PHOTOS ( ABOVE AND FACING PAGE ABOVE ) BY DAVID ESTRADA LARRAÑETA
Visibility for the ‘ credit invisible ’ Lenders in the U . S . and abroad have long relied on credit scores to determine whether someone is eligible for a loan , mortgage or other credit product . However , if you have little or no credit history , you may not have a score at all . In the U . S ., “ credit-invisible ” consumers are more likely to be people of color and low income . But it ’ s not just a problem stateside . Unbanked and underbanked consumers in countries all around the world face the same challenge .
“ I come from a family of merchants , so I was really familiar with the informal economy in Latin America ,” says Viviana Siless , Quipu co-founder and CTO . Most of the Quipu borrowers are women , and they run businesses out of their homes , such as small restaurants or shops , to support their families . “ For me , it ’ s really touching to help these entrepreneurs .”
The vast majority of Quipu ’ s borrowers “ face a lack of the money and liquidity that ’ s tied to structural poverty , and they can ’ t access formal credit ,” Constain says . He founded the company
Above / Natalia Alzate and
Beatriz Alzate use a small metal with Siless and Mercedes trolley to sell juices , breads and
Bidart , Quipu ’ s CEO . coffee they ’ ve prepared at home . Customers include community
The company ’ s first members , mechanics and laborers product was an online who work in the city center .
marketplace for microentrepreneurs . The founders wanted to give the entrepreneurs more visibility and the opportunity to build up their credit scores . They realized that data related to borrowers ’ businesses was far more useful for determining creditworthiness than their personal information and credit history .
So Quipu asked merchants to upload information and videos about their products , customers and sales . With permission , they connected to their merchants ’ devices and analyzed their digital footprint . “ Our evaluation is not only what ’ s in your bank account , but it ’ s a lot of information users report , and that also comes from their networks ,” Siless says .
Using ML algorithms , the startup analyzed more than 80,000 pieces of metadata to determine patterns that made merchants successful . Quipu found that users most likely
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