<![CDATA[Financing through loans is common in the financial world, but the loan approval process requires careful assessment to minimize credit risk that may arise. In this study, we propose the application of the Naïve Bayes method as a tool to predict the eligibility of prospective customers in making loans. This research uses historical datasets from banks that include credit information, payment history, and customer profiles. The analysis process begins with data pre-processing to clean and address missing or incomplete data. Next, the extraction of relevant features and selection of variables are carried out to build a prediction model. The Naïve Bayes method was chosen because of its simple and fast nature in classifying data. This model makes use of the assumption of feature independence, which is useful when feature dimensions are high. In the training phase, the Naïve Bayes model will learn from historical datasets to recognize good and bad customer eligibility patterns. The results of this analysis show that the Naïve Bayes method has succeeded in predicting the eligibility of prospective customers with a satisfactory level of accuracy. In addition, the model is able to identify key factors that affect loan eligibility, such as credit history, income, and financial dependents. This study contributes to finance by presenting predictive tools that can help financial institutions make more informed and efficient decisions regarding loan approvals. However, we also acknowledge that the Naïve Bayes method has limitations, such as the assumption of feature independence, which must be considered in the interpretation of prediction results.]]>
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