Implementation of SMOTE to Improve the Performance of Random Forest Classification in Credit Risk Assessment in Banking

Background: Credit is essential in banking operations, facilitating investment, corporate expansion, and financial satisfaction. Credit risk may emerge if the borrower defaults on payment commitments. Objective: This study aims to evaluate an individual's creditworthiness by classifying and assessing their eligibility for credit. Methods: This study uses the Random Forest technique to categorize credit risk evaluation. Random Forest is a decision tree technique recognized for its high accuracy in data classification, utilizing an ensemble method of many decision trees. Before executing the classification process, issues frequently arise when data cannot be directly processed due to class imbalance. This study employs the SMOTE (Synthetic Minority Over-sampling Technique) algorithm to address class imbalance. The SMOTE algorithm is a method that emphasizes oversampling and is designed to augment the data in the minority class by generating synthetic data that aligns with the minority class data. The findings indicated that the ideal ratio for partitioning training and testing data was 80:20, and implementing the SMOTE technique within Random Forest enhanced performance assessment. Results: This research contributes to improving the accuracy of credit risk classification using the Random Forest algorithm, which effectively handles complex data and is supported by the implementation of SMOTE to overcome the class imbalance in the data. The classification accuracy value rose from 91.54% to 94.41%. The precision value rose from 90.83% to 97.03%, while the recall value increased from 60.26% to 91.55%. Conclusion: This method helps banks identify high-risk debtors more objectively and efficiently and supports appropriate credit decision-making.