- Stratyfy uses AI to help lenders extend more credit to underbanked communities.
- The startup raised $10 million on March 29 from Truist Ventures, Zeal Capital Partners, and others.
- Here’s the 13-page pitch deck Stratyfy used to raise its so-called “institutional seed.”
Laura Kornhauser, a former executive director at JPMorgan Chase, was surprised when she received her adverse action notice informing her she had been denied credit.
Kornhauser had been invited to apply for credit multiple times while completing her MBA at Columbia Business School in the fall of 2016. But because she was a full-time student, she didn’t have a lot of current income.
She phoned the 1-800 number on the back of the notice, and after providing additional information to the rep, Kornhauser got the lender to approve her.
“It made me really recognize that there are these huge pockets of our population that are just basically rejected by everybody,” Kornhauser told Insider.
People with lower Fico scores, those who don’t have a long credit history in the US, or gig economy workers who don’t get a traditional W-2 from their employers all fall into a bucket that are not “effectively seen” by the way many lenders evaluate potential borrowers, she added.
The experience prompted Kornhauser to start a business that helps lenders understand “real risk versus perceived risk” of individuals and small businesses, she said. A few months later, Kornhauser and her co-founder, Dmitry Lesnik, founded Stratyfy.
On March 29, the startup raised $10 million in a so-called “institutional seed,” which falls between a traditional Seed and Series A, Kornhauser said. The round was co-led by Truist Ventures and Zeal Capital Partners, with participation from FIS, Mendon Venture Partners, and The 98. Kornhauser declined to disclose a valuation.
The startup was also previously identified as an up-and-coming fintech by insiders for a list Insider originally compiled last fall.
Stratyfy works to help lenders eliminate bias
Stratyfy uses machine-learning algorithms to help lenders remove sharp cutoffs — like those based on credit history lengths or current income — that are often used in their credit decisioning.
A lot has been written about how biased data can skew automated underwriting. Stratyfy’s product strives to give lenders the tools to uncover whether there is bias, and then helps them pinpoint what’s dragging that bias. Doing so will allow the lender to make changes so that the bias isn’t propagated forward in their ongoing decisions, Kornhauser said.
The core engine uses a lender’s traditional credit data, in addition to alternative data, to offer a breakdown of factors in a given borrower’s profile. So if Kornhauser was denied credit because of her low current income, the model could spotlight other factors, such as rental or utility data, to compensate for other areas of risk.
Stratyfy works with big banks, fintech lenders, and insurance companies across consumer and small business. The startup has also teed up a partnership with a main core banking provider, which can help Stratyfy reach community banks and community development financial institutions, Kornhauser said.
The capital will be used to build out Stratyfy’s team from 18 to 25 by the end of the year, with hires concentrated in engineering and data science, as well as marketing, Kornhauser said.
One case study with a US-based lender using historical data found that the lender was only accepting 30% of “good borrowers,” or those who later paid back their loans. With Stratyfy’s core engine, the lender was able to approve 72% of good borrowers, while having a small reduction in expected default risk, Kornhauser said.
Here’s the pitch deck Stratyfy used to raise $10 million.
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