Description: The life insurance industry is facing a rapidly changing landscape, with several emerging risks demanding the attention of actuaries and industry professionals. While actuaries commonly examine historical experience to guide our estimates of future outcomes, we are often faced with evidence that some of the forces that dictated past performance no longer persist and in some instances may even turn into contrary forces. This session aims to explore the key challenges and opportunities presented by risks with limited historical context, and discuss strategies to effectively manage them. We will delve into critical emerging risk areas, such as interest rate and asset risk, the rise of AI, climate change, predictive analytics in underwriting, and more. The session will feature speakers who are experts in managing risk, as well as results from interviews with top corporate risk professionals, such as CROs and ERM leads from major insurance and reinsurance companies. Attendees will gain valuable insights into emerging risks and practical strategies to navigate the evolving life insurance landscape.

Description: Reunite with members and non-members in the China actuarial community to discuss recent topics and related issues in areas such as macroeconomic sharing, IFRS 17, product strategy, cross-border communication and discussion, and more. Advance your career through learning and networking opportunities, and share your experiences in the finance and insurance industries, as well as other fields related to actuarial science.

Description: Attend this webcast to learn more on how to navigate managing ESG principles in the new normal of a post-COVID world. Enhance your ESG insights in a post-pandemic landscape.

Description: The insurance industry hinges on accurate actuarial assumptions. This session explores how insurers can leverage AI/machine learning (ML) to unlock greater accuracy and granularity in assumption setting, while effectively managing risks, implementing strong governance frameworks, and meeting validation expectations from regulators.

Description: Learn how modernization of data, models, and technology are influencing workflows. This webcast will address how workflows are changing in response to new regulatory requirements and the impacts the changes have on modeling and end-to-end future state operating models. Industry leading actuaries will discuss: How team collaboration can increase quality and speed of reporting to internal and external stakeholders How to strengthen controls and compliance frameworks across finance and actuarial departments How to modernize actuarial input/output for strategic analysis and insights How to attract and retain top talent by improving opportunities for actuaries There will also be dedicated time to ask questions of those who have made the transition.

Description: An introductory article describing how Bayesian statistics has evolved over the past 20 years and why it offers the actuarial discipline powerful tools to better understand and manage risk. Compared with traditional (frequentist) and black-box (machine learning) approaches, Bayesian analysis provides explainable models focused on distributions of estimates instad of simply point estimates.

Description: Dive into an overview of potential customer value propositions, trends in consumer acceptance and adoption, solving the data portability issue, potential impacts of emerging partner ships with OEMs and more within the auto insurance industry during our latest webcast. Experience interactive dialogue that will allow another dimension of thinking as industry experts provide insights and expertise. Throughout this webcast, you can expect developments in the personal lines automotive telematics-based insurance landscape that will develop a further understanding of the present and future of this industry.

Description: Analyze the market volatility behavior in context with historical extreme events during this webcast. There will be a focus on understanding volatility clustering and relationships, both contemporary and temporal, in addition to key economic and capital market variables. You'll also learn from a comprehensive attribution analysis, which uses economic data, asset data, retail investor data and event data as explanatory variables. Dive into investment-related Reddit comments used to capture features of retail investors and tree-based models, including Random Forests and Gradient Boosting Machines to justify changes in volatility modeling. Expect this webcast to end with an example of liability-driven investment optimization that illustrates the potential impact of increased volatility assumption, how to adopt stochastic volatility models, how to reflect volatility clustering and return jumps.

Description: An introductory article describing how Bayesian statistics has evolved over the past 20 years and why it offers the actuarial discipline powerful tools to better understand and manage risk. Compared with traditional (frequentist) and black-box (machine learning) approaches, Bayesian analysis provides explainable models focused on distributions of estimates instad of simply point estimates.