人工智能如何重塑再保险行业？

Core Viewpoint - The introduction of the AI intelligent agent "Rui Shu" by Taiping Reinsurance is a significant step towards enhancing the competitiveness of the reinsurance industry and better serving the real economy, aligning with national strategies on AI application in finance [1][2][4]. Group 1: AI Integration in Reinsurance - The development of "Rui Shu" aims to address industry challenges by integrating AI models with professional expertise, achieving over 90% accuracy in generating high-quality reinsurance contracts [2][3]. - AI is seen as a strategic necessity for the company, enhancing both internal efficiency and external service innovation, which are core competitive advantages in financial services [2][3]. - The company emphasizes a targeted approach to AI integration, focusing on specific business scenarios rather than a broad application, ensuring that AI enhances professional capabilities [4][5]. Group 2: Challenges in AI Adoption - The reinsurance industry faces common challenges in AI integration, including the complexity of professional knowledge, compliance and risk management, and the need for talent development [5][6]. - There is a recognition that AI's understanding of complex, non-standardized "soft knowledge" in reinsurance cannot be achieved overnight, highlighting the need for a gradual approach [5][6]. - The company is committed to building an open and collaborative AI ecosystem within the industry, focusing on knowledge co-creation and addressing regulatory challenges [6][7]. Group 3: Future Directions - The company plans to deepen its "AI+" strategy, aiming to enhance service levels, operational efficiency, innovation capabilities, and risk management through intelligent transformation [4][6]. - The integration of AI into traditional tools, such as the global natural disaster risk analysis platform, demonstrates the potential for significant performance improvements and user experience enhancements [4][6]. - The company acknowledges the importance of regulatory guidance and industry consensus in navigating the complexities of AI implementation in the financial sector [6][7].