Classification of Customer Credit Risk Levels Using the Random Forest Method: A Case Study on Microfinance Institutions

Credit risk classification plays a crucial role in supporting financial institutions, especially microfinance institutions, in assessing the ability of customers to repay loans. This study aims to develop a credit risk classification model using the Random Forest method, which is known for its accuracy and robustness in handling classification problems. The research uses a dataset obtained from a microfinance institution consisting of various customer attributes such as income, age, loan amount, repayment history, and employment status. The dataset is preprocessed and divided into training and testing sets to evaluate model performance. The Random Forest algorithm is then applied to build a classification model that categorizes customers into three credit risk levels: low, medium, and high. The results show that the Random Forest model achieves a high level of accuracy, with a classification precision of 89%, recall of 87%, and F1-score of 88%. These findings indicate that Random Forest is an effective technique for credit risk classification and can be implemented by microfinance institutions to support better decision-making in credit approval processes. This research also highlights the potential of machine learning techniques in enhancing credit risk management and minimizing non-performing loans.