In emerging markets, access to finance remains limited for large segments of the population. Despite global efforts to improve credit availability, financial inclusion gaps persist, especially among women and young adults. For instance, in West Africa, just 27% of contract borrowers are women. In Kenya, only 0.4% of 20- to 24-year-olds received formal loans exceeding $200 in 2023.
These barriers aren’t just an inconvenience; without a robust credit score, people struggle to secure housing, financing, or even basic mobile plans. Expanding financial inclusion gives underserved groups access to capital, allowing them to start and build businesses, create jobs, and generate long-term stability, all while boosting the local economy.
While many strategies aim to support fair and widespread access to finance, two of the most promising tools are artificial intelligence (AI) and alternative data.
When used responsibly, they can help bridge the credit gap for underserved populations and support more inclusive financial systems.
What alternative data brings to the table
Conventional credit scoring systems often lean on formal banking information: previous loans, repayment histories, and standard financial records. But in many emerging markets, millions of people operate outside formal banking channels, meaning they lack the traditional data required to build a credit profile.
Alternative data fills that gap. It includes insights from telecom and utility payments, mobile transactions, previous small loans, and even self-reported information. Many of these data points are accessible through credit bureaus and can be used to build a clearer picture of someone’s financial behaviour.
Satellite images can reveal the value of someone’s property or farmland. Social media behaviour – how people interact publicly or communicate online – helps spot fraud patterns, giving lenders better ways to gauge risk.
These varied data sources provide real signals about creditworthiness for underserved groups, especially when standard financial records simply don’t exist.
AI’s role in turning alternative data into insights
Effectively working with large volumes of alternative data requires advanced systems. AI and machine learning models can rapidly analyse both structured and unstructured data sources, generating insights for credit scoring that would otherwise be difficult to uncover.
To use these models responsibly, financial institutions need secure frameworks to manage, monitor, and deploy them, ensuring outputs are consistent and explainable.
AI also offers value beyond credit evaluation. It can support financial education through personalised advice tools, helping younger people make better financial decisions.
AI also enables the personalisation of financial products: lenders can tailor loan terms, insurance packages or repayment schedules to fit individual customer needs, improving access and satisfaction.
Keeping AI fair and accurate
As with any technology, AI has huge potential, but its impact depends on how it’s implemented. One challenge around AI’s use in credit scoring is bias. If AI models are trained on historical data that reflects systemic inequities – race, gender or socioeconomic status – those patterns can be perpetuated, potentially excluding the very groups financial inclusion efforts aim to support.
That’s why human oversight remains vital. Teams must regularly audit models, challenge results, and verify equitable treatment across different groups.
Privacy and data protection are just as important. AI-powered credit systems process enormous volumes of sensitive personal information, requiring strong safeguards against misuse and security breaches.
Data quality is another key factor: unreliable inputs produce unreliable assessments, potentially worsening the inequities these systems should address.
The goal is to find the right balance. Credit scoring should combine the strengths of AI and machine learning with human judgment. Automation can streamline the process, but people are still needed to interpret results, question assumptions and ensure fairness at every stage.
The path to inclusive finance
With inflation climbing and economic volatility increasing, countries need stable foundations to overcome uncertainty. Alternative data holds real promise for advancing global financial inclusion for underserved groups, but that promise depends on collecting, using and sharing data in a secure and responsible way.
Combining AI and machine learning with clear regulatory frameworks, ethical data practices and human oversight is the best way forward. This strategy can broaden access to finance, empower communities and drive inclusive, sustainable growth across emerging markets.
