为金融机构生成式AI风险控制提供可落地的框架。
Governing Generative AI Across Financial Institutions: A Framework for Generative AI Risk Control
- 按五大能力模式分类金融场景应用，梳理实用路径。
- 覆盖投研、风控、客服等10余类金融业务，支持多任务协同。
- 适合银行、资管等机构制定AI落地策略，兼顾效率与合规。
生成式人工智能正从通用实验转向银行业、资本市场、保险、支付和财富管理等领域的专业化应用。其价值不仅限于对话界面，还可整合大规模文档、从非结构化数据中提取信息、生成代码、创建情景叙事、辅助研究流程并协调多步骤任务。这些能力在金融领域尤为关键，因决策常需结合量化数据与合同、政策、申报文件、新闻、客户沟通及专家判断。本文从应用角度出发，归纳生成式AI在金融中的五类核心能力：知识融合、内容生成、分析辅助、交互支持与工作流编排，并映射至主要金融职能。代表性应用包括投资研究、客户服务、贷款支持、欺诈调查、财务报告、运营自动化、软件开发及个性化理财建议。文章还探讨了检索增强生成、工具调用助手、多模态模型与代理型工作流等常见技术架构，识别影响业务价值的实际因素。最终形成的全景图，为研究者与从业者理解生成式AI在金融服务中的最大操作与分析影响提供了基础。
原文摘要 · Abstract (English)
Generative artificial intelligence is moving from general-purpose experimentation toward specialized applications across banking, capital markets, insurance, payments, and wealth management. Its main contribution is not limited to conversational interfaces. Modern generative systems can synthesize large document collections, extract information from unstructured data, generate software and analytical code, create scenario narratives, support research workflows, and coordinate multi-step tasks. These capabilities make generative AI especially relevant to finance, where decisions often depend on combining quantitative data with contracts, policies,filings, news, customer communications, and expert judgment. This paper presents an application-oriented view of generative AI in finance. It organizes potential uses around five capability patterns, including knowledge synthesis, content generation, analytical assistance, interaction, and workflow orchestration, and maps them to major financia functions. Representative applications include investment research, customer service, lending support, fraud investigation, financial reporting, operations automation, software development, and personalized financial guidance. The paper also discusses common technical architectures, such as retrieval-augmented generation, tool-using assistants, multimodal models, and agentic workflows, and identifies practical factors that shape business value. The resulting landscape provides a foundation for researchers and practitioners seeking to understand where generative AI may produce the greatest operational and analytical impact in financial services
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