Charles Morris is the Chief Data Scientist for Financial Services at Microsoft. Charles has deep expertise in the financial services domain and works with many of the world’s leading asset managers, banks, insurance companies, and financial platforms to develop and execute their AI strategies.Currently, Charles is focused on applying Generative AI to financial services and advising many top financial institutions in building their own copilots and reimagining their businesses.Charles is a data scientist and machine learning engineer by profession and an expert in MLOps. Before joining Microsoft, Charles practiced data science, machine learning, and cloud engineering in the asset management industry. He has been a featured keynote speaker at several prestigious events and forums and is a highly sought after public speaker in the domain of AI and Innovation.

Description: In this session, attendees will understand the role of AI in risk management and underwriting. You will see insightful case studies highlighting AI-driven techniques for enhancing precision and efficiency in these areas. Additionally, the session will cover advanced AI applications in claim management and fraud detection, showcasing cutting-edge strategies to mitigate risks and streamline operations.

Description: In this session, participate in a mock negotiation of a typical value based contract taking on the role of either provider or payer and solving for the various negotiation challenges, objections, risk mitigations, data requirements, etc. from either party. Attendees will learn best practices and ideal tools and workflows to engage in when supporting VBC negotiations and gain insights into the typical challenges to equitable and agreeable terms.

Description: Insurers are now in a better position than ever to both understand and enhance their customers' health and well-being. A key avenue for this is through developing health and wellness offerings for clients—for mutual benefit. Around the world, the proliferation of lifestyle and health data continues to gather pace.

Description: Blockchain and cryptography have seen significant adoption around the world. While still in early stages, it has started to make an impact in finance, gaming, and metaverses industries. Organizations have even began to provide insurance services on blockchain. Although insurance is limited to protection against financial losses in markets, the sheer adoption has sparked interest for other insurance use cases and some startups have already started experimenting in the space.

Description: Session 2– Going Deeper through examples InsurTech (1) identifies a pain point to the industry, (2) brings a technology solution to address the shortcoming with a (3) business model that makes sense for all. Expect an illustration of this three-point structure through a handful of start-ups from various backgrounds (health, life, general operation) dealing with various aspects of the insurance value chain (acquisition, risk management, back-office…).

Description: The landscape of the reinsurance industry is evolving amid profound changes in the insurance sector. Innovation is vital for reinsurers as risks become more complex and insurers transition from a role of "protection" to "prevention." Successful reinsurers are responding to market dynamics, particularly through open innovation – collaboration with startups.

Description: Understanding how to effectively adopt AI is critical to staying competitive. This session will guide you through the five stages of AI adoption, with a deep dive into two key stages: Operational Optimization and Collaborative AI Integration. You will learn how to harness AI to streamline operations and boost efficiency, while also exploring the nuances of integrating AI in decision-making to enhance outcomes through human-AI collaboration.If you are seeking practical insights on maximizing AI's benefits while maintaining human oversight, this session will provide the tools and strategies to help you do just that—ensuring that your AI adoption journey is both effective and aligned with your business goals.

Description: Every year an AI topic goes through a hype cycle. Since 2022 and now in 2023 we continue to hear more about the ChatGPT models and how an everyday person is able to interact with AI. As more startups and applications are riding the hype, what does it all mean for our industry? Is this just a buzz or real? This talk intends to cover at a high level what are large language models, what does it mean for audit or regulatory space, and what might be some pitfalls or tolerance of error when it comes to selecting a right application for such models in insurance use cases.