How can the public sector make critical and efficient data-driven decisions?

efficient data-driven decisions
© Sarayut Thaneerat

Christian Marsden, Head of Financial Services and Government at Dun & Bradstreet, discusses how the public sector can make critical and efficient data-driven decisions

Funding is the lifebuoy businesses need while battling the forceful currents of COVID-19. Without financial aid, many companies would be unable to reopen their doors post-pandemic, which would lead to destructive consequences for employees and business owners. Thanks to the funding of UK businesses, many of these damages have been mitigated.

However, the speed and urgency at which these funds have had to be delivered to businesses have left room for improvements. Now, we’re at a stage in the pandemic where we can all begin to reflect on how the public sector can improve its funding processes and the decision-making around that further.

For example, emergency funding has been distributed on a first-come-first-serve basis. The issue with this is that funds aren’t necessarily directed at the businesses most under strain, but at the businesses that may well have survived without funding but were proactive in their loan application. While of course, funding is helpful for all that receive it, this approach doesn’t optimise the survival of businesses.

If we translate this scenario to a construction site, it could be likened to supporting a dilapidated building. You want to bolster the weakest spots in the structure while leaving those that stand the best chance of surviving on their own. This will preserve the building, as opposed to some of the weaker bricks falling while bolstering the strongest parts of the building.

So for the public sector, this means we need to identify where the weakest business areas are and prioritise funding there. This can be achieved through data that can give insight on need and efficacy. A lot of these businesses will be small- and medium-sized businesses, which make up 99.9% of the UK economy.

Although overall funding businesses in the UK has been positive, it is really important that we continue curating a smarter allocation process, with the right funds going to the right businesses. By supporting in the correct places, the risk of needing to supply additional rounds of business funding is significantly reduced. This is beneficial for the economy as it doesn’t add pressure to the government which is rising in debt, or strain public agencies and private lenders who are already experiencing high demands.

However, there are a number of ways that the smart application of data can ensure the funding process tips the UK in a positive, rather than negative, direction.

Diverse data

The majority of UK businesses can be classed as SMBs, but this is a broad term. Within it there’s multiple nuances and multiple reasons why one SMB may fair completely differently to another when facing COVID disruption. For the public sector to ensure it supports the weakest spots, particularly in the SMB market, it’s important they have a purview on these differences.

Having a diverse range of data will attain this insight. From this, the public sector will have a better understanding of where and how to distribute funding based on business size, location, revenue, lines of credit, and operational needs.

Identifying fraud and debt

The urgent need for bounce back loans gave the public sector no choice but to fund businesses at a pace by sacrificing thorough background checks. While this gave businesses much needed aid quickly, it also opened the floodgates for fraud and debt. This is a problem that the public sector will have to grapple with over the coming year as they attempt to recoup funds that shouldn’t have been granted. The government anticipated paying out £40 billion in bounce back loans, but the reality is that UK businesses have borrowed an estimated £46.6 billion in bounce back loans and borrowed an overall £75 billion through government-backed loan schemes. This could end up costing the taxpayer up to £26 billion, in part because of fraud but also due to defaulted loans. This illustrates the pressure on the government who are liable for failed loan repayments but also highlights the need for more accurate data.

Applying data alongside artificial intelligence (AI) will enable governments, and the banks they’re working with, to verify the legitimacy of loans and identify those that have committed crimes or need to return payments. Going forward, this data will prove useful in future situations where funding is needed at speed because the public sector will no longer need to compromise verification for pace.

Supply chain data

Analysing supply chain data is not commonplace for governments. However, in this instance, this is necessary to understand the threats to business recovery and to avoid wasting funds. Ultimately, what needs to be prevented in the business recovery phase is a start-stop approach.

For example, using government funding to bounce back will not work for a restaurant that will experience a supply shortage of food, cleaning supplies and linen a month down the line. This will only incur the cost of getting employees off furlough and the price of being open without being able to sell. In turn, the restaurant will face further debt and require additional government funding, which is a recipe for a double-dip recession. In this scenario, extending government funding before enabling the restaurant to open up when supply chain risks were limited, would be the best option.

Using supply chain data will give the foresight needed to avoid weaning businesses off government funding at the wrong time.

Business credit data

Unfortunately, there are some businesses that were not sustainable before the pandemic, and their inevitable downfall has coincided with COVID-19. In this instance, it may not be suitable to grant funding because it is only prolonging the administration process they were already working towards.

Using business credit data will help the government to determine whether the bounce back loan will actually be useful, or whether another public sector service, such as one that supports business owners and employees going through unemployment, would be more suitable.

There’s no justifiable reason why the public sector can’t reap the benefits of data and AI. As we continue to rebuild our economy and businesses, government funding will be a crucial component of this. With the purview data can give, we can ensure a truly sustained recovery.


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