Who We Help

We enable the subprime lending industry to make automated credit decisions with more insights of thin-file and no-file consumers. This is Credit Bureau 2.0 ®.

Want to see how we can help you boost your lending? 

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Auto Lenders

Both direct and indirect lenders face unique challenges when making underwriting decisions. It's not just about the borrower risk - it's about the vehicle, the deal structure, and the effectiveness of your collections process/skip-tracing. Our credit scoring machine learning models consider the loan data required so you can make the best underwriting decisions.

Are you an auto lender? 

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blue_lady_background_jobbies (1) Male hand holding car keys offering new blue car on background
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blue_lady_background_jobbies (1) Portrait of a smiling man checking bills on the tablet at home
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Short-Term/Single Repayment Lenders

With growing regulation and uncertainty, it is becoming more and more evident that short-term lenders need to make smarter underwriting decisions to remain profitable in the non-prime space. Our credit scoring machine learning models combined with our vast library of alternative data sources enables short-term lenders to automate profitable lending decisions in a matter of seconds.

Are you a short-term/single repayment lender? 

Contact Us Today
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Installment Lenders

Regulation is forcing installment lenders to make smarter decisions in the non-prime market. While loan margins decrease, profitability can be maintained by increasing loan originations and decreasing defaults. By implementing our credit scoring machine learning models installment lenders have the ability to make profitable underwriting decisions and automate the majority of their lending process. We help you gain insights from loan data that includes social media data, mobile data and other financial inclusive data.

Are you an installment lender?

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blue_lady_background_jobbies (1) Neon sign in window of neighborhood loan shop
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Interested in Working at Trust Science?

Check out our careers page for openings.

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