A Review of Data Analytical Approaches in the Insurance Industry

Insurance companies are facing a growth in their transactional data. Valuable information can be gained from the data collected by using analytical approaches. The key areas in which data analytics can be useful to insurance firms is customer level analytics, risk assessment and prediction as well as fraud detection. Commonly used techniques to identify patterns in the data sets are clustering and classification to be able to predict future occurrences of events. The main purpose of implementing analytics among the insurance firms is to better understand their customers, minimize their losses and gain a competitive advantage in the market. A review of several algorithms used to analyse insurance data is provided in this paper together with some evaluations of the different approaches.