Development of a Bank Customers’ Credit Rating System Using Neural Networks Algorithms
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Date
2025
Journal Title
Journal ISSN
Volume Title
Publisher
Faculty of Science, Gombe State University, Nigeria - Bima Journal of Science and Technology, 9(1A), 233 – 244
Abstract
Credit rating systems are vital for financial institutions to evaluate creditworthiness, manage risk,
and minimize losses from defaults. Traditional methods, relying on historical financial data, lack
adaptability and exclude borrowers with limited credit histories. Machine learning (ML)
techniques, such as Backpropagation Neural Networks (BPNN) and Feedforward Propagation
Neural Networks (FPNN), offer improved predictive accuracy. This study developed an
advanced credit rating system using BPNN and FPNN, trained on Nigerian bank data
incorporating financial and non-financial indicators. BPNN’s iterative weight-adjustment
capability through backpropagation enhanced performance, while FPNN served as a simpler
baseline. The methodology included data collection, preprocessing, model training, and
evaluation using accuracy, precision, recall, and F1-score. Results demonstrated BPNN’s
superiority, achieving 82.5% accuracy compared to FPNN’s 81.2%, alongside higher precision,
recall, and F1 scores. The study underscores BPNN’s effectiveness in refining credit risk
assessment, making it a more reliable tool for Nigerian banks. By leveraging ML, financial
institutions can enhance decision-making, reduce risks, and expand credit access to underserved
populations.
Keywords: Credit Rating System, Back Propagation Neural Networks (BPNN), Feed-Forward Propagation Neural Network (FPNN), Development of Banks' Customers Credit Rating System, Machine Learning in Banking system