ABUJA, Nigeria (VOICE OF NAIJA)-Nigeria’s lending sector has recorded notable progress through digital transformation, particularly in improving customer-facing credit processes and speeding up loan applications and disbursements.
However, experts say this progress has not fully extended to the core of credit risk infrastructure, leaving significant gaps that continue to constrain safe lending expansion.
Following the withdrawal of the Central Bank of Nigeria’s forbearance measures, the industry’s non-performing loan (NPL) ratio rose to 8.03%, above the regulatory benchmark of 5%, underscoring ongoing credit quality pressures.
Despite the improvements in digital onboarding, analysts note that many banks still operate with legacy risk models and backward-looking data that fail to reflect real-time borrower behaviour.
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This, they argue, leaves existing credit portfolios exposed while limiting the ability of lenders to effectively tap into Nigeria’s large retail and SME markets.
One of the major challenges is what is described as a bureau and data blind spot, where lenders rely on fragmented information sources.
Internal transaction data provides a narrow view of borrower behaviour within a single institution, while credit bureau reports offer a broader but often incomplete and inconsistent credit history.
With limited bureau coverage and uneven data sharing across the financial system, neither source provides a full picture of how borrowers earn, spend and repay.
Experts say resolving this requires integrating internal behavioural data with external sources such as payroll records, utility payments and alternative financial data to build a continuous, real-time assessment of creditworthiness.
Another key issue is the use of static risk acceptance criteria in credit decisions. In a volatile macroeconomic environment shaped by inflation and shifting interest rates, fixed lending thresholds are increasingly seen as outdated.
Analysts warn that these rigid models often lead to automatic rejections of new or thin-file borrowers, including salaried workers and individuals without formal credit histories, even where they may be creditworthy.
They argue that moving toward predictive, data-driven systems would allow lenders to adjust risk parameters dynamically, improve pricing accuracy and identify potential defaults earlier.
A further gap exists in the disconnect between credit origination and loan recovery processes. In many institutions, collections units operate separately from credit risk teams, meaning repayment data is rarely fed back into underwriting models.
As a result, lending systems fail to learn from actual borrower outcomes, leading to repeated pricing and credit assessment errors.
Industry observers say integrating collections data into the credit decision chain would create a feedback loop that continuously refines risk models and strengthens overall portfolio performance.
Overall, experts say closing these gaps will require a shift from static lending tools to more adaptive, intelligence-driven systems that continuously learn from borrower behaviour.
They argue that lenders that embrace predictive credit infrastructure will be better positioned to manage risk, expand access to credit and support long-term financial stability in the sector.