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

Description: The article urges actuaries to see AI not just as a tool but as a strategic partner, drawing parallels to past transformative technologies like the printing press. Using NAIC survey data, it shows AI adoption is surging in health and auto insurance, lagging in life. It outlines AI’s potential to boost productivity, improve data quality, and enhance contract analysis, while warning actuaries to master AI before others use it to evaluate their work. Ethical standards demand transparency and fairness, making responsible AI use part of professional duty. Historical examples—like James C.H. Anderson’s 1970s cash flow testing breakthroughs—highlight how visionary actuaries can redefine the profession. The article applies Harvard professor Jeff Bussgang’s “hair-on-fire” innovation lens, suggesting AI-powered, urgency-driven insurance products, akin to Y2K coverage, could revolutionize offerings. It calls on actuaries to address protection gaps, discrimination, and industry opacity through AI-enabled fairness. The message: courage, creativity, and proactive adoption will let actuaries shape—not just survive—the AI-driven future. The closing reflects on the author’s tenure with the SOA Reinsurance Newsletter, framing this as a final, forward-looking call to action for actuaries ready to lead in a transformed insurance landscape.

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.