Low-income women disproportionately lack access to credit, often because they lack credit histories, property rights, and formal earnings. This, in turn, leads to a cycle of exclusion from formal credit markets, as a lack of data to assess low-income women’s creditworthiness prohibits them from building a credit history. Exploring partnerships with financial institutions, the research team aims to augment machine learning credit scoring algorithms used to disburse digital credit by gender-differentiating them. Specifically, our model uses machine learning algorithms to sift through and transform a broad range of characteristics from existing low-income clients’ mobile phone data to determine the best predictors of creditworthiness separately for men and for women. Preliminary analysis suggests that, relative to a pooled (across men and women) model that includes gender as predictor, the gender-differentiated model would assign a higher predicted probability of repayment to 80% of women. Thus, low-income women would be more likely to receive credit and gender equity would be improved if lenders used a gender-differentiated model. A randomized control trial will permit the research team to evaluate the impact of credit access on women’s outcomes (and those of their businesses) among those who would not have been approved by traditional models but would be approved by the machine learning model.