Author: Khushba Hayat
Title: Partner, Walee Financial Services (Hakeem).
Across many markets, women often access finance through someone else’s name. Before they can borrow money, they must first borrow identity. This “ownership by proxy” erodes digital trust and keeps women invisible to the very systems that should serve them.
Why identity by proxy persists
Lenders assess risk through paperwork and property. Yet much of women’s economic activity is informal: home-based services, seasonal work, community caregiving, micro-enterprise. These contributions rarely create the formal records lenders require. [Journal of Social Economics, 2022]
This exclusion persists despite evidence that women are often stronger borrowers, with repayment rates surpassing men’s in many contexts. [Empirical Economics Letters, 2020].
When income is not documented and assets are not titled in a woman’s name, she is told she does not “qualify,” even when she has the skills and discipline to repay. Collateral requirements deepen the gap. In many countries, social norms and inheritance patterns leave land and major assets in men’s names. Without titled collateral, women face tougher terms or outright exclusion. Mobility limits, unpaid care responsibilities, safety concerns, transport costs, and fixed branch hours make it difficult for women to access banks, leading to their quiet exclusion from the formal financial system.
Digital trust as the missing layer
Digital channels can lower costs and expand reach. They can also reproduce old biases if women cannot prove identity on their own terms, or if systems distrust their data as atypical. Digital trust means three things for women: being recognized accurately as account holders, being treated fairly by automated decisions, and being safe when interacting online.
What inclusive AI can do
For decades, lending decisions have been shaped by subjective judgment and social norms rather than policy, and studies show that bank officers are more likely to favor male over female loan applicants [IDB, 2020], underscoring the need for AI systems that reduce human bias.
AI mirrors its training data. If women are under-represented or mislabeled, models will encode that unfairness. When institutions shift to behavior-based features, consented data, and gender-aware evaluation, risk assessment becomes more objective and explainable.
Our goal in designing digital finance has been to reduce reliance on paperwork and in-person visits, which are disproportionately difficult for women. Through AI-assisted identity verification and digital KYC, onboarding becomes more secure, remote, and accessible. In our own experience, women have represented about 13 to 15 percent of borrowers, compared with an estimated 6 percent in the traditional market. While these figures are not the outcome of a formal study, they suggest that inclusive design choices such as remote onboarding can measurably increase women’s participation.
AI-enabled virtual servicing also matters. Through secure chat and guided flows, borrowers can apply, get questions answered, and manage repayments without visiting a branch. This saves time and can reduce exposure to stigma or gatekeeping that women sometimes report in in-person settings.
But AI is not a silver bullet
Tools that predict risk or flag anomalies can over-reach, especially if built on biased data that does not reflect women’s realities. Inclusive AI for women requires specific safeguards:
- Representation: Women should be part of the design, data, and decision reviews, not only as research participants.
- Data minimization: Collect the least sensitive data needed. Keep sensitive attributes off by default unless they reduce harm and the user consents.
- Explainability and appeal: Provide plain-language reasons for automated decisions and a simple path to human review.
- Fairness Reviews: AI systems often inherit male-centric patterns, which can misjudge women’s financial behavior. Regular reviews must catch and correct these biases so women are assessed on their own merits.
- Privacy and safety: Build for secure, discreet use. Recognize that women can face surveillance or coercion at home and offer controls accordingly.
Forthcoming pilot: Khudmukhtar Khatoon
AI can make financial processes more objective, transparent, and efficient, but technology alone is not enough. Women also need products designed around their specific circumstances and aspirations. Traditional lending models often overlook realities such as irregular income, lack of collateral, or career breaks, which continues to exclude many women.
To move from principle to practice, we are preparing a pilot called Khudmukhtar Khatoon (The Self-Reliant Woman). The aim is to test whether women-centered product design, combined with inclusive AI features, can expand women’s safe and confident participation in digital finance.
The pilot will use AI-enabled credit scoring and digital documentation that reduces reliance on male guarantors. It will focus on micro-entrepreneurs and emphasize assets-in-kind rather than cash, paired with simple, Shariah-compliant contracts. Design pillars include Urdu and English interfaces, consent-based data collection, and human-in-the-loop review for sensitive cases to ensure fairness and trust.
Identity abuse and accountability
Where identity is borrowed, accountability blurs. Applications are sometimes submitted in women’s names by male relatives, especially when men face credit constraints. This weakens women’s financial identity and can expose them to liability. Digital onboarding should therefore verify who is applying, confirm consent, and provide private, in-app notifications so the named account holder stays informed. Clear grievance channels are essential.
What the ecosystem can do now
- Regulators: Set policies that require AI models to be evaluated for gender bias, and mandate that women are given clear explanations and the right to appeal automated decisions.
- Platforms: Offer privacy-by-default settings, discreet notifications, and safety resources in local languages.
- Lenders and fintechs: Co-design with women, reduce documentation hurdles with digital KYC, and publish gender-disaggregated outcomes.
- Industry and academia: Build open, representative datasets and stress-test models for gender bias.
- Employers and investors: Increase women’s participation in AI product, data, and governance roles.
Women have always been economically active. They need systems that recognize them on their own terms. If we build AI that is inclusive by design, with women in the room and safeguards in place, we can replace identity by proxy with identity by right, and expand digital trust for millions.