We caught up with Paul Christensen, the CEO of Previse to find out how the company started, its views on machine learning and the company’s plans for growth.
What does Previse do?
Previse analyses vast amounts of business trading data to deliver radically improved finance to the world’s SMEs.
We are an enabler for existing B2B networks. Products enabled by our infrastructure include flexible cash advances that are driven by business needs and automated day-1 early payments that are not dependent on a Buyer’s approval process.
What first attracted you to working in the technology space?
Impact. Simply the realisation that technology has the huge potential to make an impact by changing things for the better. I am massively future positive and believe that humans can solve our problems, and technology will be a large part of any solution. In my view, the two biggest challenges of our time are social inequality and the climate emergency, and technology can be the key to solving both.
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What was it that made you establish Previse?
Simply the belief that there has to be a better way. B2B payments and finance are archaic. Suppliers send invoices and wait and chase for months to get paid. SMEs struggle to access traditional finance. This is a huge, global, macro-economic inefficiency. And in this day and age, there is a better way. Previse was founded in 2016 by a small group of people who believe that business can be done much better. We believe that technology can change this, and that the answer is in the data.
We recognised that slow payments are damaging businesses everywhere and it was clear that current solutions weren’t working. We made it our mission to ensure that every supplier in the world can be paid instantly and at the fairest rate. We exist to unleash the power of data for B2B commerce. We believe in a world where business data makes commerce efficient and fair for all.
We realised the answer to the problem of $125trn of B2B payments lay in the huge amounts of historical data sitting in large buyers’ ERP systems which nobody was looking at. By applying machine learning to mine this data, we knew we would be able to predict the very few invoices unlikely to get paid, so that the rest could be paid instantly.
How have you seen machine learning evolve over the last five years?
The evolution of machine learning methodology has been incremental in the last few years, but the applications of machine learning have evolved significantly. It is now heavily utilised behind the scenes to enhance many products and services that we interact with every day, such as search engines, e-commerce, medical diagnosis, drug discovery, financial services, digital photography, traffic management, weather forecasting and much more. This has been made possible by the evolution and maturity of free and open-source machine learning tools, better availability of data, and cloud computing platforms making computational power more scalable, cost-effective and accessible.
What is the next big thing for Previse?
We are offering our platform through an embedded finance approach. Our software embeds into the large existing networks that power B2B commerce so that it becomes ubiquitously available. We call this the “pay me now” button and we want to make it available to every seller in the world.
Earlier this year, for example, we collaborated with Mastercard to integrate our machine learning into Mastercard Track Instant Pay – the first virtual card solution that can safely and intelligently authorise an immediate payment to a supplier once they submit an invoice.
As our journey continues, it has become apparent that we need to do more to help businesses than simply speeding up invoice payments. So we are taking another step, using our instant payment technology to make future revenue available as a cash advance today. Lots of exciting stuff on the horizon – watch this space!