How better data can advance gender equality in finance

Progress on women’s financial inclusion has accelerated, yet material gaps persist in access, usage, quality and outcomes. Hopes are now being placed in artificial intelligence to power further progress. But growing evidence actually shows that AI may distort data sets, reinforce gender biases and in fact produce harmful patterns that disadvantage women — alarming signals that regulators, advocates and financial industry executives all must be more vigilant about how emerging AI applications might worsen, rather than ameliorate, discrimination.

Better data on gender in finance is essential, not only for improving AI training and tracking its impact, but also for crafting and evaluating other policies and initiatives aimed at closing the financial inclusion gaps. Keeping a close watch on gender‑disaggregated data is a critical policy and market instrument to move from headline access numbers to meaningful, equitable outcomes for women. That’s been underscored by recent evidence showing how it translates into better outcomes. This article presents some contemporary case studies of successful gender-disaggregated data outcomes and what regulators, central banks and governments can do now to drive improved economic outcomes for women.