Approve borrowers that other lenders are missing


Most traditional underwriting systems use fewer than 50 data points for credit decisions. The Zest Automated Machine Learning (ZAML) platform is an end-to-end underwriting solution that allows you to take advantage of machine learning and thousands of data points, at scale, and with speed and full transparency. With ZAML, you can more accurately assess thin-file and no-file borrowers—such as millennials—that traditional underwriting systems overlook.
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ZAML data assimilation connecting 4 circles representing disparate data pointsData
Assimilation

Rapidly discover, acquire, and onboard data sources at a massive scale.

Graph representing ZAML's modeling toolsModeling
Tools

Train, ensemble, and productionalize machine learning models in one streamlined workflow.

Open box representing ZAML's capability for modeling explainability Modeling Explainability

Unpack the “black box” of machine learning models to clearly communicate economic value and support compliance.

Machine Learning Can Increase Approvals, Cut Losses for Auto Lenders 

Move past the myths and misconceptions surrounding machine learning in auto lending

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3 Tips to Consider Before Using Alternative Data in Credit Underwriting 

It can be daunting to determine which data sources are worth evaluating. Here are some tips to consider before you begin your search for credit data providers.

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ZAML data sheet 

Learn more about ZAML's offerings

VIEW ZAML DATA SHEET

Explainable Machine Learning in Credit 

Explainability tools are crucial to overcoming "black box" concerns of machine learning

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Get started with ZAML today!

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