SCRIBE: Semantic Credit Risk Assessment of Business Ecosystems

该提案通过改善中小企业（SMEs）获得信贷的渠道，解决了数字经济和金融服务研究的挑战。问题是，信贷决策中的信息通常仅限于公司和个人的业绩记录。它忽视了公司在其商业生态系统中的地位和重要性。因此，金融机构的信贷决策具有看不见的网络效应，并以看不见的方式限制了增长。为了解决这个问题，SCRIBE利用新兴的语义技术，基于对公司与其商业生态系统（或网络）相关的地位和价值的动态理解，以更准确的实时信用风险评估的形式提供颠覆性创新。SCRIBE的科学贡献是双重的。首先，该项目融合了最先进的（社会）网络分析和信用评估技术，以发展其基于生态系统的理解（以及相关的营销机会）。其次，作为技术基础，该项目开发了一种最先进的方法来“协调”不同的概念模型，这些模型是来自多个来源的数据的基础，这样做可以保持上下文的丰富性。上下文保存不仅对基于网络的决策很重要，而且对项目所考虑的审计和法律问题也很重要，因为传统的数据建模隐含地抽象了上下文的重要方面。科学贡献是通过合作伙伴关系开发和利用的，这种合作伙伴关系结合了对信用风险的理解和交易层面（通过开放的在线会计数据和与劳埃德的合作）和公司层面（通过与Creditsafe的合作）的评估。为了解决NEMODE的问题，该项目通过开发新的信息产品和应用程序（通过与Level Business合作）来保持对影响的关注。

This proposal addresses the Digital Economy and Financial Services research challenge by improving Small and Medium Enterprises' (SMEs) access to credit. The issue is that information in and around credit decision-making is generally limited to company and individual track record. It ignores the position and importance of a company in its business ecosystem. Credit lending decisions by finance providers therefore have unseen network effects and limit growth in unseen ways. To address this issue, SCRIBE uses emerging semantic technologies to provide disruptive innovation in the form of more accurate real-time credit risk assessment based on a dynamic understanding of the position and value of a company in relation to its business ecosystem (or network). The scientific contributions of SCRIBE are twofold. First, the project fuses the state-of-the-art in (social) network analytics and credit assessment techniques to develop its ecosystem-based understanding (and associated marketing opportunities). Second, as technical foundation, the project develops a state-of-the-art method to 'harmonise' the different conceptual models that underlie data drawn from multiple sources, preserving contextual richness in so doing. Contextual preservation is important not only for network-based decision-making, but also for audit and the legal issues considered by the project since it is relatively well-acknowledged that conventional data modelling implicitly abstracts away important aspects of context.The scientific contributions are developed and exploited via a collaborative partnership that combines understanding of credit risk and assessment at both the transaction-level (via open online accounting data and via collaboration with Lloyds) and firmographic-level (via collaboration with Creditsafe). Addressing the NEMODE ethos, the project maintains a focus on impact via the development of novel information products and applications (via collaboration with Level Business).