从小微客户信用评估、零售客户精准画像到供应链金融等业务创新，近年来，数据作为生产要素，正 加速融入银行运营各环节，成为拓展营收的重要引擎。 证券时报·券商中国记者观察到，一方面，近年来有关银行数据报送与治理违规的罚单频现；另一方面，越来 越多的银行将数据管理部从信息科技部门独立出来，并抬升至与后者地位相同的一级部门。 证券时报·券商中国记者梳理各家上市银行2024年年报发现，国有大行中的中国银行、建设银行、交通银行、 邮储银行，股份行中的浦发银行、兴业银行、民生银行，城商行中的青岛银行、贵阳银行、苏州银行等，于近 年均单独设置统筹数据治理的一级部门，与信息科技类部门同级并列，多数命名为"数据管理部"。 如建设银行的科技渠道板块，就有多个一级部门，其中包括"数字化建设办公室""数据管理部"和"金融科技 部"等。 当然，并非统筹数据治理工作的部门全然称为"数据管理部"。如交通银行将原金融科技与产品创新委员会、数 据治理（金融统计标准化）委员会整合为数字金融委员会；在部门设置中，也单独设置了"数据管理与应用 部"，与"金融科技部"并列为一级部门。再如，华夏银行成立的是"数据信息部"，光大银行设置的是"数据资产 管理 ...

Group 1: Core Insights - The new Private Economy Promotion Law, effective from May 20, aims to provide strong legal support for the high-quality development of the private economy [1] - The law's implementation raises questions about how the private economy can achieve leapfrog development [1] Group 2: Financial Transparency and Credit System - Insufficient financial transparency is identified as the core reason for the financing difficulties faced by private enterprises [1] - A credit system based on financial transparency is crucial for promoting high-quality development in private enterprises [1] - High-quality private enterprises, especially those focused on technological innovation, benefit from easier access to public financing due to improved financial transparency [1] Group 3: Data Governance and Asset Value - Data governance is essential for private enterprises to unlock asset value and build core competitiveness [2] - A comprehensive data governance analysis and application system can enhance market responsiveness, cost control, customer experience, and product innovation [2] - Three key areas for private enterprises to focus on include establishing a standardized data collection and management system, expanding intelligent application scenarios, and building a flexible technological foundation [2] Group 4: Value Creation and Innovation - The new productive forces are expected to bring paradigm shifts to the private economy, transitioning from survival competition to value creation [2] - An integrated innovation system focusing on data, technology, and talent is necessary at the enterprise level [2] - Policy optimization and resource allocation through cross-industry cooperation and mergers can provide more motivation for long-term research and development [2] Group 5: Internationalization and Compliance - Private enterprises face strategic choices between "local agents" and "self-construction" in building international supply chains, considering logistics costs and management efficiency [3] - Financial design should aim for cost minimization and maximum gross profit, with a focus on meeting expected cost-performance ratios [3] Group 6: IPO Trends and Recommendations - Five recommendations for companies considering an IPO in Hong Kong include choosing the right listing model, optimizing corporate governance, assessing listing conditions, preparing for financial compliance, and understanding market conditions [3] - Companies should align with Hong Kong's listing rules and enhance financial transparency and information disclosure standards [3]

Core Viewpoint - The financial information technology industry in China is experiencing continuous growth, with a projected market size of 72.602 billion yuan in 2024, reflecting a year-on-year increase of 13.85% [1][10]. Industry Definition and Classification - Financial information technology refers to the extensive application of information technology in the financial sector, promoting the digital, networked, and intelligent transformation of financial services, management, and operations [2]. Industry Chain Analysis - The financial information technology industry chain is a technology-driven ecosystem, with upstream focusing on infrastructure and foundational technology, midstream on system development and solutions, and downstream covering financial institutions and regulatory bodies [4]. Development History - The industry has evolved over four decades, transitioning from initial automation to deep intelligence, with key phases including the establishment of electronic systems, the rise of online banking, the integration of technology, and the current focus on self-control and interconnectedness [6]. Current Market Analysis - The financial information technology market in China has been expanding at a double-digit annual growth rate, with significant investments from financial institutions driven by business expansion, innovation, and regulatory requirements [10]. Downstream Application Areas - The banking sector dominates the market with a 48% share, followed by securities and funds at 26%, and insurance at 16%, each focusing on specific technological needs and innovations [11]. Key Enterprises Analysis - The industry features a diverse landscape with leading companies like Hengsheng Electronics and Kingdee, which dominate their respective segments, while others like Softcom and Zhongke Soft focus on niche markets [14][16]. Future Development Trends - AI technology is increasingly empowering the financial sector, enhancing risk management and customer service, while data governance is becoming crucial for maximizing data value and ensuring compliance [20][21][23].

