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How Block built a $200 billion credit operation by seeing customers traditional lenders can’t

  • Roughly 100 million Americans are invisible to traditional credit systems, either unscored, thin-filed, or misrepresented by data that doesn't reflect their actual financial lives.
  • Juan Hernandez of Block explains how the company used first-party data and alternative underwriting models to extend over $200 billion in credit across Cash App, Square Loans, and Afterpay — while keeping pricing low and access wide.
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How Block built a $200 billion credit operation by seeing customers traditional lenders can’t

The credit system in the US was built on a fundamental assumption: that past borrowing behavior predicts future risk. That assumption has left roughly 100 million Americans essentially invisible to lenders — no score, thin file, or a history that doesn’t reflect who they actually are financially today. The result is a system that compounds exclusion, requiring debt to unlock debt, and pricing risk so conservatively for anyone outside the norm that the cost of capital itself becomes a barrier.

Juan Hernandez has spent the last decade at Block building lending products for exactly those customers. As head of credit and underwriting, he leads the teams behind Cash App Borrow, Square Loans, and Afterpay — three distinct products serving consumers and small businesses that traditional underwriting models consistently misread or ignore. Block recently crossed $200 billion in credit extended to customers globally.

The engine behind that number is a data advantage. By underwriting from first-party signals native to the Cash App and Square ecosystems rather than relying on sparse bureau data, Block has built models that are both more accurate and more inclusive. Hernandez sat down with Tearsheet to talk about how they built a credit operation at that scale, what it takes to serve the underserved responsibly, and where the product suite is heading next.

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What the $200 billion milestone means for the customers block serves


 

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