10月14日，来伊份发布2025年半年度业绩说明会投资者关系活动记录表。当中披露，关于公司在AI技 术方面有哪些应用，来伊份表示，公司 5 月上线的"门店销售计划管理平台"将加盟商纳入统一框架，达 成直营与加盟一体化协同，叠加DeepSeek AI 大模型推动计划智能化，销售预测准确率、数据采纳率分 别提升30%、40%，供应链效能升级方面：五角大楼预警指挥平台构建全域预警网络，部署51个智能预 警模型及多通道触达机制，依托大数据实时识别偏差并赋能纠偏，同步推广AI智能客服以提效降本。 此外，围绕公司毛利率等财务数据变动，来伊份另解释称，公司在手现金充足，经营活动现金流净额短 期变动不会影响公司正常经营以及未来的扩张计划；公司毛利率变动主要系拓展新业务毛利结构的不同 所致。 （企业公告） 谈及公司经销商建设情况，来伊份回应，2025年上半年，公司新增经销商44个，经销特通业务重点布局 交通枢纽及教育场景，深化高铁特通渠道矩阵。 针对"公司给加盟商和联营商提供5000万的财务资助是出于哪些考量？"提问，来伊份称，公司此次提供 财务资助，资金仅限用于加盟或联营门店业务的经营，且单个加盟或联营门店的财务资助金额不超 ...

Core Viewpoint - The implementation of the Housing Rental Regulation marks a significant opportunity for compliant housing rental enterprises to thrive in a growing market, as the urban rental population exceeds 200 million in China [1][2]. Group 1: Market Dynamics - The rental market is increasingly competitive, and housing rental enterprises are seen as essential players that can stabilize and promote the rental system [2][3]. - The new regulation introduces a "classified supervision" model, categorizing market participants into four types, which allows for differentiated regulatory approaches [2]. - The regulation aims to address key issues in the rental market, such as information asymmetry, service standards, and tenant rights protection [2][3]. Group 2: Challenges and Opportunities - Current challenges in the rental market include inconsistent quality of housing supply, instability in rental relationships, and lack of regulatory oversight [2][3]. - The regulation provides a clear legal framework to address these challenges, offering compliant enterprises a pathway for sustainable growth [3][4]. Group 3: Financial and Digital Innovations - Housing rental enterprises are encouraged to leverage financial tools like REITs to transition from heavy asset models to a mixed investment approach, enhancing their resilience and long-term cash flow [4][9]. - Digitalization is identified as a crucial strategy for improving operational efficiency and transparency, with companies utilizing big data and AI to optimize services and customer engagement [6][7][8]. Group 4: Market Potential and Future Outlook - The top 30 housing rental enterprises in China have a combined operational scale of 1.398 million units, indicating significant room for growth in the market [5]. - The future of the rental market is expected to evolve into a dual-track system of market-driven and guaranteed rental housing, addressing the increasing demand for rental properties [9][10].

Core Insights - The banking industry is experiencing a dichotomy in attitudes towards AI application, with some professionals expressing skepticism while others advocate for its integration [1][2][3] - Many banks are reflecting on the necessity of AI and digital transformation, emphasizing practical applications rather than adopting technology for its own sake [3][4] - A notable trend is the increasing urgency among certain banks to enhance their digital capabilities and AI deployment, with some even incorporating data transformation rates into performance metrics [3][5] Group 1: AI Application in Banking - Numerous banks are exploring AI capabilities across various business scenarios, including customer service, wealth management, and risk control [5][6] - AI is being utilized to enhance customer interactions, with intelligent customer service systems leveraging existing semantic understanding and workflow capabilities [5][6] - In wealth management, banks are using AI to analyze customer preferences and market conditions to create tailored investment portfolios [5][6] Group 2: Challenges and Realities - Despite the enthusiasm for AI, many banking professionals express concerns about the effectiveness of AI in core business functions, particularly in customer service [2][3] - Some banks have invested heavily in AI infrastructure, such as GPU clusters, but the actual utilization rates do not justify the expenditures [3][4] - There is a recognition that technology should not be pursued for its own sake, and banks need to focus on integrating AI into practical business applications [3][4] Group 3: Future Outlook - As banks approach the release of their semi-annual reports, there is anticipation regarding more genuine disclosures about AI technology applications and digital transformation efforts [6] - The ongoing development of proprietary AI models tailored to banking needs is expected to enhance the effectiveness of marketing, customer service, and risk management [6] - The evolution of AI in banking is seen as a gradual process, requiring time for both employees and the market to adapt to these changes [6]

