Description: In September 2017, the China Insurance Regulatory Commission (CIRC) issued the “C-ROSS Phase II project construction plan”, planned to be used across three years’ time, by perfecting the regulations, improving the operational mechanism and strengthening the regulatory cooperation.

Description: Leveraging advanced technologies like Generative AI is essential for insurers looking to compete and drive business growth. In this high-level webcast, we discuss the transformative potential of Generative AI in actuarial work and how it empowers actuaries to become more efficient and effective.

Description: CMS has required hospitals to publish their chargemaster rates, cash discounts and negotiated rates as of January 1, 2021. Our panel is comprised of a data strategist, an executive from a large hospital system and an actuary.

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: Since the international financial crisis, accounting standards have become an important part of financial supervision rules, which objectively requires us to study the reform and development of accounting standards from the perspective of financial stability. Since the release of IFRS17 in 2017, the research and discussion of IFRS17 in the insurance industry has focused on technical details and implementation, while the economic impact and pros and cons analysis of IFRS17 are few. As a result, the industry's understanding of IFRS17 is not comprehensive enough, and there are still many disputes, and no industry consensus has been formed. In the future, the insurance industry should strengthen the research and analysis of IFRS17 from the perspective of financial stability, deepen the understanding and cognition of IFRS17, and improve the ability to control and use IFRS17.

Description: As the insurance industry continues to evolve due to a pandemic and rapidly changing economic and regulatory conditions, the role of data scientists and actuaries has become increasingly important. In an interview with the Atidot data science and actuarial team, we discuss what it is like to work at an InsurTech, how data scientists and actuaries can collaborate, and look at some concrete technical examples of where the innovation is having an impact. This article explores what collaboration can look like and how the role of data science and actuary may evolve in the InsurTech industry in the future.

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: Actuaries today have unique opportunities to analyze, understand and use data for strategic decision making. With nearly unlimited computing power and a broad spectrum of data sources at our fingertips, we are enabled to support our actuarial judgment with comprehensive findings backed up by deep data analyses.

Description: 1) Vision of the applications and development of data analytics in life insurance industry in Asia; 2) How big is the pie and what are the current obstacles insurers are facing; 3) Utilization of data and analytics target operating models as a competitive advantage, with focus on future readiness, controls, efficiency and effectiveness; 4) Potential roadmap ahead: a) Assessment of the current state of maturity levels of data and analytics initiatives; b) Identification of technology and competency gaps; c) Establishment of data integration and management process within BUs, alignment of advanced analytics tools across departments, with centralized data governance, technology structure and decision making framework; d) Definition of criteria and metrics to measure key outcomes and business value added. 5) Case study on agency behavior management: a practical example on how data analytics is being used in practice: (a) key business objectives: improve agent behaviors and maximize productivity with the lowest possible compensation costs; (b) data collection and current state assessment; (c) detailed predictive modeling process, modeling outcomes, and business implications; (d) implementation and monitoring process.

Description: Learn more about the predictive modeling that was applied to industry data for the lapse and mortality experience in the post-level term period. Novel approaches were taken to model the shock lapse, subsequent lapse, and mortality experience. Observe a demonstration of the Tableau tool, which was built to allow users to analyze data on their own.