香蜜湖智能金融发展报告发布

Core Insights - The second "Shenzhen Xiangmi Lake Financial Annual Conference" was held with a focus on promoting a virtuous cycle of technology, industry, and finance in the Guangdong-Hong Kong-Macao Greater Bay Area [1] - The "Xiangmi Lake Intelligent Financial Development Report (2025)" was released, emphasizing the need for enhanced financial support for artificial intelligence (AI) technology and industry development [1][2] Group 1: Financial Support for AI - Financial systems have increased support for AI technology and industry, providing comprehensive financial services throughout the lifecycle, achieving significant results [1] - There are unique challenges in financing AI compared to other technological innovations, including difficulties in technology maturation and stability, which impact traditional valuation systems and investment decisions [1][2] Group 2: Recommendations for Financial Support System - A financial support system tailored for AI development is needed, including specialized service models, customized investment and financing plans, and a layered capital operation system [2] - Recommendations include enhancing the adaptability of financial products, increasing early-stage investments, and improving risk management capabilities and post-investment service quality [2] Group 3: Data Governance in the Intelligent Era - The rapid evolution of large models is reshaping the data ecosystem, presenting new opportunities for traditional data governance challenges [3] - Financial institutions are exploring various practices from business scenarios to data innovation, establishing a tiered path for data governance that releases new value and drives efficiency [3] - Key challenges in data governance include technology adaptation, ownership definition, privacy protection, ethical issues, and governance cost pressures [3] Group 4: Challenges for Small Financial Institutions - Small financial institutions are experiencing disparities in the application of large financial models, with issues such as unclear strategic planning, limited resource investment, weak data foundations, and insufficient ecosystem collaboration [3]