Core Viewpoint - The article discusses the contrasting perspectives among banking professionals regarding the application of AI in the industry, highlighting both skepticism and enthusiasm towards digital transformation and AI integration [1][2]. Group 1: Attitudes Towards AI and Digital Transformation - Many banking professionals express a cautious approach, emphasizing the need for practical applications rather than adopting AI for its own sake [2][3]. - There is a growing realization among banks that digital transformation should not merely be a superficial exercise, as evidenced by the proliferation of various AI agents claiming to empower banking functions [2][4]. - Despite the enthusiasm for AI, some banks struggle with the effective implementation of AI tools, leading to increased manual work rather than streamlining processes [3][5]. Group 2: Practical Applications of AI in Banking - Banks are increasingly utilizing AI in various business scenarios, including customer service, wealth management, risk control, and credit assessment [6][7]. - Common applications of AI include intelligent customer service that leverages existing capabilities to assist customer managers, and AI-driven investment portfolio customization based on client preferences [6][7]. - AI is also being employed in fraud prevention by analyzing customer behavior and transaction characteristics to enhance identification accuracy [6][7]. Group 3: Differentiation Among Banks - A divide is emerging among banks regarding their approach to digital transformation, with some adopting a more aggressive stance towards AI deployment and others taking a more passive approach [5][6]. - Certain banks are incorporating AI into less common areas, such as financial market operations, and are setting performance metrics related to AI utilization [5][6]. - The ongoing evolution of AI applications in banking reflects a gradual but significant transformation, with banks recognizing the need for time to fully realize the benefits of AI technology [7].

Core Viewpoint - Shengye Holdings Group Limited has demonstrated a successful strategic transformation, achieving a net profit increase of approximately 23% despite a slight decline in overall revenue, highlighting the effectiveness of its platform technology services and light-asset strategy [1][3][8]. Financial Performance - For the six months ending June 30, 2025, Shengye reported a main business revenue of 405 million yuan, a year-on-year decrease of 7.1%, while net profit rose to 203 million yuan [1]. - The platform technology service revenue reached 211 million yuan, a significant year-on-year increase of 37%, accounting for over 50% of total revenue [1][3]. - The company has maintained profitability for 11 consecutive years and has committed to a dividend payout ratio of no less than 90% from 2024 to 2026, with an expected total dividend of 950 million yuan for 2025 [2]. Strategic Transformation - The increase in platform technology service revenue is attributed to the continuous expansion of Shengye's platform ecosystem and enhanced technological capabilities, with over 19,100 cumulative clients, a 14.4% year-on-year growth [1][3]. - The light-asset strategy has led to a 33.9% reduction in financing costs, while the average financing cost for clients using the platform has decreased by over 30% [3][4]. Technological Investment - Shengye has invested nearly 270 million yuan in R&D in the first half of 2025, maintaining a high proportion of R&D personnel at 30% [4]. - The company holds 88 national invention patents and software copyrights, with applications in AI, big data, and cloud computing [4][6]. New Industry Layout - Shengye is actively expanding into emerging industries such as e-commerce, robotics, and AI applications, with significant growth in e-commerce partnerships, achieving a funding scale of over 2.8 billion yuan, an increase of nearly 800% year-on-year [5][6]. - The company has established strategic cooperation with leading firms in the robotics sector, positioning itself for growth in the rapidly expanding Chinese robotics market, which exceeds 190 billion yuan [5]. Internationalization and Innovation - Shengye has designated its Singapore subsidiary as its international headquarters and is expanding its presence in Southeast Asia and Turkey, successfully completing its first international funding facilitation [7]. - The company is exploring Web 3.0 and stablecoin applications to reduce cross-border payment costs and mitigate exchange rate risks, with a current operating cash flow of 3.56 billion yuan [7]. Industry Outlook - The supply chain finance industry is experiencing growth driven by national policies, particularly in technology finance, green finance, and digital finance, despite challenges such as information inadequacies and financing difficulties for SMEs [8][9]. - Shengye's performance reflects significant changes in China's supply chain finance and technology service sectors, leveraging AI and big data to enhance efficiency and reduce costs for SMEs [8].

