金融业如何与大模型“共舞”

Core Insights - The financial industry is undergoing a profound transformation driven by large models, which are reshaping roles, functions, and business models within the sector [1][3] - The application of large models in finance is transitioning from a phase focused on technological validation to one that emphasizes commercial value and systematic integration [3][5] - Data is becoming a critical element in the evolution of large models, with the need to address data fragmentation and enhance data trust and governance [5][6][7] Group 1: Development Trends - The application of large models in finance is moving towards enhancing core revenue-generating areas and evolving from efficiency tools to collaborative decision-making partners [3] - The financial industry is actively embracing large models through two main approaches: training general large models with financial data and developing specialized financial models by AI startups [3][4] Group 2: Challenges - The implementation of large models faces three core challenges: high costs, scarcity of professionals who understand both finance and AI, and difficulties in managing organizational culture and processes [4] - The industry must confront the challenge of data governance, as data is currently seen as the largest obstacle in the application of large models [7] Group 3: Data Utilization - Financial institutions are encouraged to activate dormant data, develop synthetic data, and advance data standards to leverage high-value data resources [5][6] - Trust in data is essential, categorized into three levels: trust in data collection and usage, trust in the data itself, and trust in data creators [6]