Browsing by Author "Yusuf, I."

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    Development of a Bank Customers’ Credit Rating System Using Neural Networks Algorithms
    (Faculty of Science, Gombe State University, Nigeria - Bima Journal of Science and Technology, 9(1A), 233 – 244, 2025) Abdulsalam, S.O.; Yusuf, I.; Awotunde, S.O.; Edafeajiroke, M.F.
    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
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    Predicting Customers’ Churn in a Bank Using Firefly Feature Selection and RBF-Support Vector Machine Classification Algorithms
    (Faculty of Information and Communication Technology, Kwara State University, Malete, Nigeria - KWASU Journal of Information, Communication and Technology, 2(1), 17 – 25, 2023) Abdulsalam, S.O.; Yusuf, I.; Ogunwuyi, M.R.; Edafeajiroke, M.F.
    Customer churn prediction helps businesses identify factors contributing to customer attrition, allowing them to take preventive actions. This project develops a churn prediction model for a bank using Firefly Feature Selection and RBF-Support Vector Machine (SVM) Classification Algorithms. The dataset consists of 900 instances and 10 features, representing customer churn rates. Firefly Feature Selection was used to find the most pertinent features after preprocessing techniques like Label Encoding and Oversampling were used. After that, the RBF-SVM model was used to make predictions. F1-score, recall, accuracy, and precision were used to assess the model’s performance. The strong predictive capability was demonstrated by the model’s 84% accuracy, 84% precision, 100% recall, and 91% F1 score. This methodology assists companies in identifying consumers who are at risk and in taking preventative action to lower churn rates. Future research could focus on improving feature selection techniques, model robustness, and applying the model to other industries for comparison. Keywords: Customer churn prediction, Firefly Feature Selection, RBF-Support Vector Machine (SVM), Classification algorithms, Model evaluation