In a successful trial, Discover and Zest found that the inclusion of more data and more sophisticated math reduced default rates significantly with no portfolio risk.
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ZAML tools help you build models with much higher precision so you can eliminate bad borrowers and slash charge-off rates while keeping approvals steady.
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A leading e-commerce company wanted to extend credit to its customers, but lacked the expertise and data. It turned to Zest to build a new machine learning underwriting model to accurately score consumers that lack a traditional credit profile.