More than 45 million U.S. adults lack traditional credit histories, creating a gap that alternative financial service data, such as payday lending records, could potentially fill. Using the staggered adoption of the largest alternative credit database, we examine the data’s impact on automotive lenders in the subprime auto loan market. Alternative credit scores predict loan performance, leading lenders to offer better loan terms to higher-scoring borrowers. However, a history of using alternative financial services, even with relatively high alternative credit scores, comes with significant downsides: borrowers with payday loan histories experience higher delinquency rates, face higher interest rates, and reduced loan origination rates after the adoption of alternative credit data. A flexible machine learning model indicates that only 3.28% of alternative financial service users possess sufficiently strong credit histories to offset the stigma of using these services. Consequently, use of alternative credit data limits credit availability and raises traditional loan costs for most users of alternative financial services. Alternative financial services are used more frequently in lower-income areas and communities with higher shares of black residents, raising concerns that the adoption of alternative credit data may have disproportionate negative impacts on these populations. Our results contribute to the policy debate on credit data, consumer privacy, and financial inclusion.