Banks and fintech companies in Nigeria are adopting innovative methods to determine the creditworthiness of potential borrowers, moving away from traditional reliance on formal income sources and credit histories. Traditional banks typically restrict loans to customers with documented salary incomes and established credit reports, which often excludes informal workers and those without formal financial records. In contrast, digital lenders like PalmPay, OPay, and FairMoney utilize behavior-based algorithms and machine learning systems that analyze a wide array of behavioral and smartphone data. During the signup process, customers give consent to access various personal information, such as SMS alerts, contacts, app usage, device information, and location data. This data is then used to assess the borrower’s risk profile, which influences their credit limit, interest rate, and repayment schedule. Some lenders also access customers' bank statements or require their Bank Verification Number (BVN) through API integrations, providing them with more formal financial insights that supplement behavioral data.
The science behind this approach revolves around dynamic, algorithm-driven scoring systems that constantly monitor a borrower’s activities. Based on these ongoing assessments, interest rates are not fixed but fluctuate depending on the perceived risk level. This means the same individual might receive different interest rates or credit limits at different times, influenced by their recent behaviour, repayment history, and transaction patterns. Despite offering the benefit of collateral-free, quick approvals with minimal paperwork, digital lenders’ rates tend to be higher than traditional bank loans. This reflects the higher risk they carry a risk which is calculated in real-time by sophisticated algorithms assessing the borrower’s current risk profile.
The repayment process incorporates automated systems that send reminders, nudges, and notifications as the repayment date approaches. When a borrower delays repayment, the system triggers recovery actions such as automated calls and messages. In past practices, some lenders engaged in debt shaming, employing humiliating tactics like threatening calls and cursing, which sparked public outrage and regulatory scrutiny. Recognizing the importance of responsible lending, many fintechs are now shifting toward automated and ethical debt recovery methods. For example, some apps enable auto-debit arrangements where, if a customer links their bank account, the system automatically deducts the due amount when funds are available, reducing the need for aggressive collection tactics.
Experts argue that the future of lending in Nigeria hinges on refining data collection and developing inclusive models that reach underserved populations, particularly those in rural and informal sectors that lack formal financial documentation. While the current systems excel at assessing creditworthiness for urban and formally employed individuals, they still face challenges in extending credit to those outside the formal financial ecosystem. Abdulmajeed, a financial expert, warns that privacy considerations must be carefully balanced with data collection efforts to prevent violations. He emphasizes that creating models that include informal and rural populations is essential for fostering broader financial inclusion.
The potential impact of these evolving lending practices on Nigeria’s economy is significant. Economists like Abimbola Adewale highlight that restricted access to credit constrains small and medium enterprises (SMEs) and informal traders, hindering job creation and limiting economic growth. He advocates for closer collaboration between traditional banks and fintech firms, arguing that combining the stability of banks with the technological agility of fintechs can foster more inclusive, data-driven lending models. Such collaboration could enable a wider segment of the population to access credit, fuel entrepreneurship, support micro-entrepreneurs, and ultimately contribute to Nigeria’s economic development.
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