Combining technology and purpose | UCNA News

Of them CUNA Strategic Services Alliance providers enable credit unions to improve the financial well-being of their members by expanding access to low-dollar loans and using artificial intelligence (AI) to provide fairer loans on the social plan through alternative credit rating models.

QCash Financial, a credit union servicing organization formed by $4.6 billion in Washington State Employees Credit Union (WSECU) assets in Olympia, Washington, enables credit unions to make small unsecured loans in less than 60 seconds without using a credit score.

QCash has refined its decision engine after more than a decade of experience in lending, data analytics, automated underwriting and real-time funding. This gives struggling members an alternative to high cost predatory lenders.

“What we’re really talking about is when a member is in a life event; when an emergency strikes,” says Denise Wymore, QCash Marketing Manager.

In a 2021 survey, WSECU learned that 59% of its members had used QCash loans for living expenses or family emergencies.

QCcash uses a relational credit score model that takes into account member status and eligibility, deposit history and relationship with the credit union.

It is based on the credit union model that has been relied on for years with traditional select employee groups. “That’s what a loan officer did in the 1980s,” Wymore says. “It was a question of character, ability and guarantee. It’s the automated 80s without any FICO scores.

WSECU learned that it could provide loans to low-credit members who might not have been approved by traditional scoring methods, she says.

Members use the loans for auto repairs, bill consolidation, unemployment, the birth of a child and other life events, Wymore says. “Without these loans, we know where they would be: at a payday lender.”

“By replacing high-risk borrowers with low-risk borrowers, you can approve many more borrowers without increasing risk.”

Teddy Flo

Artificial intelligence

Zest AI uses machine learning and AI to produce more accurate and fair credit scores when making loans to members, says Teddy Flo, Chief Legal Officer.

“We use compliant data sources to produce a fairer risk score for women and people of color,” he says. “It can transform credit union technology, but more importantly, the lives of credit union members.”

Traditional credit scoring models essentially divide loan applicants into two groups, “good” and “bad” applicants, with a straight line based on their ability to repay.

With more advanced algorithms, machine learning provides a more nuanced line that considers more factors and makes fewer errors than the traditional linear system, Flo says.

Machine learning and AI not only approve more borrowers, but they provide greater statistical accuracy regarding their ability to repay, he says. Additionally, machine learning and AI scores are a more accurate indicator of a borrower’s ability to pay during an economic downturn.

“By replacing high-risk borrowers with low-risk borrowers, you can approve many more borrowers without increasing risk,” Flo says.

Perhaps most importantly, Zest AI has developed scoring models that are more accurate and fair in loan approvals for underserved communities, including Black, Hispanic, Native American, and older consumers.

Overall, the final model can increase approvals by 35% in these population segments.

Flo highlights a credit union that saw a 38% increase in loan approvals with no change in its risk profile.

“Credit unions now have more choice,” he says. “They can take a huge leap in statistical accuracy and become much fairer in their lending practices. This is essential for credit unions to fulfill their mission.

Flo and Wymore both note the unintended zero-sum effect of loan application denial: members who are denied loans are very unlikely to return for another opportunity – a reality that no agent escapes. experienced credit.

“But when you approve them, especially when they’re turned down by other providers, they remember them for life,” says Flo. “So when you think about the lifecycle benefit, the returns for the member and the credit union – using cutting-edge technology – are astounding.”

This article is part of Tech22, CUNA News’ special focus on innovations and technological developments. Follow the conversation on Twitter via #Tech22.

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