然而，培养"施工队"却并不容易。魏凯直言，许多企业用户往往需要先干活再立项做预算，这导致 了"脏活累活"难解决、"施工队"动力不足等问题。此外，企业的数据治理能力弱也是制约人工智能产业 发展变革的短板。"数据治理被视为'下水道工程'，这方面做不好，即使把模型引入公司，'施工队'也无 处下手。"魏凯说。 对此，魏凯建议，企业应在大模型落地过程中加大预训练阶段投入，同时提升训练集群算力效率、加快 代理型人工智能研发，充分发挥消费场景"练兵场"作用，以数据闭环优化数据供给，推动大模型优化。 此外，应培养更多具备跨界思维的复合型人才。 （文章来源：科技日报） "大模型就像一个软乎乎的大脑，发挥作用必须有脑壳、眼睛、手、胳膊和腿。这背后必须要搭建多套 软件来支撑。完整的软件栈和工具链才能让人工智能真正变成生产力。"在日前举行的清华大学数字经 济系列沙龙第八期上，中国信息通信研究院人工智能研究所所长魏凯说，算法、数据、算力的规模效应 仍在持续放大，但大模型并非"万能钥匙"。引入大模型只是"买图纸"，而要真正落地，需要配备专 业"施工队"来完成大模型开发、调优、评估、部署及推理等全流程工作。 "以宠物经济为例，大模型可以通 ...

中电科普天科技股份有限公司 2025 年 5 月 13 日投资者活动记录表 编号：2025-002 | | √特定对象调研 | | □分析师会议 | | --- | --- | --- | --- | | 投资者关系活动类 | □媒体采访 | | □业绩说明会 | | 别 | □新闻发布会 | | □路演活动 | | | □现场参观 | | □电话会议 | | | □其他： | （请文字说明其他活动内容） | | | 参与单位名称 | 彭陈张资产 | 陈荣新 | | | | 浦健投资 | 邢 军 | | | | 嘉强基金 | 邓海茵 | | | 及人员姓名 | 广金基金 | 冯柏昌 | | | | 汇阳基金 | 蔡诺怡 | | | | 华福证券 | 王二鑫 游世雯 | 祁建龙 | | 时间 | 2025年5月13日 | | | | 地点 | 公司大楼会议室 | | | | 公司接待人员姓名 | 董秘、副总裁：周震； | | | | | 董事会办公室：刘梦雨、邓晓华 | | | | | 一、公司介绍 | | | | 投资者关系活动 | | | 中电科普天科技股份有限公司（简称"普天科技"）是专业从事公网 | | ...

Core Viewpoint - The implementation of "credit instead of proof" aims to enhance public management and service efficiency by replacing multiple compliance certificates with a single credit report, thereby optimizing the business environment and reducing administrative burdens [1][2]. Group 1: Implementation and Benefits - The new policy replaces the requirement for various compliance certificates with a single credit report, which can significantly reduce the time and costs associated with administrative approvals [1]. - The initiative is expected to improve administrative service efficiency and create a more favorable business environment by integrating credit empowerment with the reduction of unnecessary documentation [1]. - The establishment of a cross-province mutual recognition mechanism for credit reports will facilitate a unified national market, breaking down regional barriers and promoting the flow of resources [1]. Group 2: Data Management and Governance - The notification emphasizes the need for enhanced data collection and sharing between local and central governments, as well as horizontal integration among various regulatory bodies, indicating a more robust data governance framework [1]. - A feedback and correction mechanism for credit data will be established, allowing for the display of corrected credit information, which reflects a balanced approach to governance [2]. - The initiative faces challenges such as regional standard discrepancies and data security risks, necessitating improvements in technical safeguards and data management systems [2]. Group 3: Future Directions - There is a call to expand the applicability of "credit instead of proof" to include more social organizations and individuals, as well as to broaden its use in various market transactions like financing and credit [2]. - The goal is to foster a culture of trust and credit usage within society, maximizing the benefits of credit and enhancing market vitality [2].

