Description: LDTI’s introduction has forced many insurers to examine their existing operations and technologies. They must determine how to optimize compliance efforts while balancing data, analytics and system modernization considerations to achieve maximum value. This article will examine the four key facets—the data, technology, processes, and people—essential to any successful LDTI exercise. It will also touch on the various approaches for tackling LDTI and how a modernization mindset can help insurers realize much greater transformation for the business.

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: The chairperson reviewed the accomplishments from the prior year and provided a preview of upcoming events.

Description: The insurance industry is in a constant state of transformation. As organizations adapt to the effects of COVID-19, undergo modernization efforts and implement new technologies, soft or “human” skills are becoming key differentiators for success. Skills such as communication, empathy, storytelling, leadership, problem-solving and more are separating the good from the great actuaries. Those who possess these skillsets can better translate data and most efficiently leverage systems and teams. At the same time, by taking a human approach to modernization efforts, leaders will be able to best engage and motivate their workforces, while building teams that will excel in the future of work.

Description: How the speed of change is accelerating and how we could react to this as a profession.

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: 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: Mairi Mallon talks about social media and reinsurance