Description: In this session, we will explore the opportunities and challenges of leveraging AI tools to support valuation actuaries in their work. We will begin by introducing the concept of large language models (LLMs), discussing their capabilities, limitations, and potential errors. We will then delve into case studies that demonstrate how LLMs can be effectively utilized to reference actuarial documents. The session will also address key considerations when developing AI tools to mitigate errors, enhance safety and security, and improve overall efficiency. The case studies will cover fine-tuning and information retrieval techniques such as retrieval augmented generation (RAG). Our goal is to provide attendees with a foundational understanding of the nature of LLMs and offer practical insights and ideas for actuaries to build their own processes and tools that ensure accountability, safety, and efficiency.

Description: With the rapid development of the Insurance market, insurance technology—including online insurance--continues to mature. Zhongan Online invests heavily in fintechand Zhongan Answer, an insurance robo-advisor, launched in 2018, could improve transaction efficiency by using artificial intelligence to communicate in a timely manner and solve the pain point of the perplexed insurance clause. Under a different consumption scenario, Zhongan Answer could reply to users in a timely manner, while also implementing private customized products and services by providing users with personal risk management and insurance assessment solutions so that users could easily and conveniently select the most suitable insurance plan. The intelligent shopping guide system of Zhongan Answer could improve transaction efficiency by analyzing the personal insurance intention and providing the humanized interpretation of insurance clause. Zhongan Answer has served millions of users and supported more than 100,000 insurance-related knowledge points with an accuracy rate over 90%. Zhongan Answer has provided more than four million Q&A services, and its intelligent shopping guide system covers more than 90% of insurance products.

Description: In this session, we will explore the opportunities and challenges of leveraging AI tools to support valuation actuaries in their work. We will begin by introducing the concept of large language models (LLMs), discussing their capabilities, limitations, and potential errors. We will then delve into case studies that demonstrate how LLMs can be effectively utilized to reference actuarial documents.

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 session will examine bias from a social science perspective. It will discuss the three types of systemic biases using the National Institute of Standards and Technology (NIST) model. The NIST model details the three types as historical bias, societal bias, and institutionalbias. The session will also provide several examples of each type to provide a framework for identifying the social structure of bias.

Description: After International Accounting Standards Board (“IASB”) issued International Financial Reporting Standard No. 17 – Insurance Contracts (“IFRS 17”), IASB received much feedback and suggestions for improvement. As a result, in early 2019, IASB made limited changes to IFRS 17. The presentation will briefly discuss the main contents and effects of those changes.

Description: Artificial intelligence is no longer an emerging technology — it is rapidly becoming the core engine of risk selection, pricing, underwriting, and customer interaction across insurance and financial services. But as models evolve into autonomous agentic systems and decision pipelines become more complex, traditional guardrails are being stress-tested like never before.