Partner Article

AI startup looking to ensure SMEs get paid on time raises £2m

London-based Previse, the FinTech startup utilising artificial intelligence to battle the scourge of late contract payments by large corporates, has announced it has closed its £2m seed funding round.

Led by Hambro Perks alongside Founders Factory and with contribution from high net worth angels, the funding round will help the company develop its proprietary AT platform which it says can support instant payments between large corporates and SMEs.

Previse, which means to predict or forsee, works by instantly analysing an issued invoice and uses its advanced AI system and ‘millions’ of data points to determine how likely a corporate buyer will be to eventually pay a supplier’s invoice.

The startup’s system the provides a score to funders, including banks and asset managers, who can then pay the supplier on the buyer’s behalf.

Previse’s Co-Founder and Chief Executive Officer Paul Christensen, believes the current system of SME suppliers being beholden to corporate behemoths is simply unsustainable and could have serious consequences for the economy as a whole.

He commented: “SMEs are the backbone of the world economy, generating the majority of growth, employment and innovation. Yet, most of them are consistently paid late by corporate buyers. It is an unsustainable position which damages the entire economy.

“Previse’s AI technology ensures we no longer have to accept this situation. Instant, frictionless and efficient payments can become the new standard for B2B payments.

“The prize is 50,000 more small businesses kept open in the UK alone, corporates able to exceed their obligations to suppliers and a new £2.4 trillion global market for funders.”

In raising the funding, Previse becomes yet another tech startup looking to apply AI and machine learning techniques to a sector that is ripe for disruption.

The company’s Chief Data Scientist and Co-Founder, Philipp Schoenbucher believes payment decisions are the ‘perfect candidate’ for the tech.

He explained: “Our advanced proprietary machine learning algorithms were developed using data sets of multiple billion dollars of corporate spending, building upon state of the art binary classifiers and highly innovative domain-specific feature engineering methods.”

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