Description: Recent and potential near future extreme experiences are leading us to a new normal rather than back to the prior environment. Learn more about several examples of past Regime Changes along with a detailed discussion of how recent disruptions lead toward a persistent change in the business, social and political environment. Come develop an understanding of these drivers of change as well as the likely impacts on the Financial Services industry.

Description: Actuarial projects have become more intricate, involving external resources and requests beyond the Actuarial team’s purview. Whether a task appears minor or exceptionally complex, adept project and change management play a pivotal role in successful execution. In this session, we explore practical strategies for tailoring implementation management approaches. Our presenters draw from both corporate and consulting experiences, sharing valuable lessons. Additionally, they highlight effective collaboration with the Project Management Office, model developers, and model users

Description: The editors preview the articles in the February 2010 issue of Health Watch.

Description: Come discuss the benefits and challenges of collaboration between academic institutions and industry stakeholders in shaping actuarial education and research agendas. Register for this event now.

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: In this article, the interviewers will discuss the pros and cons of centralized modeling function vs a business unit based modeling function. Their experience will help us in identifying factors in contributing to a successful centralized model function and understand how to set expectation with modeling stakeholders.