Semantic Credit Risk Assessment of Business Ecosystems
Principal investigator: Professor Francesco Moscone
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)
The scale of impact goes from individual companies, to networks of companies (eco-systems), networks of ecosystems, the National Economy and policy-making. In the short-term (and the 3-year duration of the project) we expect that our research will impact initially on the commercial partners involved. We foresee that the initial experimentation of the Information Product, the network-based credit model and the semantic integration hub will influence our commercial partners' businesses (e.g., products/services designed around more accurate and integrated data, credit decisions based on eco-system models, etc.).
In the medium term (5-10 years) the impact will be on financial institutions and credit rating agencies, specifically in the way businesses are assessed and money is loaned. Over the same timeframe we foresee the economic model proposed here for credit to inspire other researchers who will adopt this theory to explore its application to other economic problems. In the long term (10-25 years) we envision that the deliverables of this project will have affected and rippled throughout the U.K. Economy and affect government in their policy-making by basing their economic policies and modelling also in the network effects that business eco-systems produce.