步入深水区，智能金融迎模型迭代等多重挑战

Core Insights - The development of smart finance is characterized by a dual advancement of technology-driven innovation and deepening application scenarios, supported by policy initiatives and increasing investment in AI [1][3] - The financial industry is transitioning from a "seller's market" to a more competitive landscape, necessitating a strategic shift towards smart finance as a core strategy for managing complex risks and competition [1] - Experts emphasize the importance of integrating AI technology into traditional business processes to realize its full potential, while also addressing the challenges posed by data governance and risk management [4][5] Group 1: Trends and Opportunities - Smart finance is evolving based on two significant trends: the rapid democratization of AI technology and a fundamental shift in the macro landscape of the financial industry [3] - The "15th Five-Year Plan" presents opportunities for the financial sector to accelerate market-oriented, digital, and international transformations, with a focus on smart technology as a critical choice for enhancing resource allocation efficiency [3] - The efficiency revolution in AI, driven by innovations in model training and resource utilization, significantly enhances the "energy efficiency ratio" of AI technologies, providing a solid foundation for the smart finance sector [3] Group 2: Challenges and Risks - Despite the promising outlook, the deepening application of smart finance faces complex challenges, particularly in the integration of technology with traditional business processes [4] - A significant percentage of AI projects in the U.S. fail to deliver substantial returns due to difficulties in effectively integrating AI systems with existing workflows [4] - Emerging risks such as model hallucinations, algorithmic opacity, and information pollution complicate the risk landscape, necessitating a robust approach to risk governance [4][5] Group 3: Data and Technological Hurdles - The depletion of public data for training large models poses a significant challenge, while valuable private data remains underutilized due to inadequate governance and compliance mechanisms [5] - The rapid pace of technological iteration leads to a phenomenon where the market value of AI models depreciates quickly, complicating investment decisions and increasing the difficulty of financial support for new ventures [5] - The need for a well-structured technical and management framework is essential for advancing risk management in smart finance, requiring a balance between speed, efficiency, and professional expertise [7] Group 4: Regulatory and Ethical Considerations - The development of smart finance must align with the principle of "financial for the people," emphasizing the importance of consumer protection and the human element in technology [8] - Regulatory frameworks should keep pace with technological advancements, ensuring a balance between oversight and innovation without overstepping boundaries [6][7] - The ultimate goal is to create a financial ecosystem that is not only intelligent but also warm and inclusive, ensuring that the benefits of smart finance reach a broader audience [8]