Globally, approximately 1.4 billion adults remain unbanked, according to the World Bank. Many of these individuals lack access to financial services due to thin credit histories or systemic biases in traditional lending models. At the same time, according to a recent survey of 350 UK bank decision-makers in retail banking, AI is poised to become the most significant technological investment in the next two years, with applications ranging from credit decisioning to fraud prevention. So, where do these two worlds meet – what role does AI play in helping improve financial inclusion by serving unbanked and underbanked populations?
The Inclusion Imperative
In the UK, the Earnix banking survey showed that 59% of lenders identify rapidly changing consumer circumstances as their top challenge in pricing loans. AI-driven solutions offer a way forward by enabling banks to evaluate creditworthiness through alternative data, including utility payments, transaction histories, and digital footprints.
By integrating these diverse data sources, AI systems can build a comprehensive financial profile for individuals who have been excluded from traditional banking systems. This capability supports personalised lending products that align with both regulatory requirements and consumer needs.
AI and Consumer Duty
At the same time, the Financial Conduct Authority’s (FCA) Consumer Duty regulatory framework has set a higher bar for financial services, requiring firms to deliver fair value, improve consumer understanding, and provide accessible products. AI plays a pivotal role in helping banks meet these demands. For instance, predictive analytics and automated underwriting can tailor loan terms to individual circumstances, ensuring fairness and transparency.
Indeed, 35% of UK lenders have already adjusted their pricing strategies to align with Consumer Duty. AI-driven tools enable lenders to optimise pricing models while maintaining a clear audit trail, making compliance seamless and fostering consumer trust.
Driving Financial Inclusion with AI
AI-powered analytics are particularly impactful in expanding access to credit. Traditional credit scoring models often overlook individuals with irregular incomes, such as gig workers or recent immigrants. By contrast, machine learning algorithms can evaluate a broader range of factors, identifying patterns that indicate creditworthiness beyond standard metrics.
For example, a self-employed individual with fluctuating monthly earnings might struggle to secure a loan under traditional criteria. AI can assess their spending and saving habits, enabling the bank to offer a personalised loan product that reflects the applicant’s true financial capacity. This approach not only widens access to credit but also strengthens customer relationships by addressing specific needs.
Overcoming Challenges
While AI offers enormous potential, its implementation must be carefully managed to ensure ethical and unbiased outcomes. Regulators in the UK and US are increasingly scrutinising AI systems to prevent discrimination and ensure transparency. Earnix’s research indicates that banks are prioritising explainable AI models to meet these standards, providing clear reasoning behind lending decisions to both regulators and consumers.
In doing so, financial institutions can transform banking into a more inclusive and equitable system, ensuring that no one is left behind in the digital age.
About the Author
For over a decade, Giovanni Oppenheim, Director of Banking Solutions, Earnix, has headed up the delivery of analytics and implementations for global financial institutions at Earnix. Giovanni works with Tier 0-3 banks and global financial institutions as a trusted advisor and industry expert to define strategic requirements for Earnix products and accelerate adoption of advanced analytical capabilities in the realm of pricing and product personalization for both retail and commercial lenders.
