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: 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 industry best practices including challenges faced by retirees, such as needing exposure to growth, most exposed to sequencing risk, and the need for smooth returns to avoid poor investor behavior caused by risk aversion. Other topics will include how to set investment objectives which consider the members’ real-world needs and how to build portfolios and implement risk management strategies to meet those objectives. The speaker will provide real-life examples/case studies.

Description: Mr. Zhao stressed that the CBRC adhered to the national principles of risk-oriented, problem-oriented, and openness-oriented, and remained fully mobilized and united to the benefit of the insurance industry. The CBRC overcame the difficulties of the epidemic and pushed forward the C-ROSS Phase II project.

Description: These products effectively provide policyholders access to an equity index with an option pass-through. This new direction blurs the line between more traditional variable annuity and fixed indexed annuity products. This session will discuss these and other similar product innovations, including some interesting regulatory, accounting, and risk management considerations.

Description: The session will talk about Management expectation and key development areas of Big Data, overview of big data application in the life insurance industry.