There's an easy way to make lending fairer for women. Trouble is, it's illegal.

In partnership with a commercial bank in the Dominican Republic, the researchers conducted two separate analyses of 20,000 low-income individuals, half of them women. In the first analysis, the researchers used the individuals' loan repayment histories and gender to train a single machine-learning model for predicting creditworthiness. In the second analysis, the researchers trained a model with only the loan repayment data from the women. They found that 93% of women got more credit in this model than in the one where men and women were mixed together.