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  21世纪经济报道记者李览青上海报道

  2024年被称为“大模型应用元年”,对银行业也不例外。

  “千模大战”告一段落,头部基础模型厂商市场格局初定,应用端垂类模型“百花齐放”。作为数据最为密集、数字化基础最为成熟的行业,金融业成为率先探索大模型等AI技术应用的“排头兵”。

  随着上市银行2024年年度报告的逐步披露,包括6家国有大行与8家股份制银行在内的14家全国性银行去年的AI战略与应用布局已全部对外公开。

  21世纪经济报道记者梳理14家银行的AI战略发现,“AI+”已成为银行近两年发布AI战略的核心。纵观各家机构的“AI+”战略,主要包括三个方面,一是提供基础智能化能力的模型底座,二是强调安全保护与风险管理的配套机制,三是结合不同业务场景的创新应用。

  与过去单点业务的技术应用创新不同,如今的“AI+”已覆盖银行C端用户、B端客户以及对内员工等全业务流程。

  与此同时,记者发现,在技术之外,越来越多的银行注意到人与AI的关系,不仅强调AI与金融场景的融合,更切实关心员工如何真正使用AI技术,相关创新应用的调用率如何,技术真正带来了多少业务价值。

  场景应用“多点开花”

  尽管多家银行在前两年提出自研大模型底座,但从最新披露信息来看,应用的优先级被提到了前面。“自研大模型”的表述已转变为“应用大模型技术”或建设“自主可控的大模型技术底座”。

  “本集团依据企业级应用需求,择优选择千亿级开源通用大模型,并利用长期积累的高质量文本数据对通用大模型进行预训练、微调和强化学习,建成面向所有业务领域应用和统一技术底座的金融大模型,以及模型即服务(MaaS)的应用平台。”建设银行在年报中如是说。

  建设银行首席信息官金磐石在业绩发布会上透露,截至2024年底,该行先后适配了16个版本的通用大模型,进而形成了16个版本的金融大模型。“这种策略保证了随着金融大模型能力的快速迭代,我们业务场景的应用效果能够达到‘水涨船高’的效果。”

  目前来看,银行AI战略的基础大模型底座至少包括两个部分,一是以生成式大模型为基础的“快思考”大模型,另一个部分是以DeepSeek-R1为代表的“慢思考”推理模型,另外还有代码大模型、多模态大模型、智能体等等,分别针对不同场景的差异化需求实现落地应用。

  例如中信银行明确表示,该行升级融合了决策时AI“中信大脑”与生成式AI“仓颉大模型”,由此建成了“自主平台+场景深耕+生态共建”的三位一体AI赋能体系。光大银行也制定了《模型建设发展规划》,布局“决策式模型+生成式模型”综合应用的智能解决方案。

  在14家银行披露的AI应用场景中,已实现落地应用的成熟场景主要是智能编码研发、智能营销、智能客服、智能风控、合规内审、消费者权益保护,以及日常经营管理流程等等。

  值得关注的是,在零售信贷、财富管理等与C端用户交互较多的场景之外,2024年有多家银行在金融市场、交易银行、公司金融等对公业务层面结合大模型与小模型能力,实现综合化的智能应用。

  如招商银行在交易银行业务中深化人工智能技术应用,线上“招小财”AI助手能够准确识别客户意图,协助客户完成复杂公司金融产品操作,响应准确率达到95%,形成立体式的客户需求即时响应服务体系。招行还将大模型技术应用到资本管理领域,打造“智本GPT”,让产品定价管理系统进一步完善线上化全流程管理,推动数智化转型。

  兴业银行也在金融市场业务场景中推出“兴小二”债券交易机器人,依托智能化交易平台,引入机器学习、大模型等先进技术进行系统和策略升级,通过各类算法和因子挖掘提升交易效率。

