Improving Credit Modeling Accuracy in the Financial Sector: Evaluating the Effectiveness of the Synthetic Minority Oversampling Technique (SMOTE)

Abstract

Credit modeling especially in the financial sector, faces significant challenges in deep learning applications. Accurate credit modeling is essential for financial institutions to assess credit risk and make informed lending decisions. However, complexities arise due to inaccuracies influenced by datasets, modeling algorithms, and sampling techniques. This study sought to evaluate and validate the effectiveness of the Synthetic Minority Oversampling Technique (SMOTE) in enhancing credit Modeling. The SMOTE model integrates traditional and machine learning methods, including logistic regression, decision trees, Random Forest, Neural Networks, and Support Vector Machines. Using a diverse dataset, it incorporates borrower characteristics like age, income, and credit score, and loan details such as amount, interest rate, and term. The model focused on balancing data distribution, creating synthetic samples, and optimizing performance to surpass baseline models across metrics like accuracy, precision, recall, confusion matrix, and F1 score. Findings revealed that there was limited adoption of advanced models amongst financial institutions in Meru County, due to their complexity and training demands. Further findings reveal that, applying SMOTE improved class balance, particularly by enhancing Decision Trees and Random Forests in accuracy and error reduction. Random Forests with SMOTE showed the most significant improvements, suggesting its effectiveness in addressing class imbalance and enhancing predictive performance. Adopting SMOTE-enhanced Random Forests offers robust tools for credit risk management, advocating for increased quantitative model adoption and collaboration among financial institutions.