（文章来源：证券日报） 证券日报网2月26日讯 ，海天瑞声在接受调研者提问时表示，经过多年发展与积累，公司逐步构建起了 在行业内的竞争壁垒，核心竞争力主要体现在：公司的业务模式是服务产品双模式，且产品化贡献显 著，是收入和毛利的主要来源，标准化数据集的研、产、销体系是公司从业多年探索出来的业务模式， 其复用性为公司的规模化和高利润率提供了保障。而保持这样的能力需要具备对行业需求的强判断力和 较强的资金实力。技术平台能力：公司历来重视技术的研发，近年来更是加大研发投入的力度，全面提 升公司的算法能力、平台能力、工程化能力，加深算法辅助能力与人工工作的结合，达到更佳的人机协 同效率，这样能够做大规模、提升效率、降低成本。供应链资源管理能力：公司通过长期建设的供应链 体系，保障资源的获取，未来，公司会进一步加大供应链资源平台的建设，使人员管理、采标资源分 配、质量检验、远程工作等各方面的能力得到显著提升，为客群拓展提供有力支撑。数据安全及合规能 力：数据安全及合规能力已经成为了衡量品牌数据服务商综合能力的重要指标。公司在多年数据风险识 别和管理实践中，已形成了较为成熟的安全、合规管理体系。公司全方位做好数据风险管控 ...

（原标题：企查查数据安全体系通过中国信通院泰尔认证） 近期，企查查顺利通过中国信通院泰尔认证中心（TLC）的严格产品检测与全面审查，正式获得《数据 安全管理体系认证》证书。此次认证范围全面覆盖企业信息查询、数据产品服务全流程涉及的数据安全 管理活动，标志着企查查的数据安全管理水平已跻身行业先进梯队，获得国家权威机构的官方认可。 在用户服务与合规落地层面，企查查同样表现突出。目前，其已构建覆盖3.65亿家市场经营主体、数据 维度超400个的全域数据库，每日滚动更新亿级企业动态信息，服务超1.5亿注册用户及众多金融机构、 政企单位。值得关注的是，其ToC端办公产品投诉率仅为0.00177%，远低于行业平均水平，同时通过优 化多途径申诉功能、完善个人信息模糊处理机制、畅通投诉渠道等举措，持续提升用户体验与信息安全 保障能力。 当前，数据要素市场化改革持续深化，数据安全与合规已成为商业大数据行业高质量发展的核心前提。 此次企查查通过泰尔认证，不仅进一步夯实了自身的数据安全合规壁垒，为广大用户提供更可靠、更安 全的企业信息服务，也为行业树立了"技术赋能合规、合规引领发展"的标杆。作为入选江苏省首批数据 企业培育名单的领 ...

Core Insights - The core viewpoint of the article emphasizes the digital transformation of the futures industry in Guangdong, which is essential for improving service efficiency, risk management, and meeting the needs of the real economy [1][4]. Group 1: Digital Transformation in Futures Industry - The Guangdong futures industry is leveraging digital transformation to address long-standing issues such as inefficient service processes and inadequate risk management tools for small and medium enterprises [1][3]. - Companies like Guangfa Futures are implementing digital solutions such as pre-accounting platforms and online business processing to enhance service transparency and efficiency [1][2]. - The development of intelligent hedging systems using big data analytics has significantly reduced the time required to design hedging strategies from days to hours [1][2]. Group 2: Integration of Technology and Business - Guangfa Futures has created a global market platform that integrates real-time data from major exchanges, enabling clients to capture market dynamics effectively [2]. - The focus on digital capabilities has become a core competency for companies like Guangzhou Futures, which aims to enhance operational efficiency through automation and online services [2][3]. - Huatai Futures' Tianji platform exemplifies the integration of digital technology with industry needs, providing a comprehensive risk management infrastructure [4][5]. Group 3: Product Innovation and Risk Management - The industry is innovating product offerings to simplify complex risk management processes, such as Huatai Futures' "one-click hedging solution" that generates multiple feasible plans based on basic client inputs [5]. - The "Cumulative Treasure" product from Huatai Changcheng Capital has been well-received, providing significant profit support for clients in challenging market conditions [6]. - The Dragon Spring platform from Zhongzhou Futures offers dynamic hedging solutions and custom versions for enterprises, promoting proactive risk management [7]. Group 4: Collaborative Ecosystem Development - The Guangdong futures industry is focusing on building a collaborative digital ecosystem through platform construction, inter-institutional cooperation, and talent cultivation [10][11]. - Zhongzhou Futures is developing a cross-institutional service network to enhance collaboration and provide comprehensive services to enterprises [10]. - The Tianji platform has successfully registered over 20,000 users and generated more than 60,000 hedging plans, demonstrating its broad reach and impact [11]. Group 5: Future Challenges and Directions - Despite progress, the industry faces challenges such as data security, compliance risks, and the need for deeper integration of technology and business [12][13]. - There is a significant demand for talent that combines expertise in futures with knowledge of advanced technologies like AI and big data [13][14]. - The industry aims to continue evolving by focusing on technology-driven solutions and ensuring compliance with regulations to enhance its value in serving the real economy [15].

