By Winston Osuchukwu, Founder and Chief Executive of Mathesis
Digital transformation has modernised the front end of the credit process in Nigeria, streamlining customer journeys and shortening the path from application to disbursement.
However, this progress has not reached the core of the credit process. While digital application flows are now standard, the underlying risk infrastructure remains underdeveloped.
Following the withdrawal of the Central Bank of Nigeria’s forbearance measures, the sector’s non-performing loan (NPL) ratio climbed to 8.03% – well above the 5% regulatory limit.
The deeper, structural flaw is that banks still run on legacy risk models and backward-looking data: an approach that leaves existing portfolios exposed while shutting out the vast retail market.
To scale retail and SME credit safely, forward-looking institutions must close three critical gaps in their core credit infrastructure.
1. The Bureau and Data Blind Spot
Many institutions rely on a fragmented view of borrower risk. Internal transaction data offers a deep but narrow view of a borrower’s behaviour within one institution, while periodic credit bureau reports provide a broad but shallow, “negative-only” history across other lenders.
Because credit bureau coverage in Nigeria remains relatively low and data sharing is often inconsistent, neither source effectively captures how a borrower actually earns, spends, and repays.
Resolving this requires unifying the data architecture, integrating internal behavioural signals with diverse external streams such as payroll, utility, and alternative financial data, to build a continuous, real-time picture of cash flow and true repayment capacity.
2. Static Risk Acceptance Criteria
To assess a borrower’s credit eligibility, banks apply internal risk acceptance criteria that are often static.
In a volatile macroeconomic environment marked by shifting interest rates and inflation, a borrower’s financial reality changes rapidly, rendering these rigid, point-in-time benchmarks obsolete.
Furthermore, out of caution, these inflexible thresholds often default to conservative rejections for unfamiliar applicants, such as new salaried employees or thin-file borrowers – those with little or no formal credit history for a bureau or bank to draw on – leaving profitable loans on the table.
Transitioning to a predictive model changes risk management into a continuous, data-driven cycle.
By ingesting high-frequency behavioural data, risk systems can dynamically govern their acceptance criteria in real-time, allowing them to adjust parameters, optimize pricing, and deploy interventions well before a default occurs.
3. The Collections Disconnect
In many institutions, collections teams operate in silos downstream of the credit department, meaning critical recovery performance data rarely gets fed back to front-end risk models.
Consequently, underwriting systems fail to learn from actual repayment behaviours – repeating the same structural pricing mistakes.
Integrating these functions via a direct data pipeline creates a self-learning loop, routing recovery outcomes back into the origination engine.
This empowers the risk engine to dynamically update models, continuously refining underwriting criteria based on real-world results to prevent future defaults and capture lost basis points
The Bottom Line
Closing these gaps requires intentionality: moving away from ‘set-and-forget’ tools to systems that actively manage risk.
It means moving beyond fragmented data toward an integrated intelligence layer that learns from borrower behaviour to govern automated decisions with precision.
The lenders that lead over the next year will be those that treat credit not as an isolated transaction, but as a continuous, dynamic process.
At Mathesis, we have spent years building the engine that makes this possible, powering over eight million loans for two plus million Nigerians.
The future of credit belongs to those who adopt this predictive approach – and we have the proven tools and expertise to help you get there.
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