The Role of Artificial Intelligence in Credit Scoring for SMEs in Northern Nigeria: A Case Study of First Bank, Plateau State
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1.1 Background of the Study
Small and Medium Enterprises (SMEs) are the backbone of Nigeria's economy, contributing significantly to job creation and economic growth. However, access to credit remains a major challenge for SMEs, particularly in Northern Nigeria, where traditional credit scoring methods often fail to capture the financial potential of small businesses. Artificial Intelligence (AI) has emerged as a transformative tool in credit scoring, enabling the evaluation of non-traditional data sources such as transaction history, social media activity, and utility payments. AI-powered credit scoring models offer greater accuracy, inclusivity, and speed compared to conventional methods. Studies (Mohammed et al., 2024; Singh & Adewale, 2025) demonstrate that AI can significantly expand access to credit for underserved populations while reducing default rates.
First Bank in Plateau State has adopted AI-driven credit scoring systems to address the unique challenges faced by SMEs in the region. These systems leverage advanced machine learning algorithms to provide more reliable credit assessments, thereby enhancing financial inclusion. However, issues such as data privacy, algorithmic bias, and the technological readiness of borrowers present significant challenges. This study explores the role of AI in credit scoring for SMEs at First Bank, Plateau State, assessing its effectiveness, limitations, and potential for fostering economic growth.
1.2 Statement of the Problem
SMEs in Northern Nigeria face limited access to credit due to the inefficiency and rigidity of traditional credit scoring methods. At First Bank in Plateau State, the adoption of AI-driven credit scoring models aims to bridge this gap by offering more accurate and inclusive assessments. However, challenges such as data availability, borrower trust in AI systems, and algorithmic fairness hinder the full potential of these tools. This study investigates the effectiveness of AI in credit scoring for SMEs, addressing both its benefits and the obstacles to its implementation.
1.3 Objectives of the Study
1. To evaluate the effectiveness of AI-driven credit scoring in improving credit access for SMEs at First Bank, Plateau State.
2. To identify challenges associated with implementing AI-based credit scoring models in Northern Nigeria.
3. To propose strategies for enhancing the adoption of AI in credit assessment for SMEs.
1.4 Research Questions
1. How effective are AI-driven credit scoring models in improving credit access for SMEs at First Bank, Plateau State?
2. What challenges hinder the implementation of AI-based credit scoring systems in Northern Nigeria?
3. What strategies can enhance the adoption of AI in credit assessment for SMEs?
1.5 Research Hypothesis
1. AI-driven credit scoring significantly improves access to credit for SMEs at First Bank, Plateau State.
2. Challenges such as data privacy and algorithmic fairness limit the effectiveness of AI-based credit scoring models.
3. Strategic interventions and stakeholder education can enhance the adoption of AI in credit assessment for SMEs.
1.6 Significance of the Study
This study provides actionable insights for financial institutions, policymakers, and SME stakeholders. For banks, it highlights the operational and financial benefits of AI-driven credit scoring, emphasizing its role in expanding credit access. Policymakers can use the findings to design supportive regulatory frameworks that promote AI adoption while ensuring fairness and transparency. SME owners can gain a better understanding of AI-based credit assessment, fostering trust and collaboration with financial institutions.
1.7 Scope and Limitations of the Study
The study focuses on the role of AI in credit scoring for SMEs at First Bank in Plateau State. It examines the effectiveness of AI models, their operational challenges, and strategies for enhancing adoption. Limitations include the availability of high-quality data, potential biases in AI algorithms, and the contextual challenges specific to Northern Nigeria. The findings may not fully generalize to other regions or financial institutions with differing operational contexts.
1.8 Operational Definition of Terms
1. Credit Scoring: The process of assessing a borrower’s creditworthiness based on financial and behavioral data.
2. Artificial Intelligence (AI): The simulation of human intelligence in machines to perform tasks such as data analysis and decision-making.
3. SMEs (Small and Medium Enterprises):Businesses with a limited scale of operations, often defined by employee numbers and revenue thresholds.
4. Algorithmic Bias: Systematic errors in AI models that lead to unfair or inaccurate predictions.
5. Financial Inclusion: Efforts to provide affordable and accessible financial services to underserved populations.
