智能金融新纪元：大模型重塑行业生态的深度观察

Group 1: Core Insights - The financial industry is undergoing an unprecedented intelligent transformation in 2025, marking a critical turning point for the practical application of financial large models [1] - Four core elements—regulatory policy improvement, significant reduction in GPU computing costs, continuous enhancement of foundational model performance, and a richer application ecosystem—are redefining the industry landscape [1] - The focus of industry discussions has shifted from "whether to adopt" to "how to implement faster and better" [1] Group 2: Evaluation Framework - A new evaluation framework is being explored to accurately assess the performance of intelligent systems in real business scenarios, moving beyond traditional superficial testing methods [2] - This new assessment system extracts key elements from the daily operations of financial institutions, transforming specific business pain points into systematic testing projects [2][4] - The advanced evaluation benchmarks typically include tens of thousands to hundreds of thousands of test samples, reflecting the complexity and diversity of information processing in financial work [3] Group 3: Application in Investment Advisory and Research - Large model technology is profoundly changing traditional work patterns in investment advisory and research, significantly improving service quality and decision-making accuracy [5] - Leading fintech platforms are supporting millions of daily active users through a robust "tools + services + compliance" capability architecture [5] - Smart investment advisory platforms utilize a multi-channel deployment strategy, allowing financial institutions to reach users effectively across various platforms [6] Group 4: Smart Financial Terminals - The emergence of smart financial terminals signifies a fundamental transformation in research workflows, integrating financial large models with professional data services [6][7] - These systems inherit comprehensive coverage capabilities for various financial data and ensure rapid verification and retrieval of data indicators [7] - The "data-logical-outcome" three-stage transition model enhances research efficiency and ensures that every conclusion is traceable and substantiated [7]