Modernizing Access to Credit for Younger Entrepreneurs: From FICO to Cash Flow
Younger entrepreneurs are disadvantaged by the common practice of relying on personal credit scores to underwrite small business loans. In causal analysis exploiting within-applicant assignment to multiple lenders, we show that younger business owners have a higher chance of loan approval when the lender incorporates recent cash flows from business checking accounts. We present a new method—Tail Analysis for Comparative Outcomes (TACO)—to assess, in a nonparametric way, who benefits from alternative models. This method is especially useful for evaluating machine learning models. We show that incorporating cash flow data into default prediction models disproportionately benefits younger entrepreneurs.
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