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: The rapid economic development of Asia has ushered in profound lifestyle changes, particularly in urban centers. Increased disposable income, urbanization, and the proliferation of convenient food services have contributed to dietary habits associated with elevated health risks. Among these, insulin resistance (IR)—a metabolic condition linked to diabetes, hypertension, and cardiovascular diseases—has emerged as a critical yet underrecognized threat. IR is also strongly associated with an increased risk of dementia, particularly Alzheimer's disease and vascular dementia. For life and health insurers operating in markets such as Greater China and Southeast Asia, traditional underwriting methods face significant challenges in accurately assessing these evolving risks. This article investigates the limitations of conventional approaches and explores the potential of continuous underwriting methodologies to better align risk assessment with dynamic lifestyle factors.

Description: Investigate the importance of collaboration with accounting, underwriting, sales, marketing and other professions as an actuary when you attend this webcast. Cross-functional positions can cause unique challenges to arise, like various experts attempting to balance growth and profitability. Discover an opportunity to learn about relevant ASOPs and dive deeper into contemporary examples that highlight specific professionalism issues that can develop.

Description: President Roy Goldman speech at the SOA 2020 Virtual Annual Meeting & Exhibit.

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: The SOA and Plug and Play relationship will allow InsurTech start-ups to validate their technology and modeling processes with actuaries. The strategic partnership with Plug and Play demonstrates the SOA’s commitment to providing its members with dynamic learning experiences, rewarding volunteer opportunities, and collaborative events where they can learn from the experiences and ideas of peers around the world.