给企业做“CT扫描”+“基因检测”？宁诺这项技术已服务2900+企业

Please scroll down for the English version - UNNC research uses AI to reveal the true innovation potential of tech enterprises.

///宁诺教授

让科技企业含金量一目了然

导读

在近期举办的宁波科技周上，一批本地科技企业的创新成果集中亮相。在令人眼花缭乱的新技术背后，问题也逐渐显现：如何看懂这些企业的技术价值和发展潜力？其含金量又该如何衡量？

围绕这一难题，宁波诺丁汉大学（简称“宁诺”）金融科技教授华秀萍团队开发了“基于AI与大数据的科技企业多维度全息画像技术”。

这项技术如同为企业进行 “CT扫描”和“基因检测”，更清晰地识别科技企业的技术价值和发展潜力。

01

AI与大数据赋能：从“财务评价”迈向“创新能力识别”

“在传统融资评估中，金融机构依赖抵押物和稳定现金流，而科技型企业多为知识密集型组织，其核心资产往往是专利、技术、人才等无形资产，像‘黑箱’一样难以量化评估。”

华秀萍教授指出，由此产生的信息不对称，成为横亘在科技企业与金融机构之间的壁垒，也让真正具备创新能力的企业在融资发展中受阻。

因此，华秀萍团队深入研究近三万家企业的数据，突破以财务指标为主的传统评价方式，搭建起一套以创新能力为核心的评估体系。

在具体评估时，系统通过机器学习模型，对企业的研发投入、创新成果质量、成长情况和成果转化能力等进行量化分析，并借助AI大模型解析专利和技术资料，判断技术所处阶段及发展潜力。

在这一过程中，系统将晦涩的技术语言转化为清晰可读的信息，最终形成完整的企业画像，为金融机构和政府部门提供决策依据。

图为系统中的

“知识产权展示与专利质押贷款估值“模块

“对于一些初创团队，虽然尚未盈利，但可能掌握前沿核心技术，甚至是细分领域的开创者。在传统银行评分体系中这类企业往往得分不高，但在我们的评估体系中却可能获得较高评价。事实上我们也发现，许多优质科技企业的创新能力并不逊于一些知名上市公司。”华秀萍说道。

02

融合政策导向：不仅“评估表现”更“捕捉关键技术”

更为重要的是，该系统并非单纯筛选高科技企业，而是结合政策导向和本地产业需求，判断技术与区域发展的适配性，从而识别出能解决实际问题、支撑区域发展的关键技术。

据悉，自2013年起，该技术已服务地方政府，并应用于宁波市科技企业创新能力评价与信贷政策“白名单”的生成。

目前已服务10家金融机构、2900余家企业，促成约470亿元授信支持，其中不乏多家战略性新兴产业、未来产业，以及致力于突破“卡脖子”技术、解决当前技术瓶颈的科创企业。相关指标持续动态更新，其中风险运营类指标已实现每三天更新一次。

图为系统中的

“企业创新能力评分以及五维图”模块

“这一体系有助于政府更早识别具有潜力的科技企业，也为企业外部融资提供专业评估，从而更有针对性地配置科技金融资源，推动形成‘技术—资金—产业’的良性循环，加快区域新质生产力的培育。”华秀萍说道。

UNNC research uses AI to reveal the true innovation potential of tech enterprises

At the recent Ningbo Science and Technology Week, local technology companies presented a wide range of cutting-edge innovations. Yet behind the rapid emergence of new technologies lies a persistent challenge: how can the real technological value and growth potential of these companies be accurately assessed?

To address this issue, a research team led by Professor Hua Xiuping from University of Nottingham Ningbo China (UNNC) has developed an “AI and Big Data-based Multi-dimensional Holographic Profiling Technology for Technology Enterprises”. The system acts like a “CT scan” and “genetic test” for companies, helping financial institutions and government agencies better identify the innovation capabilities and future potential of technology enterprises.

From financial metrics to innovation capability

Traditional financing assessments often rely on collateral and stable cash flow, making it difficult for knowledge-intensive technology companies to demonstrate their true value. To overcome this limitation, Professor Hua’s team analysed data from nearly 30,000 enterprises and established an innovation-focused evaluation framework that goes beyond conventional financial indicators.

Using machine learning and AI large language models, the system analyses factors such as R&D investment, innovation quality, growth performance and commercialisation capability. It can also interpret patents and technical documents to assess the maturity and future prospects of technologies, transforming complex technical information into accessible insights for decision-makers.

“For some start-up teams, they may not yet be profitable, but they could possess cutting-edge core technologies or even be pioneers in niche sectors,” said Professor Hua. “Under traditional banking scoring systems, such companies may receive relatively low ratings, but within our evaluation framework they may achieve much higher scores.”

Supporting regional innovation and industrial development

The system also incorporates policy priorities and local industrial needs, enabling it to identify technologies that can address practical challenges and contribute to regional development.

Since 2013, the technology has supported local government initiatives and has been applied in Ningbo’s innovation capability evaluation system for technology enterprises, as well as in generating “white lists” for credit support policies. To date, it has served more than 10 financial institutions and over 2,900 enterprises, facilitating approximately RMB 47 billion in credit support for companies in strategic emerging industries and future-oriented sectors.

“This system helps governments identify promising technology enterprises at an earlier stage, while also supporting companies in securing external financing,” Professor Hua noted. “It enables more targeted allocation of science and technology finance resources, helping to create a positive cycle linking technology, capital and industry.”

图文来源：Jenny Zhuang，Tony Bao

电子编辑：Suri An

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