給企業做“CT掃描”+“基因檢測”？寧諾這項技術已服務2900+企業

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

///寧諾教授

讓科技企業含金量一目了然

導讀

在近期舉辦的寧波科技周上，一批本地科技企業的創新成果集中亮相。在令人眼花繚亂的新技術背后，問題也逐漸顯現：如何看懂這些企業的技術價值和發展潛力？其含金量又該如何衡量？

圍繞這一難題，寧波諾丁漢大學（簡稱“寧諾”）金融科技教授華秀萍團隊開發了“基于AI與大數據的科技企業多維度全息畫像技術”。

這項技術如同為企業進行 “CT掃描”和“基因檢測”，更清晰地識別科技企業的技術價值和發展潛力。

01

AI與大數據賦能：從“財務評價”邁向“創新能力識別”

“在傳統融資評估中，金融機構依賴抵押物和穩定現金流，而科技型企業多為知識密集型組織，其核心資產往往是專利、技術、人才等無形資產，像‘黑箱’一樣難以量化評估。”

華秀萍教授指出，由此產生的信息不對稱，成為橫亙在科技企業與金融機構之間的壁壘，也讓真正具備創新能力的企業在融資發展中受阻。

因此，華秀萍團隊深入研究近三萬家企業的數據，突破以財務指標為主的傳統評價方式，搭建起一套以創新能力為核心的評估體系。

在具體評估時，系統通過機器學習模型，對企業的研發投入、創新成果質量、成長情況和成果轉化能力等進行量化分析，并借助AI大模型解析專利和技術資料，判斷技術所處階段及發展潛力。

在這一過程中，系統將晦澀的技術語言轉化為清晰可讀的信息，最終形成完整的企業畫像，為金融機構和政府部門提供決策依據。

圖為系統中的

“知識產權展示與專利質押貸款估值“模塊

“對于一些初創團隊，雖然尚未盈利，但可能掌握前沿核心技術，甚至是細分領域的開創者。在傳統銀行評分體系中這類企業往往得分不高，但在我們的評估體系中卻可能獲得較高評價。事實上我們也發現，許多優質科技企業的創新能力并不遜于一些知名上市公司。”華秀萍說道。

02

融合政策導向：不僅“評估表現”更“捕捉關鍵技術”

更為重要的是，該系統并非單純篩選高科技企業，而是結合政策導向和本地產業需求，判斷技術與區域發展的適配性，從而識別出能解決實際問題、支撐區域發展的關鍵技術。

據悉，自2013年起，該技術已服務地方政府，并應用于寧波市科技企業創新能力評價與信貸政策“白名單”的生成。

目前已服務10家金融機構、2900余家企業，促成約470億元授信支持，其中不乏多家戰略性新興產業、未來產業，以及致力于突破“卡脖子”技術、解決當前技術瓶頸的科創企業。相關指標持續動態更新，其中風險運營類指標已實現每三天更新一次。

圖為系統中的

“企業創新能力評分以及五維圖”模塊

“這一體系有助于政府更早識別具有潛力的科技企業，也為企業外部融資提供專業評估，從而更有針對性地配置科技金融資源，推動形成‘技術—資金—產業’的良性循環，加快區域新質生產力的培育。”華秀萍說道。

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.”

圖文來源：Jenny Zhuang，Tony Bao

電子編輯：Suri An

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