New Risk Scoring Model Opens Credit Doors for Small Businesses Banks Leave Behind

Using alternative data to underwrite business loans to small- to medium-sized businesses (SMBs) isn’t new, whether pulling from social media or using other nontraditional means.

Now, artificial intelligence (AI) is expanding the universe of alternative data sources — from bill pay history to community standing — opening up new avenues for lending and giving financial institutions a new way to more inclusively assess the risk of lending money to SMBs and microbusinesses.

While major lenders are still enamored with FICO scores and typical proofs of viability, the idea of accessing credit risk through new, different kinda of business signals and datasets is a new type of SMB lending.

As Uplinq Financial Technologies CEO and Founder Ron Benegbi recently told Karen Webster, “Today, the majority of these small businesses are [owned by] minorities or immigrants, like I am, and they formed because of the devastation that COVID has had.”

“They’ve just put up Shopify stores, and they don’t have three years of financial data,” Benegbi continued. They certainly don’t have credit to establish themselves as business. It’s a very, very difficult situation.”

That situation calls for businesses to view this risk through a new lens.

Benegbi explained that credit scores and financials “tell you a consequence, but they don’t tell you anything about the business itself and the situation of the business, the world in which it lives, the community that it surrounds itself with, its suppliers, its partners, its customers.”

Established lending channels also don’t account for pandemics, trade wars and inflation when sizing up unproven SMBs for loans. However, there are other “environmental signals” that work well when you combine them with purpose-built artificial intelligence (AI) that can read them.

Saying the two met by chance, Benegbi notes that the AI-powered decisioning platform now part of the Uplinq solution, orginally developed by Pat Reilly, CEO of Verde International, is the heart of a very different type of business risk scoring methodology.

Who benefits? “Our end customer is any organization that lends money,” he said, “however, we’re looking to partner with a bureau, or … with other FinTechs in the open API economy who ultimately serve some type of small business lender. Who garners the benefit out of our product is the small business lender themselves, and of course, the small business owner.”

See also: APIs Unlock Financing For Underserved SMBs

Reading the Signals

Considering alternate data sources for business loan decisioning opens a fascinating window into how fresh viewpoints and a powerful AI can afford otherwise missing insights.

As an example, Benegbi explained that analyzing an SMB’s use of electricity over time and whether the utility bill was paid in a timely way is a statistically revealing metric.

Looking just at electricity usage and payments, he said Uplinq finds “the probability that [the] small business will default on a loan is cut in half” — from 8% to 4%” — yet “it is highly probable that that small business will get declined” for lack of a good FICO score or sales history.

“That’s one example, but we look at so many different factors like macroeconomic, stock markets, commodity markets, supply chain, labor markets, demographic information, traffic counts and community,” Benegbi continued.

Business loan decisions based on community reputation is another uniquely unconventional metric Uplinq takes into account during the lending process.

“Community is a really big signal in a market like the Middle East, and is a really big signal in Latin America,” he said. “What the models look at is how active is that small business in their local community, because there’s an incredible cultural shame to not fail and to not rip people off in certain markets. So, there are things like that.”

Simply put, a small business that pays its utility bills on time and is respected in the local community is less likely to default on a business loan.

Making it clear that Uplinq also factors in FICO scores and any available financial information, Benegbi added that “through the acquisition of the AI/IP, we’re integrated into the six largest core banking platforms in the world,” through which 95% of all loans globally pass, affording additional risk insights.

See also: B2B Investors Turn Spotlight On Commercial Cards, Alt-Lending

Trust and Validation

As the company is emerging from stealth, getting lenders to trust Uplinq’s data and assessments of SMB loan risk is a hurdle it faces, although Benegbi isn’t concerned.

“At the end of the day, [it’s] science and validation,” he told Webster. “It is highly, highly scientific. We can go to a bank, to a lender and say, ‘If you don’t believe us, here is the science, we’ll do all the back testing, we’ll prove it to you,’ whereas some of the analytics companies out there today are guessing. They don’t have that. Our data is regulatory compliant.”

As an evolution and enrichment of risk scoring for commercial lending, Webster noted that large legacy banks and specialized FinTech lenders would both benefit from the approach.

“I’ve seen a lot of interest come from parties that I didn’t think initially would be high on the list,” Benegbi said, invoking credit reporting bureaus and “maybe one of the biggest credit card issuers on the planet.”

“Organizations that we can collaborate with and ultimately take a novel product, a novel data solution to their customers in order to transform the lives of millions of millions all over the world,” Benegbi continued. “That’s the impact we’re looking to make.”

See also: Data Progresses SMB Finance Beyond The Legacy Business Credit Score