Innovation alone does not guarantee inclusion: what makes the difference is how a system uses the information available to shape choices, products, and policies. For digital finance to be truly open to women, we need adequate infrastructure, transparency, and shared criteria for collecting the data that inform decision-making
Digital finance is transforming the way people save, borrow, insure assets, and engage in economic life. From mobile wallets to algorithm-driven credit scoring, it is often portrayed as progress for everyone, a space where technology smooths out differences. Yet behind this promise lies a quieter problem: women’s financial lives remain largely invisible in the data that inform regulation, innovation, and automated decision-making.
This invisibility produces a structural form of exclusion, commonly described as data inequality. It does not arise from technology itself, but from decisions about what is measured, whose experiences are recorded, and whose realities remain outside formal systems of observation. When women are missing from data, inequalities are not only overlooked, they are reproduced under the appearance of neutrality.
The assumption that digital tools are inherently impartial does not hold up when outcomes are examined more closely. A growing body of evidence shows that algorithms tend to mirror the biases embedded in the data on which they are trained. In Kenya, for example, a credit algorithm considered gender neutral systematically offered women smaller loans, despite their stronger repayment performance compared to men. This case illustrates how the absence of sex-disaggregated data makes it harder to detect and correct distortions that directly shape people’s economic opportunities.
The challenge extends beyond individual algorithms to the quality of information available at the global level. The 2024-2025 pilot of the International Monetary Fund’s Financial Access Survey marked a significant step forward by introducing more than 100 new fintech indicators, many of which are disaggregated by sex. Yet only a small group of participating countries provided complete gender data. As a result, large blind spots remain in understanding how women and men use digital wallets, mobile money, and online credit platforms.
Country experiences show why visibility matters. Chile offers one of the most advanced examples. For more than two decades, the financial supervisor has published sex-disaggregated data on loans, deposits, and other financial products. This continuity has made it possible to observe lower delinquency rates and lower levels of non-performing loans among women borrowers, and to use this evidence to scale targeted programmes such as CreceMujer. Over a three-year period, the programme expanded its portfolio by approximately one-third without any deterioration in credit quality. Visibility in data translated into confidence in policy action.
Mexico follows a different but equally instructive path. Mandatory administrative reporting by financial institutions is combined with a large national household survey, the ENIF, which captures behaviours and perceived barriers. The data reveal a consistent pattern: women are more likely to hold basic accounts, often linked to public programmes, while they use less credit and fewer insurance products. These findings informed a revision of prudential rules in 2021, which reduced provisioning requirements for certain loans granted to women, explicitly recognising their stronger repayment performance. Gender data did not remain descriptive; it became a basis for regulatory change.
Kenya presents a contrasting picture. Digital financial services are widespread, and the gender gap in formal access has now narrowed to below two percentage points. Mobile money has played a central role in this achievement. However, most of the detailed data generated by private providers are not shared with public authorities; regulators continue to rely primarily on household surveys conducted every three years. In a fast-moving digital ecosystem, such intervals are too long to capture emerging risks, new vulnerabilities, or subtle forms of exclusion. In a technologically advanced setting, the absence of a continuous public data infrastructure leaves critical developments unseen.
Taken together, these cases point to a common lesson: innovation alone does not guarantee inclusion. What matters is how financial systems use information to guide supervision, product design, and policy choices. When gender data are missing or partial, even sophisticated digital tools operate with a limited field of vision. And this is not simply a technical shortcoming: in many economies, including advanced ones, the gender dimension is still not systematically embedded in the data that inform financial decision-making.
Europe itself illustrates this paradox. Despite having a strong technical capacity and formal commitments to equality, core supervisory systems, such as AnaCredit and standard reporting templates, do not include sex as a variable. As a result, policymakers lack one of the most basic pieces of information needed to assess differences in outcomes between women and men. Technology is advancing rapidly, but data practices are lagging behind, turning inclusion into an aspiration rather than a measurable objective.
Across contexts, one pattern stands out: policies are more effective when sex-disaggregated data are treated as a stable pillar of financial governance. This requires clear mandates that make gender reporting a requirement rather than a voluntary exercise; digital infrastructures capable of collecting and harmonising data in a reliable way; and transparency, because data that are not shared cannot generate accountability or change. Where these elements align, visibility becomes the basis for corrective action.
This need becomes even more pressing in the age of artificial intelligence. In Europe, creditworthiness assessment systems are classified as high-risk applications and are subject to obligations on bias monitoring and human oversight. Without sex-disaggregated data, it is impossible to assess whether automated systems produce different outcomes for women and men, or to intervene when they do.
Ultimately, the conclusion is straightforward: data are not a metaphorical ally for women in finance; they are an institutional one. When collected and used systematically, they turn recognition into representation and information into inclusion. Moving from data to action is what enables digital finance to become not just more efficient, but also more just and genuinely shared.
