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

Lead Research Organisation: University of Nottingham
Department Name: Nottingham University Business School

Abstract

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

Publications

10 25 50
 
Description The work conducted in this project has advanced existing knowledge about the determinants of pandemic risk exposure in UK Small Medium Enterprises (SMEs). Our empirical analysis of UK SMEs has revealed many important findings: 1) the industry-level market reactions to regional and national lockdown announcements represent a crucial factor in predicting the impact of the COVID outbreak on SMEs, 2) the pandemic risk exposure of SMEs materialises mostly in the form of a reduction in the number of employees, a drop in domestic revenues, an increase in cash holdings (mainly due to lower operating and investing cash flows), and an increase in gearing and leverage, and 3) the drop in creditworthiness of many SMEs is most likely caused by a higher amount of non-current liabilities and borrowing.
To quantify the pandemic risk exposure of SMEs, and in particular the potential impact of COVID-related risks on the likelihood of default, we have built a comprehensive Pandemic Risk Index (hereafter, PRI) that can be used as an early warning signal during pandemic crises.
We are still in the process of fine-tuning the index, but we have already run regressions considering different variables that might increase the vulnerability of SMEs to pandemic crises. We are now extending the set of variables to consider in our PRI using dozens of financial and non-financial variables collected from hundreds of thousands of firms, as well as industry-level and regional variables.
More specifically, the main constituents of our PRI are: a) firm-level variables such as assets, liabilities, debt, equity, number of employees and foreign exposure, b) industry-level variables such as market reactions to government announcements of lockdowns, and c) region-level variables such as the number of covid-19 cases and deaths in the local authority where the SME's primary trading address is.
The preliminary version of our PRI has a strong predictive power of post-COVID changes in variables such as the number of employees, cash holdings, gearing, and leverage. In addition, we plan to develop a dynamic version of our PRI that can be updated on a daily basis, using market movements in industry-level indices and newly reported COVID cases and COVID-related deaths at the regional level.
Exploitation Route The project has a clear and innovative dissemination plan that involves research outputs to academic and non-academic world.
We have already held our first workshop that brought together academics and policy-makers to share with them our initial findings. This was held online on 28th January, 2021. In this workshop, we have benefited from discussions and debates with representatives from our project's institutional partners (Bank of England and Confederation of British Industry) and several academics and practitioners. To support broad dissemination of our findings to the UK public and other relevant stakeholders, we are currently developing a dedicated project's website, that will be updated on a regular basis, with information on the achieved results and forthcoming events.
The outcomes of this funding can provide the basis for policy interventions by a number of institutions which support SMEs through COVID-19 disruption. These include HM Treasury, Bank of England, Department for Business, Energy & Industrial Strategy (BEIS) and Confederation of the British Industry (CBI). We also expect that SMEs and financial institutions, i.e. all types of lenders, insurance companies and private equity funds can benefit from the project outcomes.
Sectors Financial Services, and Management Consultancy