Aire, the U.K. startup that wants to give the credit scoring system a 21st century “upgrade,” has raised $11 million in Series B funding. The round is backed by European enterprise VC Crane Venture Partners, with strategic investments from Experian Ventures and Orange Digital Ventures.
Existing investors White Star Capital and Sunstone Capital also followed on, while the company says it will use the additional capital to support “rapid growth,” including U.S. expansion. Aire also plans to further invest in the technology powering its credit insights engine, which aims to make credit checking fairer for consumers who may have a thin credit file, and therefore more valuable to lenders.
“How does a new borrower bypass the catch-22 problem of credit where it takes a while to get a history, but you need credit to start a history…,” says Aire co-founder and CEO Aneesh Varma, when asked to describe the problem the startup set out to solve.
“The system today doesn’t seem to serve everyone, even if they are deserving. Our solution focuses on an approach: The consumer is the best and deepest source of real data about themselves. This first-party data is the only way… [to] deliver win-win outcomes for both the consumer and the lender.”
To solve this conundrum, Varma says Aire can be likened to the role of a “manual underwriter” who tries to better understand a credit applicant’s life and financial situation, but delivered via technology in an automated and scalable way.
“Our main product today steps in to engage with an applicant on a lender’s website when the existing decision engine is unable to reach a full decision,” he explains. “We enable the consumer to supply relevant financial data to us about their circumstances. This is beyond just transactional banking data, and therefore gives us a full picture… looking forward, not just the historical snapshot.”
On the backend, Aire’s platform accesses that data to provide ready-to-use outputs for its lending partners to use in real time. The system is designed to get smarter over time, too, as more performance data of outstanding loans becomes available.
“[This is] where machine learning is very relevant. We also keep researching other methods and data streams that consumers can bring to us, while being on the right side of the privacy concerns,” says Varma.
One of the challenges faced by any company wishing to upgrade credit scoring by employing new data points and machine-learning is not to replicate the existing biases that are arguably ripe within the current system. This is something Varma says he and Aire take very seriously, having experienced some of those prejudices first-hand himself.
“We are very insistent on a strong model governance process to ensure we are not biasing against certain individuals or protected traits. This is welded into our culture at Aire,” he says.
“First you have to know what are the biases that exist in the current system that you need to tackle. And then it requires doing the grunt work to involve real human checking and cross-calibrating the models… The challenges we are seeing with algorithms with big tech today are often because some of these companies taking the easy road out. They need to walk in that uncomfortable forest. It’s essential.”
To date, Aire says its algorithmic model has scored more than $10 billion of credit across various consumer credit categories, which it reckons gives the startup a competitive advantage as the model improves with both data quantity and quality. The company claims to help lenders access more customers without increasing risk appetite, and says it has seen credit approvals increase by up to 19 percent.
On the lender side, Aire’s customers are credit card companies and retail finance (i.e. checkout financing), although Varma says longer-term finance is also on the road map as the company’s models mature. On the consumer side, Aire typically serves working professionals who are earlier in the financial journey. These include various types of self-employment, such as contractors, freelancers and those operating within the so-called gig economy.
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