Posted on March 22, 2018 by staff

Machine learning could solve business cash flow deficit


Slow payments have forced small and medium sized businesses to take out £31.5 billion in funding to maintain their cash flow, according to new research from invoice analysis specialists Previse.

According to the report, this deficit is equivalent to the cost of hiring 640,000 new employees for a year.

“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,” said Paul Christensen, co-founder and CEO of Previse.

“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.”

The report also suggests that twenty per cent of SMEs 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 per cent of small businesses and force them to utilise expensive financing options to pay their own bills with typical interest rates of over 20 per cent APR.

Many SMEs require multiple funding sources to help cover their cash flow crisis, according to Previse, with the most common forms of funding being business credit cards (46 per cent), overdrafts (40 per cent) and business loans (38 per cent).

The company reports that one in five company founders are forced to take out personal loans to cover their business’s cash flow problems.