UK SMEs: quantifying their pandemic risk and credit risk exposures in the wake of the COVID-19

英国中小企业：量化 COVID-19 后的流行病风险和信用风险敞口

• 批准号: ES/V015419/1
• 负责人: Meryem Duygun
• 金额: $ 38.04万
• 依托单位: University of Nottingham
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=ES%2FV015419%2F1
• 关键词: UK; SMEs; quantifying; their; pandemic

Small and medium-sized enterprises (SMEs) constitute a critical pillar of the UK economy. More than 99% of the roughly 6 million businesses in the UK are SMEs and they employ more than 16 million workers. As the impact of the COVID-19 pandemic becomes clearer, it is evident that SMEs are facing serious and unprecedented challenges, including declining revenues, defaulting on loans, inability to retain employees and postponing growth plans. However, many SMEs in the UK find it extremely difficult to obtain funding through standard banking channels as the lack of financial information about SMEs makes it difficult to evaluate SMEs' credit risk and debt repayment capacity. Hence, to meet all these pressing needs, it is critical to develop an efficient protocol to assess SMEs' pandemic risk exposure and SMEs' resilience towards funding shortages caused by COVID-19.This project will use Artificial intelligence (AI) techniques including Machine Learning (ML), Deep Learning (DL), and Big Data to develop two novel analytical tools:1) The Pandemic Risk Index of UK SMEs (PRI):In this strand, the project will develop a novel Pandemic Risk Index (PRI) to model the potential economic, financial, and reputational effects of COVID-19 on UK SMEs in the short and long run. The academic and professional literature emerging in the wake of the COVID-19 crisis has considered several factors in isolation. However, this index aims to combine as many COVID-19- relevant variables as possible into one holistic multidimensional set of metrics. This is to have a better informed understanding of the big picture by accounting for and explaining the various weights and interrelationships of these variables. The main variables (but not exclusively) of this index would be (all of them are at the firm-level): exposure to global supply chains, exposure to international capital markets, corporate governance, financial flexibility, and geographical proximity to COVID-19 hotspots.2) AI-based Programme Suite to assess the Credit Risk of Borrowing UK SMEs (AI_CREDIT):In this strand, the project will develop an effective AI-based Python programme suite (AI_CREDIT) using Machine Learning (ML) and Deep Learning (DL) to provide policymakers in the UK government and financial intermediaries with an accurate and timely evaluation of an SME borrower's credit risk profile. With this, policymakers and lenders can make prompt decisions in providing appropriate emergency loans to SMEs to overcome their funding shortages and mitigate the impact of COVID-19. Based on the cutting-edge application of ML/DL to corporate credit risk, this project will develop a novel programme suite by integrating innovative methods. The innovations introduced by this project will extend the application of ML/DL in the estimation of SMEs' credit profiles by training ML/DL with a large amount of seemingly irrelevant data about large firms. The research impact of this project is relevant to many stakeholders. Policymakers and lenders can directly benefit by gaining access to novel tools to allocate funds and support SMEs efficiently. Other financial institutions including Insurance companies and private equity funds will benefit from the tools in assessing the risk related to SMEs in terms of insurance policies and investment decisions, respectively. All these are likely to lead to efficient allocation of funds and reduction of cost of funds allocated to SMEs which in turn will help SMEs to survive and thrive the current and any future pandemic disruptions. The planned project is UK wide, and it will be applicable to all UK SMEs. The project is in collaboration with the Bank of England and the Confederation of British Industry (CBI). CBI is a leading business lobby group that promotes business interests within public bodies and deals with the impact of policy on businesses in the UK. The engagement with the project partners and other stakeholders is crucial to scale up the implement

中小企业是英国经济的重要支柱。在英国约600万家企业中，99%以上是中小企业，雇用了1600多万工人。随着COVID-19疫情的影响变得更加清晰，中小企业显然正面临着严峻和前所未有的挑战，包括收入下降，贷款违约，无法留住员工和推迟增长计划。然而，英国的许多中小企业发现，通过标准的银行渠道获得资金极为困难，因为缺乏中小企业的财务信息，难以评估中小企业的信贷风险和偿债能力。因此，为了满足所有这些迫切的需求，开发一个有效的协议来评估中小企业的流行病风险敞口和中小企业对COVID-19造成的资金短缺的抵御能力至关重要。本项目将使用人工智能（AI）技术，包括机器学习（ML），深度学习（DL）和大数据，开发两个新的分析工具：1）英国中小企业流行病风险指数（PRI）：在这方面，该项目将开发一个新的流行病风险指数（PRI），以模拟COVID-19对英国中小企业的短期和长期潜在经济，财务和声誉影响。COVID-19危机后出现的学术和专业文献孤立地考虑了几个因素。然而，该指数旨在将尽可能多的COVID-19相关变量联合收割机组合成一个整体的多维度指标集。这是为了通过说明和解释这些变量的各种权重和相互关系，更好地了解大局。的主要变量（但不限于）该指数将是（所有这些都是在公司层面）：对全球供应链的风险敞口，对国际资本市场的风险敞口，公司治理，财务灵活性，以及地理上接近COVID-19热点。2）基于人工智能的程序套件，以评估借款英国中小企业的信用风险（AI_CREDIT）：在这条河上，该项目将开发一个有效基于人工智能的Python程序套件（AI_CREDIT）使用机器学习（ML）和深度学习（DL）为英国政府和金融中介机构的决策者提供对中小企业借款人信用风险状况的准确和及时的评估。借此，政策制定者及贷款人可迅速作出决定，向中小企业提供适当的紧急贷款，以克服其资金短缺及减轻COVID-19的影响。基于ML/DL在企业信用风险中的前沿应用，该项目将通过整合创新方法开发一个新的程序套件。该项目引入的创新将扩展ML/DL在估计中小企业信用状况方面的应用，通过使用大量关于大公司的看似无关的数据来训练ML/DL。该项目的研究影响与许多利益攸关方有关。政策制定者和贷款人可以通过获得新的工具来有效地分配资金和支持中小企业而直接受益。其他金融机构，包括保险公司和私人股本基金，将受益于这些工具，分别在保险政策和投资决策方面评估与中小企业有关的风险。所有这些都可能导致资金的有效分配，并降低分配给中小企业的资金成本，从而帮助中小企业在当前和未来的疫情干扰中生存和发展。计划中的项目是全英国范围的，将适用于所有英国中小企业。该项目与英格兰银行和英国工业联合会（CBI）合作。CBI是一个领先的商业游说团体，促进公共机构内的商业利益，并处理政策对英国企业的影响。与项目合作伙伴和其他利益攸关方的接触对于扩大实施规模至关重要。