金融行业大模型应用落地白皮书：AI原生开启金融智能新未来

Group 1 - The core transition in algorithm technology is moving from "passive task handling" to "active evolution strategies," particularly in the financial sector, which is data and computation-intensive, presenting a historical opportunity for deep transformation [1] - Major global players are innovating algorithms to address the financial industry's challenges related to "long text, high real-time, and strong professionalism," with advancements such as OpenAI's GPT enhancing "long text causal reasoning" capabilities and Google's Gemini upgrading "multimodal dynamic interaction" algorithms [1] - The financial industry is shifting from merely applying open-source models to building a deep collaborative system of "scenarios-algorithms-data," creating knowledge barriers and deeply integrating industry scenarios to train effective agents for AI-native transformation of core business scenarios [1] Group 2 - The integration depth of "core business and AI" has become a core competitive advantage for financial institutions, with large models excelling in processing unstructured data and understanding intent in employee-facing scenarios [2] - In customer-facing applications, particularly in high-stakes areas like credit, risk control, and marketing, there are challenges such as low accuracy and delayed feedback, which can be addressed by specialized models that adapt to financial compliance and dynamic risk factors [2] - IDC predicts that the future will see a collaboration between general large models and specialized models, with AI solutions that manage and adapt to complex semantic understanding becoming mainstream in the financial sector [2] Group 3 - The development of large model toolchains is transitioning from "technology-driven" to "business-driven," enabling financial institutions to quickly build intelligent agents tailored to their specific business needs through low-code/no-code platforms [3] - Financial institutions are increasingly demanding intelligent agents in core areas such as investment research, credit decision-making, and risk management, which will create more value [3] - The collaborative management of "general models + specialized models" will become mainstream, with the core value of tool platforms being to lower the AI usage threshold for financial institutions, allowing business personnel to solve business problems using AI [3] Group 4 - The transition from "data-driven" to "knowledge-driven" is crucial for the AI-native application in finance, requiring the conversion of scattered data into reusable structured knowledge to meet the industry's high compliance, precision, and dynamism requirements [4] - Financial institutions aim to build a data flywheel by connecting end-to-end data flows, ensuring compliance through sensitive data classification, and integrating cross-modal data for collaborative analysis [4] - The construction of a data flywheel will enhance the breadth of knowledge, depth of reasoning, and robustness of financial intelligent systems, enabling rapid responses to changing business demands [4] Group 5 - The evolution from traditional computing to intelligent computing is essential for significantly improving computing efficiency, especially as large models evolve to trillion-parameter scales, leading to exponential growth in training computing requirements [5] - Efficient computing solutions, such as heterogeneous computing clusters and mixed training, are becoming critical for balancing cost and energy efficiency in response to the demands of ultra-large-scale models [5] - For different parameter scales, precise adaptation of computing solutions is necessary to optimize the match between computing resources and business needs, with specific strategies for billion-parameter models and trillion-parameter models [5]