Financial Data and Key Metrics Changes - Total revenues in Q1 were $93.1 million, up 25% year over year, exceeding guidance [22] - SaaS revenue for Q1 was $68.9 million, representing year-over-year growth of 34% on a constant currency basis, comprising 74% of total revenues [22][23] - Gross profit for Q1 was $69.8 million, with a gross margin of 75%, an improvement from 74.1% in Q1 of 2024 [29] - Operating income for Q1 was $13.4 million, with an operating margin of 14.4%, compared to 8.9% in the prior year [30] Business Line Data and Key Metrics Changes - Term license and support revenue grew 12% in Q1, driven by large deals in the APAC region [23] - Maintenance revenue declined year over year, while service revenues grew 4% but decreased as a percentage of total revenues [23] - Combined SaaS and term license revenues grew 31% in Q1, indicating strong subscription revenue growth [23] Market Data and Key Metrics Changes - North America SaaS revenues grew 31% year over year, while EMEA and APAC saw growth rates of 36% and 40% respectively [24][25] - Total ARR at the end of Q1 was $345.5 million, representing year-over-year growth of 26% to 28% when adjusted for FX [26] - New ARR in Q1 was $18.5 million, representing organic growth of 57% year over year, the highest as a public company [26] Company Strategy and Development Direction - The company aims to become the world's leading data management software company, targeting $1 billion in ARR by 2029 [7] - Focus on integrated platforms to address converging challenges in data security, governance, and resilience [13][20] - Continued innovation in multi-cloud capabilities and AI-powered solutions to enhance customer value [14] Management's Comments on Operating Environment and Future Outlook - Management remains confident in navigating macroeconomic uncertainties, emphasizing the mission-critical nature of their solutions [8][20] - The demand environment has remained stable, with AI and security being top priorities for enterprises [38] - The company is cautious about potential geopolitical risks impacting the second half of the year while maintaining a strong pipeline [33] Other Important Information - The company ended Q1 with $351.8 million in cash and equivalents, including $87.3 million from warrant exercises [31] - Free cash flow was negative $1 million, primarily due to one-time tax payments [31] - The company repurchased 800,000 shares for approximately $12 million in Q1 [32] Q&A Session Summary Question: Changes in demand environment - Management indicated that the demand environment has remained stable, with no significant changes in customer hesitation or sales cycles [38] Question: Competitive environment and data governance - Management noted that data governance is a key driver for new customer acquisitions, especially as enterprises prepare for AI deployment [40] Question: Momentum in the MSP segment - The company remains optimistic about the MSP segment, which accounted for 14% of total ARR and grew 60% annually from 2020 to 2024 [47] Question: License outperformance in the quarter - The outperformance in license revenue was attributed to a combination of large deals and favorable timing dynamics [75] Question: AI implementation among customers - Management confirmed that AI implementation has not slowed down, with a sense of urgency among businesses to adopt AI solutions [80]

Summary of Key Points from Conference Call Records Industry and Companies Involved - The conference call discusses the performance and trends in the AI application sector, particularly focusing on companies like Duolingo, Palantir, and Applovin, as well as the education industry and programmatic advertising in China [1][4][8]. Core Insights and Arguments - **Growth in AI Applications**: The AI application sector in the US stock market is expected to see significant growth starting in the second half of 2024, with companies like Duolingo, Palantir, and Applovin reporting revenue and profit that exceed expectations, leading to stock price increases of 5-10 times [1][4]. - **Duolingo's Performance**: Duolingo has integrated GPT-4 into its platform, launching Duolingo Max, which has led to a 44% year-over-year increase in daily active users to 46 million and a 40% increase in paid subscribers to 10.3 million [1][9]. - **Palantir's Financials**: Palantir reported Q1 revenue of $884 million, a 39% year-over-year increase, with net profit rising 130% to $214 million. The company raised its full-year revenue guidance to $3.89 to $3.92 billion, reflecting a 20% growth [3][11]. - **Applovin's Advertising Growth**: Applovin's Q1 revenue reached $1.5 billion, with a 71% growth in advertising business and a non-GAAP profit margin of 81%. The company holds a 28% market share in game advertising and is expanding into the e-commerce advertising market [1][5][6]. - **AI and Programmatic Advertising**: The combination of AI and programmatic advertising offers insights for domestic companies like Tencent and Kuaishou, potentially increasing app display time and conversion rates [1][8]. Other Important but Potentially Overlooked Content - **Challenges in Education Sector**: The education industry faces challenges in producing large-scale, low-cost, high-quality educational products. AI technology can enhance personalized education but is limited by cost and precision issues [9][10]. - **Domestic AI Adoption**: Chinese education companies are adopting low-cost AI technologies to upgrade their products, with companies like Haotian and Anlian Education utilizing models like DeepSeek [3][10]. - **Future of AI in Enterprises**: The core challenge for enterprise AI applications lies in data governance. Companies that can manage data effectively are likely to benefit in the future [12][16]. - **Potential of Domestic Companies**: Companies like Fourth Paradigm are seen as potential equivalents to Palantir in China, focusing on data training platforms and data governance [17]. This summary encapsulates the key points from the conference call records, highlighting the performance of specific companies, industry trends, and the implications for future developments in AI applications.

