Get Guardian on GoogleThe future of the lending industry will depend significantly on how well financial institutions can assess risk. As demand for credit grows and digital channels make it easier for customers to access financing, lenders must also improve the systems they use to evaluate applications, assess creditworthiness, and make lending decisions. The ability to disburse loans quickly is important, but the quality of the decision behind each loan determines whether a lending business can grow sustainably.
Lending is often measured by how much money financial institutions disburse, how many customers they serve and how quickly borrowers can access funds. These are important indicators of activity, but they do not tell the full story of a lending business. Behind every loan that is approved is a decision about the borrower’s ability to repay, the level of risk the lender is prepared to accept and the amount of credit that can reasonably be extended.
At the Moonshot conference, Chukwuma Nwanze, the Managing Director, Credit Direct, highlighted the importance of improving this process, identifying the automation of the underwriting process and the application of artificial intelligence as key priorities.
Underwriting is one of the most consequential activities in lending. It determines who receives credit, how much they can borrow and the terms under which financing is provided. An inaccurate assessment can expose a lender to avoidable losses, while an overly restrictive one can exclude customers who may have the capacity to repay.
For many years, credit underwriting has relied on a combination of customer information, financial records, repayment history and established risk assessment methods. These remain important foundations of lending. However, the scale and complexity of modern financial activity are exposing the limitations of processes that depend heavily on manual reviews, fragmented information and models that cannot always account for the different circumstances of individual borrowers.
An accurate assessment of creditworthiness also requires lenders to take into consideration the differences in individual financial circumstances, as borrowers’ earnings, spending patterns, existing obligations and repayment behaviour can differ considerably. A system that relies on a narrow set of indicators may not capture enough of this complexity to produce an accurate assessment. This is why lenders need systems capable of examining a broader range of relevant information, identifying patterns and translating those findings into more precise lending decisions.
Automation offers a way to make the underwriting process faster, more consistent and easier to scale. Rather than depending on manual intervention at every stage, lenders can build systems that handle routine assessments, process customer information and support decisions according to established risk criteria. This does not mean removing human judgement from lending. Credit decisions carry financial consequences for both the borrower and the institution, and there will continue to be cases that require closer examination.
Artificial Intelligence (AI) takes this development further by improving how information is analysed. While automation helps execute defined processes, AI can support the identification of patterns within large volumes of data, allowing lenders to develop a more detailed understanding of borrowers and their repayment behaviour. In underwriting, this could mean analysing repayment records alongside other relevant financial information to identify indicators that may not be immediately apparent through conventional assessment methods. It can also help lenders learn from the outcomes of previous lending decisions and refine their models as new information becomes available.
For individuals and businesses, better access to relevant data could make a meaningful difference to how they are assessed for credit. People with consistent income and repayment records, for instance, could have a better opportunity to demonstrate their creditworthiness, even if they have limited borrowing history with a particular lender. Small businesses could also benefit from more accurate assessments of their financial activities, making it easier for lenders to understand their capacity and financing needs. At a national level, stronger data infrastructure could support more informed lending decisions, reduce information gaps, and help achieve productive economic activity.
Together, these capabilities will support a more efficient lending model. They can help reduce avoidable credit losses, improve the use of lending capital and create room for institutions to serve more customers without allowing risk and operating costs to grow unchecked.
Nwanze explained that for the next ten years, Credit Direct will focus on automating the underwriting process and applying artificial intelligence to improve the quality of decisions. This approach is expected to strengthen the company’s ability to assess risk more accurately, make better-informed lending decisions and operate more efficiently, ultimately supporting a more profitable and sustainable lending business.
About Credit Direct
Credit Direct is building Africa’s leading embedded credit company by integrating credit into the supply chains and payment flows of partners, unlocking financial success for individuals and businesses. The company also provides retail investment solutions, expanding its role from access to credit to broader financial growth and wealth-building. Credit Direct has served millions of customers nationwide, including those historically underserved by traditional banking. Credit Direct is a wholly owned subsidiary of First City Monument Bank (FCMB) Group Plc.
