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Gender-Differentiated Credit Algorithms using Machine Learning

Financial Inclusion Dominican Republic

Low-income women disproportionately lack access to credit, often because they lack credit histories, property rights, and formal earnings. This study seeks to understand 1) whether gender-differentiated credit scoring models using non-traditional data (e.g. mobile phone call detail records) can increase women’s access to formal credit, 2) whether women rejected by standard credit scoring models would benefit from credit access along the dimensions of asset ownership, labor supply, risk coping, intra-household bargaining power, and intra-partner violence, and 3) how these benefits compare to the benefits of credit access for women selected by standard models. Partnering with a large telecommunications company and a large bank in the Dominican Republic, this study combines novel algorithm development with an impact evaluation of credit allocated based on the novel algorithm. The findings will contribute to lowering barriers to financial inclusion and gender equality. Results forthcoming.

All PIS on Project
  • Asociación La Nacional de Ahorros y Préstamos
  • Claro Dominicana
  • IPA (Dominican Republic)
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Financial Inclusion

DCO Quarterly Newsletter: February 2020


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