AI’s Role in Financial Inclusion: Opportunities, Risks, and the Path Forward

It wasn’t so long ago that the use of Artificial intelligence in banking was primarily centred around customer service functions in the form of chatbots – with varying levels of success. But AI was not being used in any meaningful way at the heart of banking operations. Things are changing fast, it is already transforming the industry. From real-time fraud detection to credit scoring based on alternative data such as mobile phone usage, AI is changing how financial institutions serve customers.      

According to McKinsey, generative AI alone could generate between $200 and $340 billion in annual revenue for banks, the equivalent of 2.8–4.7% of total global industry revenue (McKinsey, 2023). Global spending on AI in finance is expected to accelerate dramatically, rising from $35 billion in 2023 to $126 billion by 2028, with some forecasts suggesting that AI could allow financial institutions to process transactions up to 90% faster and deliver as much as $1 trillion in savings globally by 2030 (McKinsey, 2023).

Breaking Down the Barriers to Reduce Cost

For the 1.4 billion adults who remain unbanked worldwide, the implications go far beyond efficiency gains. AI has the potential to break down the barriers that keep low-income individuals outside the formal financial system. For many, those barriers are stark: high account fees, the absence of credit histories, or simply the physical distance from a bank branch. While mobile money and fintech solutions have already expanded access, it is AI’s ability to reduce costs, personalise products, and leverage alternative datasets that could unlock the “last mile” of financial inclusion.

We already have real-world evidence of how digital infrastructure can radically alter costs. In India, the combination of the Aadhaar biometric ID system and the Unified Payments Interface (UPI) reduced customer acquisition costs for financial institutions from around $12 to just six cents, according to IMF research. AI has the power to push this further, using biometric verification, matching algorithms, and facial recognition to streamline KYC (Know your customer) processes and remove the friction that makes serving low-income customers uneconomical (IMF, 2021). By lowering these barriers, financial service providers can profitably reach communities they once overlooked.

But reducing costs is only the start. AI also makes possible the tailoring of financial products to fit real lives. Fintech platforms such as Boost in Nigeria, Copia in Kenya, and Fairbanc in Indonesia are already applying AI to integrate last-mile retailers into digital ecosystems, providing working-capital loans based on real-time transaction flows. Similarly, conversational interfaces, AI-driven chatbots and voice assistants, allow people in remote or low-literacy contexts to interact with financial institutions in their own language, accessing guidance and education 24/7. Other models, like Farmer.Chat, combine agricultural advice with financial tools, demonstrating how AI can blend financial and non-financial services to strengthen livelihoods.

Closing the Credit Gap

Perhaps most significantly, AI is closing the credit gap that has historically excluded micro-enterprises and informal workers. The World Bank estimates that the global financing gap for MSMEs stands at around $5 trillion annually (World Bank/IFC, 2017). By analysing digital receipts, utility bills, and inventory data, AI can assess creditworthiness even where collateral or credit histories are absent. In India, fintechs such as Fundfina, which serves small shops, and KarmaLife, which supports gig workers, have shown that AI-driven models can match the predictive power of traditional scoring while unlocking credit for segments the mainstream system had written off.

Trust, too, is central to inclusion, and AI has a role here. The Philippines’ central bank, for example, launched BSP Online Buddy, an AI-enabled chatbot that allows consumers to lodge complaints via SMS or Messenger, while regulators use the data to detect market misconduct in real time. This kind of supervisory technology (“SupTech”) demonstrates how AI can enhance both consumer protection and institutional accountability. Meanwhile, AI-powered literacy apps are helping first-time users build the confidence to save, borrow, and invest responsibly.

Of course, AI’s potential is not without risk. By its nature, it depends on vast datasets, which heightens the danger of privacy breaches, misuse, and cyberattacks. Algorithmic bias is another concern: if low-income groups or women are under-represented in datasets, AI models can entrench exclusion rather than alleviate it. And without proper human oversight, financial institutions could place undue reliance on algorithmic outputs, risking unfair outcomes or systemic errors. Scholars have warned that “lending by algorithm” is only as fair as the data and governance frameworks that underpin it.

Choice

The choice, then, is not whether AI will reshape finance, it already is, but whether it will do so in ways that are fair, inclusive, and sustainable. Achieving that requires investment in digital infrastructure, particularly consent-based data systems and gender-disaggregated datasets that ensure everyone is represented. It requires balanced policies and regulation that enforce transparency and accountability. It demands consumer protection frameworks that combine cybersecurity with financial literacy initiatives. And it calls for collaboration, because no single institution, bank, fintech, or mobile operator, can deliver inclusion alone.

AI can deepen financial divides, or it can dismantle them. The difference lies in the choices we make today. With bold but responsible action, we can harness AI not just to build a smarter financial system, but to build one that is more just, more inclusive, and more human.