AI for Inclusive Growth: Moving from Possibility to Reality
In less than 30 months, Artificial intelligence (AI) has become one of the most transformative technologies on Earth. This is also true in financial services, with the potential to extend financial access, reduce costs, and improve decision-making at an unprecedented scale. But this promise is not new. Those of us who have been building digital financial systems for decades know that technology alone does not transform lives – context, execution, and trust do. AI offers extraordinary possibilities, but it must be anchored in a deep understanding of the challenges and opportunities that have shaped financial inclusion efforts over the past decades.
The Need – Why AI for Inclusive Growth?
Despite significant advances in digital finance, billions remain excluded from formal financial systems. According to the World Bank, over 1.4 billion adults remain unbanked globally, with the majority concentrated in emerging markets. This exclusion is not just a financial challenge but a barrier to economic growth, social mobility, and resilience. For small businesses, farmers, and low-income individuals, financial exclusion limits their ability to invest, grow, and recover from economic shocks. It traps entire communities in cycles of poverty.
AI presents a powerful tool to break this cycle, but only if we understand the unique barriers facing underserved populations. These include high transaction costs, limited credit histories, weak digital infrastructure, and, crucially, a lack of trust in digital financial systems. For AI to drive true financial inclusion, it must do more than automate existing processes or reduce costs – it must create meaningful financial relationships that can withstand the challenges of emerging markets.
The Art of the Possible – What AI Can Actually Do
AI is not a monolithic technology. It spans a spectrum from simple automation to complex, self-improving systems. This flexibility is its greatest strength but also its greatest risk, as poorly designed AI can reinforce biases, exclude vulnerable groups, or even harm the very communities it aims to serve.
Done right, AI can help institutions reach and engage clients at every stage of their journey, from awareness to retention. Consider a layered approach: At the foundational level, AI can improve efficiency by automating routine tasks like KYC, credit scoring, and customer support. This alone can reduce cost-to-serve and improve financial resilience by making every dollar of capital more productive. For example, AI-driven credit scoring models can unlock access to capital for small businesses and first-time borrowers by using alternative data sources – mobile transactions, utility payments, and even psychometric profiles – to assess creditworthiness more accurately than traditional models.
At a more sophisticated level, AI can enhance customer engagement through natural language processing (NLP) and machine learning models that can understand and respond to client needs in real time. Imagine a rural entrepreneur receiving voice-based financial advice in their local language or a smallholder farmer getting predictive insights on crop yields based on satellite data. These are not just tech fantasies – they are achievable realities, as demonstrated by forward-thinking financial institutions that are deploying AI to personalize financial products and strengthen customer loyalty through dynamic pricing, early warning systems, and hyper-personalized customer journeys.
Moreover, a responsible AI strategy emphasizes the critical importance of building systems that are not only efficient but also transparent, ethical, and adaptable to the unique needs of underserved communities. This approach moves beyond basic automation to create truly intelligent systems that can anticipate customer needs, proactively manage risk, and continuously improve based on real-world feedback. It also highlights the need for AI systems that can evolve alongside customers, integrating both financial and non-financial data to deliver meaningful, context-aware insights. This is where AI has the potential to redefine financial inclusion – not just by reducing costs, but by fundamentally reshaping how financial services are designed, delivered, and experienced.
The Human Element – Managing Change from Within and Beyond
For AI-driven financial inclusion to succeed, change management must extend beyond the walls of the organization. Internal readiness is critical, but so too is the capacity and willingness of customers to engage with digital financial tools. For many in emerging markets, the shift to digital finance represents not just a technical transition, but a cultural one – moving from cash to digital, from face-to-face relationships to algorithm-driven interactions.
This requires financial institutions to invest in building digital literacy, fostering trust, and creating user experiences that reflect the real-world contexts of their customers. It means training staff to guide customers through this transition, ensuring that new systems are intuitive and accessible, and creating feedback loops that allow for continuous improvement based on customer behavior.
Moreover, this external change management must be culturally sensitive, recognizing that financial habits are deeply rooted and often resistant to rapid change. Successful AI systems will need to blend the digital and physical worlds, integrating human touchpoints where needed and using AI not just to scale, but to deepen relationships and build lasting financial resilience.
The Real Risks – What Could Go Wrong?
But these opportunities come with significant risks. Poorly designed AI systems can reinforce existing inequalities or create new ones. For instance, credit models that rely on digital transaction data may inadvertently exclude those who operate primarily in cash, exacerbating the very exclusion AI is meant to solve. Privacy is another critical challenge. In regions where data protection frameworks are weak or non-existent, the potential for misuse is high. AI systems must be designed with data minimization, consent, and transparency as core principles to avoid the pitfalls of digital exclusion.
Moreover, AI can suffer from the same biases as the humans who design it. If the training data is not representative or the algorithms are not carefully calibrated, AI systems can unfairly penalize women, minorities, or first-time borrowers – groups that already face significant barriers to financial access. This is why responsible AI design is not just a technical challenge but a governance and ethics imperative.
The Real Risks – What Could Go Wrong?
But these opportunities come with significant risks. Poorly designed AI systems can reinforce existing inequalities or create new ones. For instance, credit models that rely on digital transaction data may inadvertently exclude those who operate primarily in cash, exacerbating the very exclusion AI is meant to solve. Privacy is another critical challenge. In regions where data protection frameworks are weak or non-existent, the potential for misuse is high. AI systems must be designed with data minimization, consent, and transparency as core principles to avoid the pitfalls of digital exclusion.
Moreover, AI can suffer from the same biases as the humans who design it. If the training data is not representative or the algorithms are not carefully calibrated, AI systems can unfairly penalize women, minorities, or first-time borrowers – groups that already face significant barriers to financial access. This is why responsible AI design is not just a technical challenge but a governance and ethics imperative.
Owning the Space – The Path Forward
For those of us who have spent decades building digital financial systems, the question is not whether to use AI, but how to use it responsibly, strategically, and sustainably. We must move beyond the hype to create AI systems that truly empower the underserved, not just as data points, but as full participants in the financial ecosystem.
This requires a commitment to transparency, fairness, and continuous learning. It means designing AI that can adapt to the realities of local markets, integrate with existing community networks, and evolve as customer needs change. It means building the infrastructure to support real-time data analytics, personalized engagement, and secure digital transactions. And it means investing in the human talent needed to manage these systems responsibly.
We have the tools. We have the experience. Now, we must have the courage to design AI for inclusion, not just efficiency – for resilience, not just profit. AI can be a bridge to financial inclusion, but only if we design it that way.
Will share more in the coming weeks.
Welcome your thoughts and discussions regarding some and more questions on my mind:
- Can we really leapfrog using AI?
- What about regulations?
- What about over-reliance on technology?
- What about the cost of transactions going up?
- What about displacing jobs in the providers?
