Follow Us on Google
By Benson Michael
In an exclusive interview with Godwin David Akhamere, a finance professional and researcher specializing in credit risk and portfolio analytics, we discuss his groundbreaking research on how behavioural data and artificial intelligence can transform access to finance for small and medium enterprises (SMEs) in Nigeria.
Akhamere explains how his peer-reviewed studies point to new pathways for inclusive yet responsible lending.
You’ve published academic work on behavioural indicators in credit analysis. What motivated this research, and what did you find?
When I began examining credit systems in emerging markets, one issue stood out: many SMEs remain invisible to lenders because they lack traditional financial histories. My research on Behavioural Indicators in Credit Analysis looked at whether non-financial signals, such as transaction behaviour, repayment timing, and even customer engagement patterns, can serve as early warning indicators of default.
The findings confirmed that behavioural data can be statistically significant predictors of repayment capacity. This suggests lenders don’t always need a long financial history to responsibly assess creditworthiness. That’s especially important in contexts like Nigeria, where SMEs are often excluded from credit despite being the backbone of job creation.
You also explored how deep learning can help predict risk. How does that connect to real-world finance?
In my second study, Beyond Traditional Scores: Using Deep Learning to Predict Credit Risk from Unstructured Data, I applied neural network models like LSTMs and Transformers to non-traditional datasets. Think of mobile transaction histories, digital footprints, and behavioral records that are usually discarded by banks.
The research demonstrated that AI systems can analyze these unstructured datasets and generate highly accurate risk predictions. In practice, this means banks could responsibly extend credit to SMEs that have never taken a loan before, while still managing portfolio stability. It bridges a critical gap between financial inclusion and systemic risk management.
What do you see as the biggest barriers to SME credit in Nigeria and other emerging markets?
The biggest challenge is the reliance on outdated credit scoring models that undervalue entrepreneurial activity outside formal structures. SMEs often lack collateral or long financial histories, but they do leave behind rich behavioural and transactional data.
Regulatory hesitancy is another barrier, institutions want to innovate but worry about compliance and risk. My research provides frameworks for responsible adoption, showing that inclusion and prudence are not mutually exclusive.
How does your research tie into your professional experience in the U.S. and Nigeria?
Working in Nigeria exposed me to the struggles SMEs face in accessing finance. Now, as a Portfolio Risk Analyst in the U.S., I see how advanced data analytics and risk frameworks are applied at scale in global finance.
By combining both perspectives, I argue that what works in advanced markets can be adapted for emerging economies, provided we respect local realities. My academic work builds the bridge between these worlds, creating models that are rigorous enough for global finance but flexible enough for local contexts.
What is the broader impact if these approaches are adopted?
The impact is twofold. On one hand, SMEs would gain fairer access to credit, fueling entrepreneurship, innovation, and job creation. On the other, lenders would strengthen their portfolios by using advanced risk analytics that reduce defaults and enhance profitability.
At a systemic level, it makes the financial system more resilient. Instead of concentrating risk in a small group of “creditworthy” borrowers, banks would have a diversified SME base supported by predictive analytics. That reduces systemic shocks, something regulators and policymakers increasingly care about.
Finally, where do you see this research going next?
The next step is scaling these models through partnerships between regulators, banks, and fintech. I see a future where behavioural and AI-driven credit models are not the exception but the standard.
If Nigeria and other emerging economies embrace this, they can leapfrog traditional barriers and build a financial system that is inclusive, ethical, and resilient. That is the frontier I am most passionate about.
As Akhamere highlights, the future of credit risk management lies in looking beyond static balance sheets and embracing behavioral and AI-driven insights. His research offers not just academic theory, but a practical framework for solving one of the most persistent challenges in emerging markets: fair and sustainable SME financing. For policymakers, lenders, and entrepreneurs alike, the message is clear, innovation in risk analytics is the key to unlocking inclusive growth.