Core Insights - The industry is undergoing a comprehensive transformation, shifting from a "seller product sales" model to a "buyer advisory" model, focusing on customer lifetime value and comprehensive solutions [2][7] - Customer coverage is expanding from high-net-worth individuals to a broader audience, including middle-aged, younger generations, senior citizens, and rural populations [2][7] - Customer demands are becoming increasingly diverse, encompassing both stable investment needs and high-risk, high-reward aspirations, as well as extending to family inheritance scenarios [3][7] - Product innovation is accelerating, with banks not only enriching their own product systems but also introducing public funds and insurance as part of their ecosystem [3][7] - The logic of technological support is evolving, with AI becoming a core production tool that reconstructs the entire business process [4][7] Industry Challenges - There is a contradiction between conservative regulatory policies and limited application scenarios for technology, as AI is currently not allowed to directly replace human management in trading, which restricts the full release of technological value [4][7] - Approximately 70%-80% of large model project investments have not met expectations, primarily due to discrepancies in model selection, application scenarios, and implementation methods [4][7] - The recommendation for institutions is to adopt a "small steps, quick iterations" approach to enhance investment returns [4][7] Data Security and Compliance - There is an optimistic view regarding balancing data security and compliance, suggesting that technological means can effectively resolve the contradictions between regulatory compliance and innovative development [8] - Techniques such as pre-processing constraints and post-processing checks can prevent data leakage, while privacy computing and data de-identification can achieve data usability without visibility [8]

Core Principles and Philosophy - The Hong Kong Stock Exchange (HKEX) emphasizes a "disclosure-based" approach, focusing on ensuring companies meet listing qualifications and providing accurate, timely information for investors to assess value and risk [2][5] - The core principles include ensuring suitability for listing, investor protection, and enhancing market quality and reputation [5][8] Specific Review Points - The review process is guided by the Listing Rules, particularly Chapter 8, covering aspects such as entity qualifications, business and industry disclosures, corporate governance, and internal controls [3][4] - Key areas of focus include: - Disclosure of significant information in the prospectus without major omissions or false statements [5] - Assessment of whether the company meets quantitative and qualitative listing thresholds [5] - Protection of shareholder rights, especially for minority shareholders [5] - Evaluation of the company's business sustainability, competitive advantages, and industry outlook [5][8] Financial Information and Compliance - Companies must provide clear and feasible plans for the use of raised funds, avoiding vague statements [6] - Financial statements must comply with Hong Kong Financial Reporting Standards or International Financial Reporting Standards, and significant accounting policies must be reasonable and prudent [9] - Disclosure of all relevant risks, including major litigation, tax compliance, and asset ownership, is required [9] Recent Trends and Additional Focus Areas - There is an increasing emphasis on ESG (Environmental, Social, and Governance) disclosures, particularly for high-impact industries, although not yet mandatory [9] - Data security and compliance are critical for technology and fintech companies, focusing on adherence to relevant laws [9] - Attention is given to supply chain concentration and geopolitical risks, as well as potential market manipulation behaviors [9]

Group 1 - The year 2025 is referred to as the "Year of Intelligent Agents," marking a paradigm shift in AI development from "I say AI responds" to "I say AI does," with intelligent agents becoming a key commercial anchor and the next generation of human-computer interaction [1] - As intelligent agents approach practical application, the associated risks become more tangible, raising concerns about overreach, boundary violations, and potential loss of control [2] - A survey indicates that nearly 80% of industry professionals are worried about the consequences of user data leakage, highlighting the pressing issue of data transparency and compliance in the era of intelligent agents [2] Group 2 - The transparency of data usage varies significantly across platforms, as illustrated by the example of using AI to generate a resume, where some platforms clearly disclose the tools being used while others do not [3][4] - Different platforms adopt varying approaches to handling sensitive personal information, with some providing clear risk warnings and others obscuring sensitive data in outputs without prior notification [7][9][11] - The responsibility for data handling is often obscured, with user agreements typically placing the onus on developers for the data generated during interactions with intelligent agents [12][13]