Summary of the Conference Call for New Point Software Company Overview - **Company**: New Point Software - **Industry**: Software and AI solutions for government and construction sectors Key Points and Arguments Financial Performance - In Q1 2025, New Point Software reported revenue of 277 million yuan, a year-on-year decline of 5.94% [3] - Net profit attributable to shareholders was 41 million yuan, an increase of 23.2% year-on-year, with losses narrowing by approximately 12 million yuan [3] - The company experienced declines across various business segments: Smart Finance down 7.47%, Smart Government down 2.83%, and Digital Construction down 7.59% [3] - Despite the declines, overall conditions are gradually improving, with orders recovering [2][3] Cost Management and Profitability - The company achieved a 10.13% year-on-year reduction in operating expenses, leading to a continuous improvement in net profit margin [2][5] - The internal target for gross profit growth from orders is set at 30% [7] - The company anticipates a positive revenue turnaround for the full year, with rapid profit growth and sustained profit margin improvement [7][8] AI Developments - New Point Software has made significant progress in AI, launching the AI model product "Trading Brain" to empower the entire process of government procurement [2][6] - AI-driven customer service upgrades have improved service accuracy and reduced costs [2][6] - The company has developed various AI applications, including intelligent evaluation assistants and procurement document preparation tools, enhancing digital transformation for state-owned enterprises [6][10] Market Trends and Customer Engagement - Government IT spending is showing slight recovery, but overall budget growth remains cautious [22] - Government clients are increasingly pragmatic, focusing on immediate results and showing reluctance towards large-scale investments in traditional systems [23] - The company is expanding its target customer base from large projects to small and medium-sized clients, increasing market reach [26][27] Data Governance and Management - New Point Software has implemented a systematic public data collection and governance strategy, collaborating with local governments for data governance projects [19] - The company is focusing on procurement data, regulatory data, and supply chain management in the manufacturing sector [19] SaaS Business Growth - The maintenance service revenue growth rate has increased significantly, reaching 20% in Q1 2025, with government service revenue accounting for 40% of total revenue [31] - The market potential for SaaS applications is estimated to be between 1 billion to 2 billion yuan, with ongoing efforts to enhance service offerings [31] Future Outlook - The company aims to leverage AI to enhance the quality of government services and governance, addressing existing bottlenecks [25] - New Point Software is committed to achieving a turnaround in revenue and profit levels despite uncertainties in project acceptance cycles [8][9] Additional Important Insights - The company is focusing on improving cash flow management and has established a dedicated accounts receivable management department to enhance collection efficiency [28] - AI's impact on the company's business model is significant, leading to the creation of numerous SaaS payment application scenarios [13] - The integration of AI technology is expected to transform the entire landscape of government services, with interactive intelligent agents potentially replacing traditional service platforms [32][33]

在全球人工智能(AI)竞争如火如荼之际，欧盟委员会近日正式推出"人工智能大陆行动计划"，试图通过 政策松绑、算力基建和行业应用三大支柱，扭转其在AI领域的落后局面。这一计划被视为2024年正式 生效的《人工智能法案》的配套战略，标志着欧盟AI政策从"监管先行"向"监管与创新并重"的重要转 变。 市场研究机构TrendForce(集邦咨询)数据显示，2023年，全球AI服务器出货量中，北美占比60%，亚洲 占比约30%，而欧洲占据的份额不足10%。法国人工智能协会主席吕克·朱利亚指出，"算力是AI时代 的'石油'，欧盟此举意在确保技术主权。但问题在于，这些芯片从何而来？"目前，欧盟本土企业如英 国的拟未(Graphcoref )和法国的SiPearl尚未形成规模替代能力。新计划将与欧盟《芯片法案》联动，进 一步扶持本土半导体生态。 新计划还强调增加高质量数据获取渠道，发挥欧盟差异化优势，重点推动制造业、医疗、交通等战略领 域的AI应用。近年来，德国"工业4.0"以及法国"未来工厂"等项目积累了大量制造业数据，而欧盟《通 用数据保护条例》的严格隐私保护法规也使欧洲在医疗等敏感数据治理上更具公信力。"虽然欧盟未在 ...