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FinTech Lending to Borrowers with No Credit History
Despite the promise of FinTech lending to expand access to credit to populations without a formal credit history, FinTech lenders primarily lend to applicants with a formal credit history and rely on conventional credit bureau scores as an input to their algorithms. Using data from a large FinTech lender in Mexico, we show that alternative data from digital transactions through a delivery app are effective at predicting creditworthiness for borrowers with no credit history. We also show that segmenting our machine learning model by gender can improve credit allocation fairness without a substantive effect on the model’s predictive performance.
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