Description: Walk through real-life examples of how artificial intelligence can be used to drive insights and aide actuaries in improving the management of their company’s inforce book of business. A panel of experts will walk through case studies of how, as practitioners, predictive analytics provided solutions to tap further into their data and draw improved insights.

Description: As data continues to become more robust and readily available, there has been a push in the actuarial field to transition away from Excel to SQL and Python native cloud-based platforms. While these platforms are widely used and powerful, they present their own challenges for the actuarial industry, requiring processes to be production-ready from the onset, technical expertise for integration, and robust requirements documentation. Low-code tools provide a unique alternative solution, which works well when the organizations operating model allows for actuarial and IT to work together, giving actuaries the flexibility they need while maintaining a level of control followed by the organization. Make use of demonstrations to showcase low-code tools (such as Alteryx and Dataiku) and their use case in the actuarial space. Learn how these low barrier-to-entry tools can be leveraged to create robust and controlled processes. Discuss how low-code tools can integrate with coding languages such as Python as well as efficiently prepare data for downstream visualization platforms such as Tableau and PowerBI.

Description: Gather with members and non-members in the China actuarial community to discuss recent topics and related issues in areas such as macroeconomic sharing, pension, investment, product strategy, and more.

Description: How can generative artificial intelligence (AI) be leveraged to transform and modernize life insurance? Gain an overview of AI and some of their current use cases in the industry, including the latest trends in the market. Next, you will learn about the current challenges companies face, including costly underwriting procedures, customer acquisition and retention, and complicated application processes. Finally, explore how generative AI can help address these challenges, improve customer experience and help streamline processes to free up more time for higher-level business insights and other company initiatives.

Description: Focus on Accelerated Underwriting (AUW) in the life insurance industry, which is an expedited underwriting process that utilizes predictive models. Expect to address the challenges in designing the process, from modelling to performance monitoring. Additionally, you'll address risk management, particularly in mortality and proxy discrimination andhighlight the role an actuary can play in the AUW process, as well aswhat they should know to contribute effectively.

Description: "As digitization and automation rapidly progress in today’s life and health industry, issues with misrepresentation during the insurance application process are also becoming a growing concern for insurers around the globe. A recent survey finds that misrepresentation in application forms costs the average insurance customer an estimated 5% to 10% in higher premiums. In order to reduce this risk, insurers must find and correct areas of misrepresentation, consult post-issue sampling, review the automated underwriting processes, and monitor the quality of financial advisory firms. Learn how predictive disclosure modeling detects and reduces misrepresentation by harnessing readily available underwriting data to analyze issues, helping insurers understand various drivers for disclosure rates and areas of misrepresentation.Advantages of this modeling to various stakeholders in the life insurance journey include improved risk selection, claims experience, and reinsurance terms for insurers. For customers, it leads to increased certainty of coverage, payout of the claim, and ultimately lower premiums on average. By the end of the session, attendees will understand: The disclosure modeling process at a high level The technical details of a standard approach to disclosure modeling: data cleaning, standardization, visualization, and predictive modeling Success stories about how predictive disclosure modeling has impacted insurers"

Description: Internal modernization initiatives and external changes of the financial reporting regulations (e.g., LDTI, IFRS 17) increase the amount of validation activities for actuarial models. In addition, the use of predictive analytics in actuarial modeling requires a different set of validation skillsets/techniques in model validation. These internal and external forces call for a structured, streamlined and robust model validation practice.