Access to credit is a fundamental enabler of economic opportunity, yet it remains out of reach for more than a billion adults globally. The challenge is particularly acute in South Africa. Over 85% of small businesses seeking funding have a turnover of less than R1 million, and face the highest rejection rates from traditional credit scoring models. This reality highlights an important question: How can lenders responsibly increase 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 shows that there is a growing consensus among senior decision makers on the potential of ML to democratize credit. 70 percent of respondents The eleven countries believe that the improved accuracy of ML will enable them to serve consumers who would otherwise be denied credit, an important step towards a more inclusive financial ecosystem. This article explains how these technologies can transform credit decision making in South Africa, opening up opportunities for disadvantaged consumers and micro, small and medium enterprises (MSMEs).
Why machine learning matters for financial inclusion
Machine learning models are revolutionizing risk assessment. By analyzing huge datasets and identifying patterns beyond traditional scorecards, ML enables more accurate prediction of repayment behavior. The results in South Africa are fascinating: 93% of organizations surveyed using ML reported improved approval rates for credit cards, while 89% saw a reduction in bad debt. For lenders, this means better decisions and the ability to serve new markets profitably. For consumers, this means opening the doors to financial inclusion. The research is clear: ML isn't just improving risk models; It is redefining inclusion in a diverse, fast-growing 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 necessary to assess thin-file customers with limited traditional credit history. In South Africa, this group includes young adults, gig workers and many MSMEs working within the informal cash economy. Ignoring this sector means depriving a significant section of the population 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 capacity. More than three-quarters (77%) of credit risk decision makers surveyed agree that alternative data is important for improving loan accuracy. When combined with ML, this data strengthens decision making, with 71% of respondents saying it improves profitability by reliably evaluating thin-file customers.
Real-world applications are driving change
Initiatives like Open Banking, which allow consumers to share their transaction data securely, are gaining popularity around the world. This data provides detailed insight into income and expenses, helping lenders make unbiased decisions. In EMEA, 86% of businesses have invested or plan to invest in Open Banking, with more than half of them already seeing significant value. For lenders, early adoption provides a competitive advantage; For consumers, this means faster, equitable access to credit. the question is not If Open Banking will transform lending in South Africa, but how soon The nation will adopt it.
SME and ML promise
MSMEs are the backbone of South Africa economyContributing more than 40% to GDP and employing more than 60% of the workforce. Yet, their growth is often hindered by manual, document-heavy credit evaluation. ML is changing this. South African organizations using ML reported significant improvements in SME loan approval rates, contributing to business growth and job creation. This trend is mirrored in EMEA and Asia Pacific, where 88% of surveyed businesses have seen similar improvements, highlighting the potential of technology to unlock economic opportunities.
future of financial decisions
The momentum behind ML is undeniable. Eight out of ten organizations already using ML in South Africa plan to significantly increase their investment over the next one to three years. However, barriers such as cost, lack of understanding and legacy IT infrastructure are slowing progress for non-adopters.
As financial institutions adopt AI and ML, the question is no longer just “how fast?” Ka is no more. But “for what?” The South African experience shows that inclusion and innovation can go hand in hand. Expanding access to credit promotes 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.
