将超市购物数据用于信贷决策

Many consumers across the world struggle to gain access to credit because of their lack of credit scores. This paper explores the potential of a new alternative data source, grocery transaction data, for evaluating consumers’ creditworthiness. Our analysis takes advantage of a unique, individual-level match of credit card data and supermarket loyalty card data. By developing credit scoring algorithms that either exclude or include grocery data, we illustrate both the incremental value of grocery data for credit decisions and its boundary conditions. We demonstrate that signals from grocery data can improve credit approval decisions, particularly for individuals who lack traditional credit scores. Furthermore, as a consumer establishes a relationship with lenders and builds a credit history, the marginal value of incorporating grocery data diminishes. These findings highlight the potential of grocery data in informing credit decisions and, consequently, in enabling financial institutions to extend credit to consumers who lack traditional credit scores.