PCCM 2026-07-30

Background

On 29 July, the People’s Bank of China and eight other government authorities issued a new policy on strengthening the development and use of data for sci-tech finance.

The policy introduces a national data catalogue covering eight categories and 26 indicators, including corporate innovation attributes, R&D investment, intellectual property, financing, operations, import and export activities, and innovation capability. It also encourages trusted data spaces, the authorized use of corporate cash-flow data, digital credit profiles, sector-specific risk models, industry-chain mapping, and the development of technology-enterprise databases by market-based credit reporting agencies.

This should not be understood as simply another government database project. It signals a broader transition in China’s sci-tech finance system: from assessing companies mainly through financial statements, collateral and official qualification lists, toward evaluating innovation capability, commercialization potential, real operating activity, cash flow and industrial-network position.

Korea’s Technology Credit Bureau (TCB) system offers a particularly relevant reference.

Since 2014, Korea has spent more than a decade developing a technology-finance infrastructure that connects technology assessment, corporate credit information and bank lending. TCB assessments typically examine not only the technical merits of an enterprise, but also marketability, business feasibility and its broader financial and credit position.

Based on our long-term tracking of the Korean TCB system and professional exchanges with institutions including KCIS，NICE，KoDATA and eCredible, the most important lesson is not that China should simply copy the Korean model. Rather, technology credit assessment must become a specialized and independently validated profession.

It requires industry-specific knowledge, reliable data standards, qualified evaluators, quality-control mechanisms and continuous feedback from actual lending performance. Korea’s experience also shows that rapid expansion in the number of assessments does not automatically guarantee assessment quality or better credit decisions.

The new Chinese policy could therefore create an important transformation opportunity for corporate credit reporting agencies.

By 2024, China had 154 corporate credit reporting agencies. However, many have yet to establish sustainable business models or sufficiently differentiated products. Some remain dependent on relatively simple public-data aggregation, conventional corporate reports or government-supported platforms.

Sci-tech finance opens a more specialized market. Credit reporting agencies could move toward:

• Sector-specific technology-enterprise databases • Technology credit assessment and monitoring • Patent, R&D team and commercialization analysis • Industrial-chain and enterprise-network mapping • AI-supported risk models for different technology sectors • Continuous post-lending monitoring and early warning

The commercial opportunity is not to build another static database or produce another apparently precise score. It is to create an explainable evidence layer between technology enterprises and financial capital.

AI will be essential—but the AI era makes professional databases more necessary, not less.

General-purpose large language models cannot replace reliable, time-stamped, permissioned and sector-specific data. Their value lies in helping institutions extract and verify information from patents, research documents, corporate disclosures and industry materials. Knowledge graphs can connect companies with shareholders, R&D teams, patents, technologies, customers, suppliers and industrial chains. Graph analytics can reveal dependencies, network stability and possible risk transmission, while AI agents can support continuous monitoring.

This allows technology assessment to move from a static, company-by-company approach toward a dynamic and network-based approach covering both company-to-company relationships and technology-to-technology connections.

At the same time, one principle must remain clear:

Innovation potential is not the same as creditworthiness.

A company may own valuable patents or employ an excellent technical team but still face weak cash flow, uncertain commercialization or high customer concentration. Technology capability, commercial viability and repayment risk should therefore be evaluated separately before being integrated into a final financing decision.

Against the broader background of global technology competition and supply-chain restructuring, China’s attempt to improve financial support for AI, semiconductors, new energy and other technology-intensive industries is understandable. The more constructive international question is not who will “win” a technology race, but how financial institutions can support innovation without weakening credit discipline, data governance or risk management.

This is also where our current research and international cooperation are increasingly focused: combining technology credit assessment, corporate credit reporting, AI and relationship-graph analysis to explore the next generation of sci-tech finance infrastructure.

China’s new policy creates a window of opportunity. Whether it succeeds will depend not only on how much data is collected, but on whether that data can be converted into professional, explainable and continuously validated credit decisions.

Reference

https://www.pbc.gov.cn/goutongjiaoliu/113456/113469/2026072915223955058/index.html

Korea FSC《Technology Finance Plan》（2024-04）

Korea Credit Information Services）TDB and Credit Inforamtion Service

Research Report about Korea TCB：https://mp.weixin.qq.com/s/Xh3qTsk6wcAAGwkdLaIg5Q

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