Access to credit is a foundational enabler of economic opportunity, yet it remains out of reach for over a billion adults globally.
By Francois Grobler, chief: decision analytics at Experian Africa
In South Africa, the challenge is particularly sharp. More than 85% of small businesses seeking funding have turnovers under R1-million, and they face the highest rejection rates from traditional credit scoring models.
This reality highlights a critical question: how can lenders responsibly expand access to credit without compromising risk integrity? The answer may lie in machine learning (ML) and alternative data.
A recent study by Forrester Consulting, commissioned by Experian, reveals a growing consensus among senior decision-makers on ML’s potential to democratise credit. 70% percent of respondents across eleven countries believe ML’s improved accuracy enables them to serve consumers who would otherwise be denied credit, a significant step toward more inclusive financial ecosystems.
This article explores how these technologies can transform credit decisioning in South Africa, unlocking opportunities for underserved consumers and micro, small and medium enterprises (MSMEs).
Why Machine Learning Matters for Financial Inclusion
Machine learning models are revolutionising risk assessment. By analysing vast datasets and identifying patterns beyond traditional scorecards, ML enables more accurate predictions of repayment behaviour.
The results in South Africa are compelling: 93% of surveyed organisations using ML reported improved acceptance rates for credit cards, while 89% have seen reductions in bad debt. For lenders, this means smarter decisions and the ability to serve new markets profitably. For consumers, it means opening doors to financial inclusion.
The research is clear: ML isn’t just improving risk models; it’s redefining inclusion in a diverse, fast-evolving economy like South Africa.
Alternative Data: The Missing Piece in South Africa’s Credit Puzzle
Alternative data, such as utility payments, rental history, and mobile transactions, is essential for assessing thin-file customers who have limited traditional credit history.
In South Africa, this group includes young adults, gig workers, and many MSMEs operating within an informal cash economy.
Ignoring this segment means leaving a significant portion of the population out of formal credit.
For these financially active consumers, many of whom are micro-business owners, alternative data provides valuable insights into their spending habits and repayment ability. More than three-quarters (77%) of credit risk decision-makers surveyed agree that alternative data is key to improving lending accuracy.
When combined with ML, this data strengthens decision-making, with 71% of respondents stating it improves profitability by reliably assessing thin-file customers.
Real-World Applications Driving Change
Initiatives like Open Banking, which allow consumers to share their transaction data securely, are gaining traction worldwide. This data offers granular insights into income and spending, helping lenders make fairer decisions.
Across EMEA, 86% of businesses have invested or plan to invest in Open Banking, with over half already seeing significant value. For lenders, early adoption offers a competitive advantage; for consumers, it means faster, fairer access to credit.
The question isn’t if Open Banking will transform lending in South Africa, but how quickly the nation will embrace it.
SMEs and the Promise of ML
MSMEs are the backbone of South Africa’s economy, contributing over 40% to GDP and employing more than 60% of the workforce. Yet, their growth is often stifled by manual, document-heavy credit assessments.
ML is changing this. South African organisations using ML reported significant improvements in SME loan acceptance rates, contributing to business growth and job creation.
This trend is mirrored across EMEA and Asia Pacific, where 88% of surveyed businesses have seen similar improvements, highlighting the technology’s potential to unlock economic opportunity.
The Future of Financial Decisioning
The momentum behind ML is undeniable. Nearly eight in ten organisations already using ML in South Africa plan to significantly increase their investment in the next one to three years.
However, barriers like cost, a lack of understanding, and legacy IT infrastructure continue to slow progress for non-adopters.
As financial institutions embrace AI and ML, the question is no longer just “how fast?” but “for whom?”
South Africa’s experience shows that inclusion and innovation can go hand in hand. Widening access to credit fuels entrepreneurship, job creation, and economic resilience. In a market where financial exclusion has long been a barrier, this technological shift could be a turning point for inclusive prosperity and sustainable economic transformation.