Determining Customer Risk Factors in an Insurance Company through Data Mining Analysis

Abstract

In recent years, data mining applications have been widely used in academic and sectoral studies as they provide speed and flexibility to sectors in areas such as decision support systems, market strategy, and financial forecasts. Data mining tools, which enable access to meaningful information in data stacks, contribute to increasing the sustainability level of companies. Data mining techniques are widely used in customer relationship management to improve flexibility, agility, and the ability to meet changing dynamics in customer demands. In this way, while enhancing the customer experience of companies, customer satisfaction and loyalty are also increased. This study aims to investigate the variables that affect the insurance premium value that customers will pay in an insurance company and the effect of the services provided by agencies on the insurance premium. Within the scope of the study, information on 150 customers of an insurance company operating in Ankara who have traffic insurance and the vehicle to be insured was collected, analyzed, and classified using data mining techniques. As a result of the study, the variable that affects the insurance premium the most is the traffic insurance level, i.e. the driver's no-claims status. Whether the services provided by the agency affect the determination of the insurance premium was investigated. As a result, no difference was found in the utilization of the services.