Artificial intelligence has enormous potential to broaden participation in the financial system for the unbanked and under-banked. But its contribution will depend entirely on the choices institutions make about data, governance, design and human oversight.
This was the key message from the Future Skills Forum in Singapore during a panel discussion focused on women in AI. Serey Chea, governor of the National Bank of Cambodia, Tancy Tan, chief operating officer of HSBC Singapore, and Jessica Rusu, chief data, information and intelligence officer at the UK Financial Conduct Authority, examined how AI is reshaping access to financial services and what this means for the future of financial inclusion.
Historical data as a barrier to inclusion
AI models are only as inclusive as the information they learn from. In finance, historical datasets reflect decades in which participation in the financial system was uneven. Women, informal workers and lower-income communities tend to have thinner credit files and fragmented financial histories. If AI systems are trained solely on such information, they will naturally reproduce these patterns and classify exclusion as statistical normality.
This is not an aberration. It is the logical consequence of training predictive tools on unequal data. The risk is that financial exclusion becomes more systematically embedded through automation.
Synthetic data provides one way to counter this. By generating credible but rebalanced datasets, institutions can help AI systems detect genuine creditworthiness among groups who were previously overlooked. This enables financial models to incorporate potential rather than simply magnifying historical disadvantage.
AI literacy as a foundation for responsible inclusion
A central theme of the discussion concerned the level of understanding required to use AI safely and effectively. AI literacy is too often assumed to be a purely technical competence, but its relevance for financial inclusion is far wider. It encompasses the capacity to interrogate data quality, recognise when model outputs are inconsistent or biased and understand the conditions under which an algorithm may produce misleading results.
The danger arises when organisations approach AI as if it were an objective decision-maker. If practitioners cannot clearly articulate what a reasonable outcome should look like, they lack the ability to challenge or contextualise algorithmic recommendations. In the realm of financial inclusion, this can have profound consequences, particularly for consumers who may already be on the margins of formal finance.
Strengthening AI literacy across institutions is therefore not simply about operational efficiency. It is about embedding the reflective capabilities required to use technology in a way that identifies risk, protects vulnerable customers and ensures that innovation supports inclusion rather than narrows it.
The value of human diversity in financial technology
Another key point highlighted was the relevance of human diversity to the design and governance of AI. Empathy, contextual reasoning and emotional intelligence are not technical traits, yet they play an important role in understanding how automated decisions affect people who may have limited or fragile engagement with formal financial services.
Diversity beyond gender also matters. Individuals from different cultural, socio-economic and disciplinary backgrounds approach problems differently and notice patterns that others might miss. In AI design, these varied perspectives help reduce blind spots and temper the risk of embedding one-dimensional assumptions into systems that will be used by broad populations.
Diversity therefore becomes a practical asset in creating financial technology that is more aware of the realities of those it aims to serve.
Frameworks that embed responsibility and trust
The panel discussion referenced the Monetary Authority of Singapore’s FEAT principles, which emphasise fairness, ethics, accountability and transparency in the development and use of AI. Frameworks of this kind give institutions a structured approach for monitoring how data are used, evaluating whether model outputs differ across population groups and ensuring there is a clear chain of accountability for automated decisions.
For financial inclusion, this kind of structure is vital. It clarifies expectations and establishes a shared language for examining whether AI systems support equitable access to financial services.
Financial inclusion is inseparable from trust. Individuals are more likely to engage with digital financial services when they believe that systems are fair, transparent and safe. Ethical AI regulation plays a central role in establishing this trust by ensuring that automated decisions can be explained and reviewed.
Regulations that emphasise transparency and responsible use are not obstacles to innovation. They create the conditions for inclusive digital finance by making sure that new technologies do not exacerbate existing inequalities or introduce new ones.
Designing a more inclusive financial future
AI is providing a significant opportunity to make financial systems more accessible and more responsive. It can help identify underserved customers, personalise product offerings and simplify the entry points into formal finance. However, none of this guarantees inclusion. Technology becomes a leveller only when accompanied by intentional design, rigorous oversight and an understanding of the social context in which financial decisions are made.
The conversation in Singapore emphasised a fundamental truth. AI may transform financial services, but it will reflect the values and assumptions of those who shape it. The future of inclusive finance will therefore depend on decisions taken now about data integrity, institutional culture, regulatory frameworks and human expertise.
If these elements are aligned, AI could become one of the most powerful tools for expanding economic opportunity in a generation.
Ben Rands is Managing Director of Operations and Marketing.
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