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By Rita Okoye
Small and medium-sized enterprises (SMEs) are often called the backbone of the economy, yet many struggle to access the banking support they need to grow.
Research by financial analyst and business analytics expert Sola Adesemoye explores how data analytics can transform SME banking, offering tailored solutions that drive economic growth and job creation
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Traditional banking models tend to treat SMEs as high-risk borrowers, partly due to limited credit histories or insufficient collateral. This perception often shuts them out of affordable financing. Adesemoye’s research argues that with the right use of predictive modelling and real-time data analysis, banks can assess SME creditworthiness more accurately, widening access to loans.
“Small businesses shouldn’t be judged solely by outdated metrics,” said Adesemoye, who has worked extensively in credit analysis and financial data evaluation. “By analysing transaction patterns, seasonal revenue shifts, and even alternative data sources, banks can build a truer picture of a business’s ability to repay.”
The study emphasises the use of machine learning to detect subtle trends that human analysts might miss. For example, an SME with fluctuating cash flow might still be a low default risk if seasonal downturns are consistently followed by strong recoveries. Predictive models can identify such patterns, supporting more balanced lending decisions.
Data analytics also offers operational benefits for banks. Automated loan application reviews, credit scoring, and fraud detection can reduce costs and speed up approvals. “Faster processing times not only improve customer satisfaction but also allow banks to serve more clients without overextending resources,” Adesemoye explained.
Another key finding is the value of segmentation. SMEs are not a uniform group—needs vary widely by sector, location, and growth stage. By segmenting SME customers based on behaviour and performance, banks can offer targeted financial products, from short-term working capital to specialised investment loans
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The research cites successful international case studies where banks increased their SME loan portfolios and reduced defaults after adopting analytics-driven models. In one example, data-driven credit scoring opened up funding for thousands of businesses previously considered too risky.
Beyond lending, analytics can help SMEs themselves become more competitive. Real-time dashboards, provided through banking platforms, can give business owners insights into cash flow, inventory levels, and sales trends. This helps them plan more effectively and avoid liquidity crises.
However, the research acknowledges challenges. Data privacy regulations require banks to handle SME data responsibly, while smaller firms may lack the digital readiness to integrate with advanced banking tools. The cost of implementing such systems can also be a barrier, especially for smaller financial institutions.
Adesemoye argues that partnerships between banks, policymakers, and technology providers could help overcome these obstacles. “Digital inclusion must go hand-in-hand with financial inclusion. We can’t empower SMEs without also giving them the tools to use modern banking services,” he said.
The economic benefits of optimising SME banking are significant. Increased access to finance enables businesses to expand, hire more staff, and invest in innovation, creating a ripple effect that boosts communities and strengthens the wider economy.
Policymakers are also watching closely. As economies seek to recover from global disruptions, supporting SMEs is seen as a key pathway to sustainable growth. Data analytics could provide the missing link between small businesses’ needs and banks’ lending priorities.
