Study Overview
Despite the promise of FinTech lending to expand access to credit to populations without a formal credit history, FinTech lenders primarily lend to applicants with a formal credit history and rely on conventional credit bureau scores as an input to their algorithms.
Study Results
Using data from a large FinTech lender in Mexico, we show that alternative data from digital transactions through a delivery app are effective at predicting creditworthiness for borrowers with no credit history. We also show that segmenting our machine learning model by gender can improve credit allocation fairness without a substantive effect on the model’s predictive performance.
Intervention: AI model that differentiates creditworthiness between men and women
Intervention Partner: RappiCard
IBSI Funding Acknowledgement: Lab for Inclusive FinTech (LIFT)
News & media
There’s an easy way to make lending fairer for women. Trouble is, it’s illegal.
November 15, 2019
Preliminary results from an ongoing study funded by the UN Foundation and the World Bank are once again challenging the fairness of gender-blind credit lending. The study found that creating entirely separate creditworthiness models for men and women granted the majority of women more credit.
Gender-Differentiated Credit Scoring: A Potential Game-Changer for Women
February 27, 2020
The Alliance spoke to Sean about this research and the significant impact the model potentially could have on women’s ability to access credit.