The credit market is the engine room of the South African economy, but for many financial institutions and aspiring business owners alike, that engine is currently running on a partial cylinder.
By Eshmael Mpabanga, regional head: southern Africa and senior vice-president of Intellect Design Arena Ltd
Despite the rapid digitisation of banking, the reality is that approximately 50% of the country’s economically active population is effectively “invisible” to traditional lending models. These are individuals and entrepreneurs who are active, earning, and transacting, but because they lack a “thick” credit file of historical formal data, legacy systems can see only a blank space – even if they are looking.
This issue has always been a key social inclusion challenge, but even for the banking sector itself it is a significant market share opportunity being left on the table. The barrier to entry has always been two-fold: the inability to accurately assess risk without traditional data, and a cost-to-serve that makes small-ticket lending operationally unprofitable.
However, by moving toward an AI-first, orchestrated approach to credit, we can finally turn this invisible segment into a visible – and profitable – growth engine.
Using a better lens, not a lower bar
A common misconception is that expanding credit to the underbanked requires a lowering of risk standards. In a volatile economic climate, that is a risk no responsible organisation is willing to take – and which the National Credit Act of 2005 protects against. But the shift we are seeing today isn’t about lowering the bar, but rather about using a better lens to see the bar for what it is.
Traditional credit scoring is a narrow, retrospective view. AI-first lending, powered by a composable architecture like what we’ve built with eMACH.ai, allows institutions to leverage a vast ecosystem of alternative data. Through thousands of APIs, banks can now plug into telco records, utility payments, and retail data to build a real-time picture of economic behaviour.
When you apply First Principles Thinking to credit, you stop looking for a paper trail and start looking for patterns of reliability. This kind of intelligent inclusion allows a credit provider to safely grant a loan to a micro-entrepreneur based on their actual cash-flow consistency rather than a lack of a decade-old mortgage history. AI is helping us match our financial services architecture to the way modern people are active in the new economy: diversely, uniquely and in more than one place.
Solving the cost-to-serve crisis
Even when the risk is understood, though, the economics of lending often break down at the smaller end of the market. In a traditional environment, the manual handoffs, physical documentation, and human interventions required to process a loan stay relatively constant, whether the loan is for R500 000 or R5 000. This makes micro-lending a loss-leader for many banks. Without tackling this barrier, policies, mission statements or cultural shifts can’t achieve the goal alone.
This is where the shift to cloud-native microservices becomes a commercial game-changer. By using an elementalised architecture, organisations can automate the heavy lifting of the lending journey – from origination to collections.
Our own client client data indicates that moving to this modular, automated framework can lower operational costs by 30% to 40%.
Suddenly, the R5 000 loan that was previously too expensive to process manually becomes a profitable, scalable product. Efficiency improves the bottom line, yes, but it also expands the bank’s serviceable universe.
A positive sum game
AI-first lending fundamentally shifts the experience from process-driven to customer-driven. The only way it works is if all stakeholders win.
For banks:
- Faster, more accurate credit decisioning
- Better risk segmentation and pricing
- Lower cost-to-serve through automation
For customers:
- Instant or near-instant approvals
- More personalised offers based on real behaviour, not generic scoring
- Increased financial inclusion, particularly for customers with thin or non-traditional credit histories
- Reduced friction, fewer documents, fewer manual steps
In markets like South Africa, this is particularly powerful. AI enables institutions to move beyond rigid credit models and recognise real economic behaviour, opening access to credit for underserved segments.
The end of the ‘black box’
As we integrate AI into these critical life decisions, we must address the trust concern head-on. Consumers and regulators are rightfully wary of “black box” algorithms that reach conclusions without explainable or auditable logic. In South Africa, where fairness and transparency are non-negotiable, “the computer said no” is not an acceptable answer.
The solution is Governed Intelligence, specifically through Explainable AI (XAI). Unlike generic AI, the models we have invested in are designed to provide reason codes for every decision. If a loan is rejected or a limit is set, the system provides the specific, governed data points that led to that outcome.
This transparency serves two purposes: it ensures the bank remains compliant and defensible, and it empowers the consumer with the information they need to improve their financial standing. AI should augment the relationship between the bank and the customer, not obscure it.
The real risk, then, is not AI itself – it’s ungoverned AI. When properly designed, AI actually reduces bias by applying consistent, data-driven logic rather than subjective judgment.
Responsible AI, in our view, is an architectural requirement. It means:
- Transparency: Every decision can be explained and traced
- Governance: Role-based controls, audit trails, and policy enforcement
- Fairness: Continuous monitoring for bias and unintended outcomes
- Security & privacy: Strict control over how data is used and accessed
Crucially, responsible AI also means aligning AI outcomes to business and societal impact, not just efficiency. If AI improves speed but compromises fairness or trust, it has failed. Responsible AI ensures you don’t have to choose between the two.
The 2030 vision
Looking ahead to 2030, the concept of a “loan” as a static, standalone product will likely feel antiquated. We are moving toward a world where credit is a fluid, contextual event embedded into the customer’s life.
In this future, the bank acts as an orchestrator of financial outcomes. Using a unified platform for scaling, credit will be offered at the exact point of need, not through separate processes, and risk will be managed dynamically and continuously – not just at origination.
The institutions that will dominate this landscape are those that recognise their core infrastructure is not just a backend system, but a strategic growth lever. By embracing a composable, intelligent architecture, South African banks can stop trying to “fix” the legacy models that exclude half the population and start orchestrating a future where every economically active citizen is visible, valued, and banked.