统一金融操作系统通过分布式微服务、云平台、DevOps平台、统一标准平台等技术平台构建了一套精 益敏捷研发体系。目前已发布33项技术标准，完成了81个标准组件和1000余个能力项的建设，为科技研 发注入新活力，实现"让研发更简单"。 通过能力矩阵、业技融合共创平台，统一金融操作系统构建了一套价值驱动创新体系。目前已为总分行 190多个创新项目提供了"一站式"产品孵化支撑，已实现150余个系统以及700余项能力的上架，完成了 业务资产沉淀，实现"让业技融合更优秀"。 数字化浪潮奔涌之下，金融科技的竞争日趋激烈，数字化转型的焦点也从工具化的"赋能"升级为战略性 的"驱动"，引领银行业将人工智能技术作为驱动业务创新与服务的重要引擎。 北京银行于2022年启动数字化转型"三年行动"，明确了以数字化转型统领发展模式、业务结构、客户结 构、营运能力和管理方式的"五大转型"的战略方针。经过三年时间，北京银行数字化转型从1.0阶段进 入到2.0阶段，即全面数字化经营阶段，成效达到同业先进水平。 "在这三年时间，北京银行每年打赢数字化转型'三大战役'，推动20个科技重大项目，完成了以统一数 据底座、统一金融操作系统和统一风控平 ...

Core Viewpoint - The Global Finance Forum held in Beijing focuses on the theme "Towards a Digital Intelligence Era: China and the Global Economy," highlighting the importance of digital transformation in the financial sector [1]. Group 1: Digital Transformation at Beijing Bank - Beijing Bank has undergone a three-year digital transformation, establishing a unified financial operating system and a unified data foundation to address issues like system silos and data isolation, which are crucial for AI development [3]. - The unified financial operating system enables trusted data sharing and resource allocation, facilitating rapid business application deployment and overcoming communication barriers between systems [3]. - The unified data foundation supports enterprise-level data applications and ensures end-to-end lifecycle management, addressing problems of data centralization and uniformity necessary for AI development [3]. Group 2: AI Development and Resource Management - Beijing Bank has centralized its AI computing resources through a corporate-level AI platform, enhancing operational efficiency and data analysis capabilities while reducing overall costs of intelligent computing hardware [4]. - The bank emphasizes the importance of managing AI technology risks, including issues related to model hallucinations and knowledge base quality, and suggests measures like manual verification and quality checks for uploaded data [5]. Group 3: Compliance and Ethical Considerations in AI - The bank stresses the need for a strong awareness of data security and compliance with laws and regulations as AI applications involve vast amounts of data [5]. - It advocates for a balanced approach to AI development that prioritizes legal compliance while fostering innovation, ensuring that AI applications are fair, transparent, and do not produce discriminatory or illegal outputs [6].