华为赵蕊：金融AI成功90%取决于工程能力 战略目标需从“可用”转向“好用”

Core Insights - The core theme of the China Wealth Management 50 Forum 2025 Annual Meeting is "Towards the Construction of a Financial Power during the 14th Five-Year Plan" [1] AI Application in Finance - AI applications in the financial industry are transitioning from the "usable" stage to the "useful" stage, with 90% of success depending on engineering capabilities [3][8] - The development of large models is entering the "Agentic" era, where AI will autonomously complete tasks and create business value [3][8] - AI will lead to structural changes in financial institutions, reshaping competitive barriers in five key areas: 1. Redefining traffic entry points from passive app clicks to proactive intent recognition through intelligent services 2. Redefining financial products and services for deep customization 3. Restructuring the entire user journey to make financial services more inclusive 4. Redefining operational objects and forms, with intelligent assistants becoming the main channel influencing customer mindset 5. Ultimately affecting talent and organization, moving towards a "human-machine coexistence" state [3][8] Huawei's AI Strategy - Huawei's financial AI strategy aims to support the industry in moving from "usable" to "useful," providing a full-stack capability from advanced Ascend computing power to a one-stop AI development platform (ModelArts) and an intelligent agent development and operation platform (Versatile Agent) [3][8] - The strategy includes talent training courses and focuses on three typical scenarios co-created with leading financial institutions [3][8] Specific Use Cases - In mobile banking app scenarios, Huawei uses models like Pangu 7B to enhance service accuracy to over 95% while optimizing computing power and reducing costs, achieving end-to-end latency under 2 seconds [4][9] - In intelligent risk control scenarios, the core solution involves converting expert experience into "thinking chain" data and using large models with "slow thinking" capabilities for reinforcement learning, ensuring real-time updates and high accuracy of risk control models [4][9] - For report generation (applicable to credit and investment research), an innovative "Deep Research" development paradigm allows intelligent agents to automatically organize tasks and generate high-quality reports through repeated interactions with external data sources and knowledge bases [4][10] Engineering Challenges and Recommendations - The financial industry, characterized by strong regulation and high standards, faces challenges in engineering rather than merely applying generic models or external knowledge bases [5][10] - To address systemic latency, accuracy, humanization, and cost issues, strong dynamic business orchestration capabilities are required, along with complex model tuning, intelligent agent tuning, system integration, and full-link monitoring [5][10] - Eight recommendations for financial institutions include: 1. AI should be a company-level strategy led by top management 2. Business departments must deeply participate in building integrated teams of technology, business, and data 3. Focus on "useful" applications rather than "showcase" applications, paying attention to actual usage metrics 4. Adopt diversified models and open architectures 5. Combine engineering experience from professional fields 6. Build enterprise-level AI pipelines and regulatory-compliant governance systems 7. Develop high-quality datasets 8. Recognize that 90% of success depends on engineering capabilities [6][10]