  值得关注的是,邮储银行在年报中特别单独展示了两个市场交易业务场景下的机器人,展现了大模型与小模型结合在同一业务板块、不同场景、不同用户端的应用潜力。

  一个是货币市场交易机器人“邮小助”,主要是在货币市场询价场景下与外部机构交易员进行人机交互对话,目前覆盖数百家金融机构,接受询价总量超过1.5万亿元,总成交金额超过2000亿元,交易平均耗时较人工节约94%,捕获超额收益率6个基点。“邮小助”的底层模型主要是小模型,可以在简单任务下实现更快的推理速度,参数少、复杂度、硬件资源要求和训练成本低,稳定性较高,当货币市场询价时用小模型抽取出用户意图及查询的实体数据,辅以大模型识别兜底,最终提高识别准确率。

  另一个机器人是交易员助理机器人,主要为该行交易员提供服务,底层模型具备大模型意图理解、语义分析能力,集成多个系统查询功能,实现交易对手及交易标的债权基础信息、行内授信、历史持仓、评级估值等内外部信息的批量查询、对比和展示,一站式解决交易员交易前的查询准备需求,并可以根据用户的点赞、采纳等反馈自动学习,持续自动完善应答准确度。在交易员查询数据时,由大模型识别用户查询意图,生成数据查询代码,再由小模型完成数据加工并展现给用户,目前交易员助理机器人可以将交易前准备工作耗时大幅压缩75%,极大地提高了交易员的交易效率。

  从“AI+金融”到“人+AI”

  在“AI+”战略的推动下,各家银行披露的人工智能、大模型等新技术应用场景数量繁多,甚至“成百上千”。

  如工行企业级千亿金融大模型技术体系“工银智涌”已赋能20余个主要业务领域、200余个场景,累计调用量超10亿次。建设银行金融大模型体系已赋能行内193个应用场景。招商银行全行大模型应用场景超120个。中信银行决策式AI“中信大脑”的落地场景已超过1600个,生成式AI“仓颉大模型”孵化了超过80项创新应用。兴业银行大模型助手应用场景数量超过70个。民生银行全行落地了超过30个生成式AI的典型场景应用。

  摆在银行面前的下一个问题是,如何确保在这些应用场景中,AI能够真正赋能于员工,让每个人都得以分享科技带来的便利。

  越来越多的银行开始关注“人+AI”。

  建设银行在财报中提到,在客户经营层面推出ChatBot交互版“帮得”客户经理全功能AI智能助理,以智能化为关键驱动,打造“人+AI”的新模式。基于ChatBot交互框架,以人工智能“大模型+小模型”架构路径对客户服务进行“全流程”再造升级,经营策略实现从总部数字大脑生产后直达一线,支持员工通过自然语言一键唤起知识、产品、活动等客户服务工具,AI辅助完成一站式产品配置和高效营销服务。此外,还推出一系列智能辅助工具,并打造AI小诸葛智能体。2024年,“帮得”智能助理总交互次数3463万次,为全行3万名对私客户经理服务。

  招商银行也在财报中表示,加快打造“AI+金融”“人+数智化”新模式,促进人与科技相互赋能。在经营管理方面强化智能化工具应用,例如在零售条线打造零售智能助手产品矩阵,批发条线针对客户经理打造CRM智能助手,在人力资源数字化服务领域,基于大模型技术开发“数字美眉”机器人,实现对员工的智能服务,截至报告期末,“数字美眉”用户数达到2.53万人。

  在为员工提供智能化工具的同时,一场全行级别的AI基础设施改造正在进行,前者是让员工“有得用”,后者能让员工“用得好”。

  中国银行副行长蔡钊在业绩发布会上表示,围绕智能化建设发展,该行将“建机制,搭平台、汇数据、调模型、落场景、练队伍”,一方面,强化配套机制建设,推动建立人工智能应用治理架构,推进AI平台完善和模型调优,畅通大模型数据使用流程;另一方面,推进应用落地见效,按照“先内后外”的策略,以需求为牵引,以成效为驱动,优先聚焦知识辅助、内容生成等高价值场景,实现提质增效降本。

  招行则是夯实“云+AI+中台”科技底座,充分释放上云红利,稳步推进自主可控的大模型全体系建设,聚焦打造行业领先的技术中台,不断提升组件规模与质量,提高IT资源复用水平,截至报告期末,累计发布组件5,942个。数据中台持续沉淀企业级的数据能力和数据资产,截至报告期末,大数据服务覆盖全行63%的员工,数据已成为员工经营分析的核心依据。

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