Michael Bowren | Co-Founder | Finch Technologies | mail me |
South Africa’s next wave of credit growth will come from a generation that is digitally fluent, economically active, and increasingly reliant on credit to navigate rising living costs. Yet, this generation remains largely invisible to traditional credit systems.
According to TransUnion, only 28% of South African Gen Z consumers over the age of 18 are credit active. This highlights the challenge lenders face when assessing first-time borrowers.
Gen Z’s thin file credit challenge
A growing number of Gen Z are moving into the credit-active age, but many are ‘thin file’ consumers, with little or no bureau-visible credit history. They may be banked, employed, and transacting daily, yet they remain difficult to score using traditional models.
Gen Z’s financial behaviour looks very different to that of previous generations. Many are supplementing traditional employment with income from the gig and creator economies. Furthermore, 75% of South Africans aged 18 to 29 report more than one source of income. As a result, side hustles have become a defining characteristic of this generation.
Gen Z consumers may sell goods on platforms like Yaga and Facebook Marketplace. They may also use digital wallets to manage their money or embrace new digital financial tools. Yet, these behaviours often sit outside traditional bureau data. Consequently, they leave very little information in their credit file.
At the same time, lenders are tightening risk controls in a volatile economy. Therefore, thin-file applicants are often the first to be declined. This does not necessarily happen because they are risky. Instead, lenders may decline them because the data used to assess them remains incomplete.
This is a structural problem – and a massive opportunity for lenders who are willing to rethink how they assess risk. Forward-thinking lenders are recognising that behavioural data, not just credit data, is the new frontier of risk assessment. And among the different behavioural data sources available, one stands out as the most powerful, the most predictive, and the most reflective of real financial behaviour: cash flow data.
Cash flow data – a new dimension of credit assessment
Transactional bank data provides a real-time, detailed view of a consumer’s financial life. It reveals income patterns, spending habits, debit orders, financial stress signals, and even side-hustle income. Traditional bureau-only models cannot see all of this information.
For Gen Z, active bank accounts can provide valuable information even when consumers do not have loans. Therefore, cash flow data offers the clearest window into their financial reality. It also provides a more accurate representation of their financial lives.
This data answers the questions that matter most to lenders. Does this person earn consistently? Do they manage their expenses responsibly? Are they over-extended? Do they show signs of financial stress? Can they afford the product they’re applying for?
This is risk assessment grounded in behaviour, not history, and it is the key to unlocking credit access for millions of thin-file consumers. Globally, lenders, credit bureaus and fintechs are exploring how transactional data can strengthen credit decisioning. It can add context that traditional credit histories alone may not provide.
Gen Z’s financial behaviour makes this approach particularly relevant. Their income sources, spending patterns and use of digital financial tools can differ significantly from those reflected in conventional credit histories. Cash flow data can therefore provide lenders with additional insight into these consumers’ financial lives.
A cash flow-centric future
South Africa’s credit ecosystem is steadily moving towards a future where behavioural financial data complements traditional credit bureau information. This approach gives lenders a more complete view of a consumer’s ability to repay. As Gen Z enters the credit market in greater numbers, this shift is becoming increasingly important. This applies not only to banks but also to retailers, telcos and other businesses that now offer embedded financial products.
Advances in open banking, affordability technology and transaction analysis are making this approach possible. Lenders can assess consumers based on how they manage their money today, rather than solely on the credit products they have held in the past. Solutions that enable lenders to securely collect and analyse transactional bank data will also help build a richer understanding of day-to-day financial patterns.
In conclusion
As behavioural financial data becomes increasingly central to modern credit assessment, agile fintechs have a responsibility to ensure accurate data collection and interpretation. Robust transaction categorisation and tagging can support this process. Financial behaviours also need careful identification, categorisation and continuous refinement. Machine learning models trained on real-world transaction data can support these efforts.
This level of accuracy is essential if lenders want to confidently identify spending patterns and detect financial stress. It can also help them build predictive models that assess affordability and future credit risk.
Gen Z’s financial behaviour shows why lenders need a broader view of creditworthiness. For Gen Z, a thin credit file should not be mistaken for a thin financial life. As this generation continues to reshape how money is earned, managed and spent, financial institutions will need to evolve their approach to assessing creditworthiness alongside them.