Africa’s next decade of socio-economic growth will be defined by one core principle; data-first financial market solutions. We already see demand for better, faster, and fairer financial services. The missing link is neither talent nor capital alone; it is systematic, ethical use of data to democratise access to meaningful, affordable credit and financial products at scale.
A data-first approach begins with rethinking what data means across African markets. Traditional credit histories are sparse for large segments of the population and Micro, Small and Medium Enterprises operating on the continent.
Despite being the life-blood of Africa’s economy, the funding gap is acute, with only about 29 percent of these MSMEs having access to credit or overdraft facilities. Similarly, despite great progress achieved in terms of financial inclusion across Africa over the past decade, average household credit penetration rate of six percent for Kenya and nine percent for South Africa is still low compared to the global average of 19 percent.
That scarcity is not a barrier, it is an opportunity. By combining alternative data sources such as mobile usage patterns, bill payments, local transaction flows, agent network footprints and psychometric indicators, fintechs can build more accurate risk profiles that reflect real borrower behaviour rather than legacy paperwork.
These profiles allow us to price risk dynamically, extend loans, credit lines and overdraft facilities responsibly, and onboard customers previously excluded by conventional scoring. But “data-first” is more than assembling large datasets.
It requires rigorous governance: privacy-first collection, transparent modelling, explainable algorithms, regulatory disclosures, and robust consent mechanisms. Trust is the currency of financial inclusion.
Consumers will only share sensitive signals when they believe the outcome is fair and legible. Fintech firms that set the bar for privacy and transparency will win both market share and regulatory goodwill.
Operationalising data-first methods also means embedding analytics into every stage of product design. Start with a question: what customer problem are we solving? Then move from the question to a hypothesis that proposes a solution to the customer problem.
After that, instrument the product to capture signal-rich events, run rapid tests, and iterate based on causal evidence. This scientific loop shortens learning cycles, reduces wasted spend on poor product-market fit, and produces measurable impact on default rates, customer retention, and lifetime value.
Partnerships are central to data-first approaches and scaling solutions. No single firm holds all the data or distribution needed to scale across diverse African markets.
Strategic alliances with telcos, utilities, banks, and retail networks unlock complementary datasets and distribution channels while sharing the cost and complexity of onboarding. These partnerships must be governed by clear data-sharing agreements that protect consumers and comply with local regulations.
Regulators across Africa increasingly appreciate the power of responsible data usage to expand financial access. Constructive engagement with policymakers including offering anonymised insights, pilot results, and risk mitigation playbooks can accelerate approvals and create sandboxed environments for innovation.
We must be proactive collaborators, not defensive reactors, shaping rules that protect customers while enabling experimentation.
Another critical lever is operationalising machine learning responsibly. AI can surface subtle signals that humans overlook, but models must be continuously monitored for bias and drift.
Investing in model explainability tools and bias audits avoids systemic exclusion of vulnerable groups and preserves product fairness. In parallel, robust fraud-detection systems built on real-time anomaly detection reduce losses and protect consumer trust.
Finally, scale requires sustainable unit economics. Data-first strategies often lower customer acquisition and default costs by enabling precision targeting and dynamic pricing. But they demand upfront investment in engineering and analytics talent. Leadership must balance short-term profitability pressures with strategic allocation to data infrastructure.
The writer is the global head of capital markets, Tala.
