Small and medium-sized businesses have been forced to take out £31.5 billion in funding to maintain their cash flow due to slow payments – equivalent to the cost of hiring 640,000 new employees for a year2 – according to new research from Previse3, which creates value from invoice data using machine learning.
One in five SMEs (20%) typically wait 90 days or more for payments from clients, with buyers taking on average 34 days to pay invoices. As a result, slow payments impact cash flow for 77% of small businesses and force them to utilise expensive financing options to pay their own bills with typical interest rates of over 20% APR.
Many SMEs require multiple funding sources to help cover their cash flow crisis with the most common forms of funding being business credit cards (46%), overdrafts (40%) and business loans (38%). One in five (20%) company founders are forced to take out personal loans to cover their business’s cash flow problems.
Late payments lead to 50,000 UK small businesses being forced to close each year4. In addition, the extra costs of repaying the business debts have a significant impact on their ability to grow and employ new staff. With SMEs/SMBs responsible for 60% of hiring5 and generating more than 50%6 of GDP, slow payments cause a substantial drag effect on UK economic growth.
From the aftershocks of the Carillion disaster to the everyday struggle of entrepreneurs chasing down money they are owed, it is clear that slow payments are having a corrosive effect on small businesses’ prospects and on the economy as a whole. However, speeding up payments within large businesses is very difficult, as there are a number of important processes within the business that just take time.
What we need is the commitment of the whole business community to implement affordable and sustainable programmes for small businesses to receive their money much faster, so that they don’t need to rely on expensive financing. Artificial intelligence makes that possible, in ways that can benefit both small suppliers and their corporate buyers alike.Paul Christensen, co-founder and CEO of Previse
Currently being adopted by a number of the UK’s largest institutions, Previse’s machine learning technology enables large buyers to safely ensure they pay all suppliers instantly, cash-on-delivery, underwritten by external funding providers, removing the need for SMEs to access costly financing. Previse is cheaper, easier and safer than alternatives.
1 Number of SMEs taking loans to cover cash flow according to Previse’s research (29%) as a percentage of total SME funding according to UK Finance’s latest quarterly lending figures (£108.7 billion)
3 Total lending figure divided by the cost of employing a worker on the average UK salary using ASFB’s employee cost calculator: http://www.accountingservicesforbusiness.co.uk/calculators1/true-cost-of-an-employee/
3 Surveyed carried out by Censuswide with 500 “C-suite decision makers” inside UK companies with turnover between £100,000 and £50 million with the majority between £1 and £10 million.
4 “Time to act: the economic impacts of poor payment practices” – Federation of Small Business, 2016
5 Business population estimates for the UK and regions 2017 – Department of Business, Energy & Industrial strategy
6 Business Statistics briefing paper – House of Commons Library December 2017