<![CDATA[Credit is one of the main sources of income for banking institutions and plays a crucial role in supporting long-term profit growth. However, credit distribution is inherently associated with risks, especially the risk of default when borrowers fail to meet their repayment obligations as agreed. One effective strategy to minimize such risks is to conduct a comprehensive and accurate creditworthiness assessment of prospective borrowers before loan approval is granted. This study aims to evaluate the performance of three classification algorithms—Random Forest, Support Vector Machine (SVM), and Artificial Neural Network (ANN)—in predicting credit risk based on the borrower’s occupation. The dataset used consists of 1,314 loan records with an imbalanced distribution between performing and non-performing loans. The experimental results show that the Random Forest algorithm achieved the highest accuracy at 97%, followed by Support Vector Machine at 73% and Artificial Neural Networks at 64%. While ANN is capable of capturing complex patterns through multilayered learning, Random Forest proved to be the most effective and robust in handling the given dataset. These findings clearly indicate that Random Forest can serve as a reliable method for financial institutions to enhance credit risk evaluation and minimize potential losses arising from loan defaults.]]>
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