The World Bank estimates that 1.4 billion individuals worldwide are unbanked, lacking access to credit due to the absence of traditional credit scores. In this article, the authors demonstrate how retail transaction data can be used to construct an alternative credit score, potentially expanding credit access for these individuals. The study utilizes a unique dataset obtained through a partnership with a Peruvian company. The authors merge customer loyalty data and credit card repayment data with administrative records from the Peruvian financial system that provide individuals’ detailed financial histories. This comprehensive dataset allows the authors to construct credit scores for people both with and without a credit history. Through simulations of credit card approval decisions, they find that incorporating retail data increases approval rates for individuals without a credit history, from 16% to between 31% and 48%. In contrast, for those with an established credit history, approval rates remain largely unchanged, at around 88%. The authors investigate why retail data particularly benefits people without a credit history and discuss the broader implications of this credit scoring methodology for consumers, firms, and policy makers. The findings highlight the methodology’s potential to transform credit access for millions of previously unbanked individuals.
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