Core Viewpoint - The recent joint announcement by four regulatory bodies aims to enforce stricter accounting standards for data assets, emphasizing cost measurement and enhancing cost management to curb valuation irregularities in data assets [1] Group 1: Regulatory Changes - The new regulations require companies to measure data assets based on actual costs incurred, eliminating the use of future value assessments, thereby preventing asset bubbles from forming [2] - Companies are prohibited from retroactively capitalizing previously expensed data resources, ensuring the authenticity and comparability of financial data [2] - An annual impairment test is mandated for data assets, requiring companies to assess and adjust the book value of data assets based on their current utility and potential obsolescence [2] Group 2: Information Disclosure - Companies must provide detailed disclosures regarding data asset classification, measurement methods, useful life, and impairment status, increasing transparency and reducing information asymmetry [3] - The new rules aim to enhance the reliability of financial data, thereby reducing the risk of investors falling into overvaluation traps when making investment decisions [3] - The regulations offer clear guidelines for auditing firms, enabling them to perform their roles more effectively and reducing the risk of professional judgment errors [3]

Core Insights - The value of data as an economic factor is increasingly recognized, with the number of data enterprises in China expected to exceed 400,000 and the data industry scale projected to reach 5.86 trillion yuan by 2024, marking a 117% increase from the end of the 13th Five-Year Plan [2] - Policy breakthroughs, such as the implementation of accounting regulations for enterprise data resources, have transformed data from an intangible asset to a measurable asset, facilitating innovative financing practices [2] - The emergence of various "first cases" of data asset pledge financing provides new pathways for small and medium-sized enterprises to address financing challenges [2] Industry Developments - The first data asset pledge financing in the artificial intelligence sector in Zhejiang Province was provided to Lianxin Technology, amounting to 10 million yuan, showcasing the innovative financing solutions for light-asset technology companies [3][4] - Hengfeng Bank issued a 10 million yuan data asset financing to Jining Public Transport Group, marking the first million-level data asset pledge financing in Jining, utilizing key operational data for the pledge [4] Challenges in Data Asset Financing - Despite the increasing number of cases, data asset pledge financing remains in a "small-scale trial" phase, with a total financing scale of 1.39 billion yuan expected by the end of 2024, indicating limited participation from various industries [6] - The lack of a unified national framework for data asset rights recognition poses significant challenges, leading to increased financing costs and risk management difficulties for financial institutions [6][7] - The complexity of valuing data assets, which are non-physical and easily replicable, complicates the assessment process, as traditional valuation methods may not be applicable [7][8] Risk Management Strategies - Banks are advised to implement dynamic risk management strategies throughout the data asset pledge loan process, including pre-loan assessments, pledge registration, and post-loan monitoring to mitigate risks associated with data asset financing [8][9] - The establishment of industry standards for data asset valuation is seen as a foundational step to address the challenges in this emerging financing area, with initiatives underway to create guidelines and tools for accurate valuation [9]