Banks and credit unions want to do more to reach underserved communities. Still, they are wary of lending to borrowers they view as risky, such as people of color, people without a college degree, recent immigrants, and many of the 40 million “credit invisible” Americans. Generations of discrimination and rejection have caused many minorities to avoid seeking credit at all. But a lack of credit history doesn’t make someone riskier than someone with a robust file.
There are new strategies to ensure fairer lending. Zest Ai, a fintech software company based in California presents its suggestions in this short publication:
- Using machine learning to break the circle.
- Automating the search for the fairest models.
- Providing consumers with more accurate denial reasons.
- Building a better yardstick: using ML to improve race prediction.
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