Artificial intelligence and alternative data analytics are reshaping how lenders assess credit risk, creating fresh opportunities to expand lending to millions of Nigerians traditionally excluded from the formal financial system, according to Winston Osuchukwu, founder and chief executive of Mathesis Analytics.
Osuchukwu, in an article titled “ How Data Deconstructs the Myth of the ‘High-Risk’ Nigerian Borrower”, argues that the country’s credit gap is driven less by borrowers’ inability to repay than by the limitations of conventional underwriting models, which rely heavily on formal banking records and collateral while overlooking financial activity generated outside traditional banking channels.
“The average Nigerian borrower is widely considered high-risk – a claim repeated in credit committees, priced into retail loans, and largely treated as settled fact. High-risk does not mean no credit – it simply requires that the lender embrace alternative datasets to price the risk appropriately,” he stated.
Osuchukwu said advances in AI-powered credit decisioning now enable banks to analyse digital payment histories, mobile money transactions and other alternative financial data to develop more accurate assessments of borrowers who have long remained outside conventional credit frameworks.
Nigeria’s financial system has historically relied on traditional indicators such as documented income, bank transaction histories and collateral in making lending decisions. While these standards have helped banks manage credit risk, they have also left a significant portion of the country’s informal economy outside the formal credit ecosystem.
According to the Mathesis Analytics CEO, many borrowers, including market traders, gig workers and remote employees earning through fintech platforms generate consistent and measurable financial activity that remains invisible to conventional banking systems despite demonstrating stable income patterns.
He noted that advances in AI-powered analytics now enable lenders to analyse digital payment histories, mobile money transactions and other alternative financial data to build more comprehensive borrower profiles without replacing traditional credit assessment processes.
“The ‘high-risk’ label, applied broadly to an entire category of borrower, was never a risk pricing tool so much as the limit of what the available tools could see,” he noted.
The development comes as Nigerian banks continue to seek new avenues for loan growth amid strong liquidity but relatively cautious retail lending. Industry analysts have increasingly identified credit assessment, rather than funding availability, as one of the key constraints limiting financial inclusion and private-sector credit expansion.
Osuchukwu believes improved data visibility could allow banks to extend lending into underserved segments while maintaining prudent risk management standards.
“The Nigerian credit gap has never been a non-lendable population problem, but one of incomplete visibility. By unifying varied data sources and partnering with the institutions that hold the capital and scale to move the market, we translate out-of-ecosystem behaviour into reliable, bank-grade risk scores,” he added.
He further pointed out that an expanded adoption of AI-driven credit decisioning could strengthen banks’ loan portfolios while expanding access to finance for small businesses, traders and other underserved borrowers, supporting wider economic growth.
