全球金融系统为何需要可靠的高质量数据？

Core Viewpoint - The article emphasizes the critical importance of high-quality, trustworthy data in the financial industry, especially as AI technologies reshape the landscape and increase demand for data [1][2][4]. Group 1: Data Integrity and Management - The financial industry is witnessing a surge in demand for reliable data, with an annual growth rate of approximately 40% since 2019 [1]. - Ensuring data integrity and relevance is essential for large language models (LLMs), as poor data can lead to unreliable AI outcomes, including hallucinations and biases [2][4]. - The implementation of data transparency, security, and integrity is crucial for compliance and building customer trust, exemplified by the use of "watermark" technology in financial data [5][6]. Group 2: Digital Rights Management - Digital rights management is increasingly important, with clients expecting clear definitions of data source availability, AI policy responsibilities, and effective measures against intellectual property infringement [4][6]. - The establishment of a robust framework for digital rights management is necessary to ensure compliance and responsible AI usage [9]. Group 3: Regulatory Framework and Collaboration - The article highlights the need for standardized definitions of "data trust" across the industry to enhance data quality and facilitate its circulation [6][10]. - Collaboration across the industry and the establishment of interoperable regulatory frameworks are key to fostering high-trust AI systems and promoting innovation in the global financial sector [2][10]. - Regulatory initiatives, such as the EU's AI Act and the NIST's AI Risk Management Framework, provide guidance for safe and fair AI practices in the financial services sector [7][11]. Group 4: Future Outlook - The importance of high-quality data will continue to grow as the industry develops AI tools, necessitating a deep understanding from policymakers [8][11]. - The article calls for a collective effort from financial and tech companies, regulators, and users to navigate the challenges posed by evolving AI technologies [11].