What financial inclusion means
Financial inclusion is the idea that people and small businesses can reach useful, affordable financial services: a safe place to keep money, a way to send and receive payments, access to credit when needed, and insurance or savings tools. Estimates of how many adults worldwide lack an account have changed over the years and differ by source, so it is best to say simply that a substantial number of people still lack full access, with the gap varying greatly between and within countries.
People may be excluded for many reasons: distance from a branch, lack of identity documents, irregular income, low trust in institutions, fees, or products not designed for their needs. Technology can help with some of these. It cannot fix all of them.
The role of mobile banking
For many people, the phone has become the bank branch. Mobile money and app-based banking allow someone to hold a balance, pay bills and transfer funds without visiting a physical office. The history of these services is part of the story of how mobile payments developed. In places with limited branch networks but widespread phone use, this can reduce the cost and time of basic transactions.
Where does AI fit in? Mostly behind the scenes: verifying identity from photos using computer vision, spotting fraud, answering common questions through chat tools, and scoring applicants for small loans. Each of these can lower the cost of serving a customer, which is one reason providers may be willing to serve people they previously overlooked.
Alternative data and credit scoring
Traditional credit scoring relies on a history of borrowing and repayment. A person who has never had a loan or card may have no file, and so no score, which makes it hard to get one. This is sometimes called being “credit invisible.”
Alternative data aims to address that. Depending on the country and its rules, lenders may consider things such as:
- patterns of bill and rent payments
- mobile phone top-up or usage patterns
- transaction history in a digital wallet
- cash-flow records for a small business
A model can look for patterns in such information that correlate with repayment. For someone with no formal history, that might open an opportunity that otherwise would not exist.
Why it is not simple
The same approach carries several risks.
Hidden bias. If a model learns from historical lending data, it may reproduce older patterns of unfair treatment. Even when a model is not given sensitive attributes, other variables can act as proxies for them. This is a general concern in AI ethics and is closely related to hiring bias in employment.
Opacity. A person who is refused a loan may not understand why. Some places require lenders to give reasons for adverse decisions, and more complex models make that harder.
Privacy and consent. Using phone and transaction data raises questions about what people have agreed to and how long the data is kept. Broader debates about online privacy and web tracking apply here too.
Behavior changes. If people learn which behaviors raise a score, they may change them for the score rather than for their own needs, or may avoid using services that are monitored.
Errors. Data can be wrong, outdated or attached to the wrong person. Without an easy way to correct it, one error can block access.
Exclusion by algorithm
The phrase “exclusion by algorithm” describes cases where automated systems consistently decline or underserve certain groups. This may happen without anyone intending it. For example, a system might treat irregular income as risky, which can disadvantage gig workers, seasonal farmers or people who are paid in cash, even though they manage money responsibly.
Other forms of exclusion are less about decisions and more about design: apps that assume a smartphone, a stable data connection or a particular level of literacy. The digital divide and limited rural connectivity matter for banking as much as for any other service.
Safeguards that tend to help
Policy makers, researchers and providers have suggested a range of safeguards, though how widely each is used varies.
- Testing models for different outcomes across groups before and after deployment.
- Giving applicants clear explanations and a route to appeal or have a person review the decision.
- Allowing people to see and correct the data held about them.
- Keeping non-digital options available.
- Setting fair limits on fees and interest so that wider access does not simply mean wider exposure to costly products.
Access is not the same as benefit
It is worth noting that simply getting more people into credit does not automatically help them. Small, quick loans can be useful for covering an emergency, but they can also lead to debt problems if costs are high or repayment terms are poor. Evidence on the long-term effects of various microcredit and digital lending schemes is mixed, which is a good reason to be careful about confident claims either way.
Putting it together
AI and mobile technology can reduce the cost of offering simple financial services, and new kinds of data may help people who lack a traditional record. At the same time, automated decisions can embed bias, hide reasoning or shut out people whose circumstances do not match the patterns a model was trained on. Inclusion in practice depends on regulation, good product design, fair pricing and human oversight as much as on any algorithm.