Optimizing Insurance Customer Segmentation with C4.5 Decision Tree Algorithm - Sigit Candra Setya; Moch. Iswan Perangin-angin; Marsono Marsono; Asyahri Hadi Nasyuha; Lucia Nugraheni Harnaningrum (Journal of Information System Research (JOSH), 2025)
Insurance companies rely on premium payments as their primary source of revenue. However, economic instability often causes delays in premium payments, impacting revenue recording. This study applies the C4.5 Decision Tree algorithm to classify insurance customers based on premium amount, age, income, and claim history, thereby improving product recommendations. The research utilizes data mining techniques to analyze customer attributes and generate decision rules for optimal insurance product selection. The findings indicate that customers with a premium of IDR 500,000 are best suited for PRUMed Cover (PMC), while those with IDR 1,000,000 are recommended PRUCritical Benefit 88 (PCB88). For customers with IDR 750,000, additional factors such as age and income level influence the recommended insurance type. The entropy and information gain calculations identify premium amount as the most significant attribute for decision-making, followed by age, income, and claim history. By implementing this method, insurance companies can enhance customer segmentation, streamline product selection, and optimize marketing strategies. The transparent and interpretable decision tree structure ensures regulatory compliance while improving customer satisfaction. Future research should explore additional variables, such as behavioral data and regional trends, and compare C4.5 with other classification algorithms like Random Forest or Support Vector Machines (SVM) to enhance accuracy and scalability.