Questions and answers from Previse CEO Paul Christensen

We caught up with Paul Christensen, CEO of Previse, to learn more about the company’s beginnings, his perspective on machine learning, and the company’s growth plans.

What does Previse do?

Previse analyzes vast amounts of trade data to provide radically improved financing to SMEs worldwide.

We are enablers for existing B2B networks. Products enabled by our infrastructure include flexible cash advances driven by business needs and automated Day 1 early payments that are not dependent on a buyer’s approval process.


What first drew you to work in technology?

Influence. Simply the realization that technology has the tremendous potential to make a difference by changing things for the better. I’m totally future proof and believe that humans can solve our problems and technology will be a big 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.

What made you decide to found Previse?

Simply the belief that there must be a better way. B2B payments and finance are archaic. Suppliers send invoices and wait and hunt for months to get paid. SMEs face difficulties in accessing traditional finance. This is a huge global macroeconomic inefficiency. And nowadays there is a better way. Previse was founded in 2016 by a small group of people who believe business can be done much better. We believe that technology can change this and that the answer lies in data.

We recognized that slow payments hurt businesses everywhere, and it was clear that current solutions weren’t working. We have made it our mission to ensure that every supplier in the world can be paid immediately and at the fairest price. 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 realized that the answer to the $125 trillion B2B payments problem lay in the vast amounts of historical data in the ERP systems of large buyers that nobody was looking at. By applying machine learning to extract this data, we knew we would be able to predict the very few bills that were unlikely to be paid, so the rest could be paid immediately.

How has machine learning evolved over the past five years?

The evolution of machine learning methodology has increased in recent years, but the applications of machine learning have evolved significantly. It is now being used heavily behind the scenes to improve many products and services we interact with on a daily basis such as: Such as search engines, e-commerce, medical diagnostics, drug research, financial services, digital photography, traffic management, weather forecasting and much more. This has been made possible by the development and maturity of free and open-source machine learning tools, greater availability of data, and cloud computing platforms that make computing power more scalable, cost-effective, and accessible.

What’s the next big thing for Previse?

We offer our platform via an embedded finance approach. Our software embeds itself into the major existing networks that drive B2B commerce, making it ubiquitous. We call this the Pay Now button and we want to make it available to every seller in the world.

For example, earlier this year we worked with Mastercard to integrate our machine learning into Mastercard Track Instant Pay – the first virtual card solution that can securely and intelligently authorize an instant payment to a supplier once they submit an invoice.

As our journey continued, it became clear that we need to do more to help businesses than just speed up bill payments. So we take it a step further and use our instant payment technology to make future earnings available today as a cash advance. Lots of exciting things on the horizon – watch this space! Questions and answers from Previse CEO Paul Christensen

Fry Electronics Team

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