Open Banking Helps Fast Track AI-Driven Lending, Says Nucleus Finance CEO

When it comes to artificial intelligence (AI)-driven credit decision-making, Chirag Shah, founder and CEO at U.K. FinTech lender Nucleus Commercial Finance, is of the view that it’s all about data.

“Most lenders right now have gathered substantial amounts of data in a form that can help us build the right machine learning (ML) and AI tools on top of it to drive benefits for businesses and drive credit benefits for us as a lender,” Shah told PYMNTS in an interview.

Despite the progress made, he said the full power of AI has not yet been unleashed in the business lending space — a goal that will require bringing all stakeholders up to speed on its potential.

“We still need to bring a lot of players [to the table], and that includes not just the lenders and the tech players, but also the banks who are providing the ultimate capital for the loans,” he said, adding that getting to a good level of comfort with AI will require incorporating human intelligence in the lending process instead of adopting a 100% automation approach.

Overall, he said it comes as no surprise that the uptake of AI-driven lending has been slower in the business lending space given the billions of dollars at stake. But businesses are starting to warm up and embrace the innovative technology.

“We are seeing the tide turn that way, but it still has some way to go,” he said.

In the meantime, he said open banking technology has had a transformative effect in the lending and borrowing space by enabling loan providers like Nucleus Commercial Finance to access businesses’ accounts and make informed credit decisions, all while reducing the default rates by more than 30%.

“If a business wants to work with us, they need to provide us with open banking and open accounting access,” he said. “Without that, we won’t lend.”

Implementing AI would be very challenging without open banking data, he said.

The benefits of open banking data also extend to startups that tend to struggle to access more funding due to a lack of sufficient data.

“But as we gather data on multiple businesses that have applied to us and are able to make better predictions, we are also able to apply that to expand the pool of businesses that we can work with, especially to younger businesses,” he said.

Source Funds When You Don’t Need Them

Last September, the U.K. firm launched its Pulse data tool to offer businesses regular, data-driven insights into their financial performance to improve their chances of getting approved for a loan.

“It’s a once-a-month snapshot of how the business is performing, and based on our predictive technology, we can point out issues that may come up, so that they can take corrective action,” Shah explained, adding that most times, issues tend to stem from cash flow gaps.

He also offered another tip to improve businesses’ chances of success: “The easiest time to source funding is when you don’t need it. Unfortunately, most businesses look for funding when they desperately need it, and at that point, it’s incredibly challenging.”

On accelerating loan approval times and developing instant, single-click lending via embedded finance, he said Nucleus has achieved about 95% of that goal so far thanks to AI and ML, but closing that gap has been slow due to how segregated data is on the business lending side.

“The number of sources we need to go to get the data is significantly higher than on the consumer side,” he explained.

Overall, he expressed optimism about the future based on the data insights gathered so far this year, with demand from small- to medium-sized businesses (SMBs) picking up significantly as firms make better decisions around capital investment and planning future growth.

“It’s still not at the pre-COVID levels,” Shah noted, however. “Over the last two and a half years, there has been a lot of government support, and that has kind of led to some ebbing in demand, but we are seeing it return.”

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