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Home Finance How OakNorth is Using AI to Address SMB Lending

How OakNorth is Using AI to Address SMB Lending

Small enterprises appear to be left behind by banking institutions.

Stringent capital and risk requirements of the post-financial-crisis have undeniably created a gap in the small and medium-sized business (SMB) lending market that both marketplace and alternative lenders rushed to fill.

In the United Kingdom, the support for FinTech and challenger banking institutions to increase competition, particularly in the small business lending market.

With an already growing community made up of alternative finance (AltFin) firms, banking institutions are now allowing AltFin fill the SMB lending gap or even collaborating with and purchasing those FinTech companies in a bid to maintain their SMB clients.

However, a gap still exists. Even though the Goldilocks Conundrum provides simple reasoning of why delving more into the matter of small business bank leading shows a more complicated problem.

According to details from OakNorth an AI-driven challenger bank, both alternative FinTech companies and banking institutions are going through a hard time trying to deal with the obstacles of SMB lending.

READ MORE: 10 Applications of Machine Learning in Finance

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OakNorth’s Chief Operating Officer Amir Nooriala asserted that underwriting an SMB loan requires financiers to feed and evaluate unstructured data.

“In micro-lending, P2P, and retail, its structured data,” he said. “It’s easy to source; it’s rule-based. I look at your credit score and your bank statement and card statements, and the machine gives a ‘yes’ or ‘no.’”

However, the SMB industry provides lenders with loads of unstructured data in different formats. For instance, the case of a hotel that requires a $5 million loan for expansion.

That enterprise may boast financial data detailed down on Excel spreadsheets in both a format and structure that a banking institution has never witnessed before.

Data may be stored in physical bank branches. Furthermore, the information has to be translated in case the business is based outside a market made up of English speakers.

This case could translate to miscommunication or errors. However, this information plays a vital role when it comes to underwriting a loan.

“It’s going to be very bespoke to them,” Nooriala continued. “You need advanced technology to translate that data, to take unstructured data and structure it, and give meaning to it.”

Aside from this challenge, he added that banking institutions are challenged to evaluate tens of thousands of distinct market scenarios as well as abstract data points.

“You need artificial intelligence [AI] to run through the scenarios,” claimed Nooriala. “At the corporate level, that’s maybe 64,000 scenarios — you can only have a computer do that, and you need structured data to do that.”

For massive financial institutions (FIs), funding a global corporation with a loan makes the analytics process worthwhile.

However, for SMBs, the margins are particularly thin to have expert teams analyzing, standardizing and aggregating this data.

Competitive pressure stemming from FinTech companies, a wider understanding of that gap and encouragement from policymakers have made big banking institutions to venture back into the market, Nooriala asserted that traditional financial institutions must make a decision to determine whether they intend to facilitate FinTech firms in taking the reins.

Nonetheless, the pullback from SMB lending by big banks is not the only residual impact that the finical crisis had on this market, especially in the UK and other places. Nooriala says that the crisis has made conventional financial institutions open to trying out things.

“When there is a crisis shock to the market, it wakes people up,” he said — adding that, for some, it’s led to fear and protectionist action, while, for others, it’s forced FIs to examine how to stand out in a crowded market.

“Banks are looking at what they currently have and trying to figure out what they need to acquire an upgrade. Are they willing to accept that there are third parties out there that will do something better than they ever could?”

For institutions that are not willing to accept this, most are shifting to such third parties as collaborators.

For businesses such as OakNorth, which gives FIs the ideal technology for not only standardizing unstructured data but also automating underwriting processes through artificial intelligence.

The willingness in the conventional banking industry for exploring options has proven to be a positive impact from the financial crisis that affected the globe.

“What the financial shock events have done is … wake [FIs] up, as opposed to forcing them in a single direction,” said Nooriala. “It’s definitely opened doors. People want to try something new.”

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KC Cheung
KC Cheung has over 18 years experience in the technology industry including media, payments, and software and has a keen interest in artificial intelligence, machine learning, deep learning, neural networks and its applications in business. Over the years he has worked with some of the leading technology companies, building and growing dynamic teams in a fast moving international environment.
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