Core Insights - The widespread application of intelligent customer service is a reflection of how fintech is reshaping banking services [1][3] - The digital transformation of banks is deepening, with a significant reduction in customer visits to physical branches, leading to the adoption of intelligent customer service as a standard [2][3] Group 1: Industry Transformation - The number of bank customer service personnel in China decreased to 50,200 by the end of 2021, down by 4,200 from 2020, marking a shift from previous growth trends [3] - The total amount of off-counter transactions in the banking sector reached 257.28 trillion yuan in 2021, a year-on-year increase of 11.46%, with an average electronic channel diversion rate of 90.29% [3] - The COVID-19 pandemic has accelerated the digital transformation of banks, pushing user habits further online and increasing the frequency and depth of online interactions [3] Group 2: Investment in Technology - In 2020, A-share listed banks invested 207.8 billion yuan in information technology, a year-on-year increase of 25% [6] - Major state-owned banks invested nearly 100 billion yuan in fintech in 2020, with Industrial and Commercial Bank of China investing 23.82 billion yuan, a 45.47% increase year-on-year [7] - China Merchants Bank reported an information technology investment of 13.29 billion yuan in 2021, a year-on-year increase of 11.58%, accounting for 4.37% of its operating income [7] Group 3: AI and Customer Service - In 2021, China Merchants Bank's AI initiatives replaced over 6,000 human roles through intelligent customer service and related technologies [4] - Intelligent customer service has expanded its application beyond routine inquiries to marketing and collection efforts, with voice robots effectively screening potential customers [5] - A report indicated that the satisfaction level of intelligent customer service is limited, with common complaints about repetitive responses and inadequate problem-solving capabilities [5] Group 4: Competitive Landscape - The demand for AI in the financial sector is steadily increasing, with total AI investment expected to exceed 22 billion yuan in 2022 [9] - Companies like BaiRong Cloud have reported significant revenue growth, with a 43% increase in total revenue to 1.623 billion yuan in 2021 [9] - Smaller banks are beginning to adopt AI technologies, with Guilin Bank collaborating with iFlytek to launch a virtual digital employee for customer service [10]

Core Insights - The Chinese government is promoting the "Artificial Intelligence +" initiative, aiming to integrate digital technology with manufacturing and market advantages, supporting the widespread application of large models in various industries, including finance [1] - KPMG's report indicates that the Chinese banking sector is at the forefront of implementing large models, with applications expanding from state-owned banks and joint-stock banks to leading regional banks [1] - The application of AI in banking is broadening, covering front-office services like intelligent investment advisory and product consultation, as well as middle and back-office functions such as intelligent anti-money laundering and regulatory compliance [1] Group 1 - The banking industry is experiencing unprecedented efficiency improvements and innovative breakthroughs due to the transformation of business processes through human-machine collaboration [1] - By 2025, more banks are expected to actively embrace AI and explore its application potential across various fields [1] - Challenges related to data security, model governance, ethical compliance, and talent skill upgrades accompany the application of new technologies, necessitating banks to establish comprehensive governance frameworks and risk prevention mechanisms [1] Group 2 - Despite the widespread application of large models in banking, there remains a gap between actual performance and user expectations, particularly in areas like AI customer service, which often leads to communication difficulties [2] - The banking sector needs to deepen its exploration of large models, shifting from "usable" to "optimal" applications, and from "broad" to "specialized" implementations [2] - Future efforts should focus on understanding the actual needs of different business scenarios, particularly in wealth management and investment strategies, potentially integrating AI with industry experts to address the limitations of large models in complex decision-making [2] Group 3 - There is ongoing potential for the application of large models in banking, particularly in customer marketing, business innovation, risk management, and institutional operations [3] - The emergence and promotion of open-source large models have reduced cost inputs for many banks, but the focus should be on optimizing and enhancing model performance rather than merely achieving usability [3] - Continuous resource investment is necessary for the ongoing exploration of "Artificial Intelligence + Banking" applications, ensuring data quality and improving the effectiveness of large model applications [3]

Core Viewpoint - The article discusses the successful hosting of an industry salon themed "AI Empowering Finance, Smartly Initiating the New Future of CFOs" in the Greater Bay Area, focusing on the application prospects and practical paths of AI technology in finance [1][13]. Group 1: Event Overview - The salon attracted numerous CFOs and finance professionals from the Greater Bay Area to explore the application of AI in financial work [1]. - The event was co-hosted by the Unknown AI Research Institute and Panoramic Network, aiming to leverage both parties' strengths to create a platform for cutting-edge technology exchange and idea collision for finance elites [2]. Group 2: Key Insights from the Speaker - Dr. Du Yu, the director of the Unknown AI Research Institute, highlighted how AI can assist enterprises in reducing costs and increasing efficiency [4]. - He emphasized that generative AI is becoming a new engine for industrial innovation and economic growth, particularly in the finance sector, where it can enhance work efficiency and provide more precise decision-making support [5]. Group 3: Applications of AI in Finance - AI technology has diverse application scenarios in finance, such as intelligent financial robots that can automate the processing of large volumes of financial data, significantly reducing manual operation time and error rates [7]. - AI can also conduct financial risk prediction and analysis, helping enterprises to proactively avoid potential financial risks and optimize financial management strategies [7]. Group 4: Future Implications for CFOs - Dr. Du Yu warned that CFOs who do not utilize AI tools may find themselves at a competitive disadvantage in the job market [9]. - He advised CFOs to embrace AI technology actively and enhance their digital skills to meet the demands of intelligent financial work [9]. Group 5: Participant Feedback and Future Plans - Many CFOs expressed that the salon provided them with a clearer understanding of AI applications in finance and recognized the significant potential and opportunities AI technology brings to financial work [11]. - The successful hosting of the salon not only provided a learning and exchange platform for finance professionals but also further promoted the application and development of AI technology in the financial sector of the Greater Bay Area [13].