Analyzing Life Insurance Data with Different Classification Techniques for Customers’ Behavior Analysis

Abstract

Analyzing data of life insurance companies gives an important insight on how the customers are reacting to the offered insurance policies by the companies. This information can be used to predict the behavior of future policy holders. Life insurance companies maintain a large database on their customers and policy related information. Data mining technique applied with proper preprocessing of data prove to be very efficient in extracting hidden information from data stored by life insurance companies. There are many data mining algorithms that can be applied to this huge set of data. The main focus of our work is to apply different classification techniques on the data provided by a life insurance company of Bangladesh. Attribute selection techniques are applied to properly classify the data. Classification techniques proved to be very useful in classifying customers according to their attributes. A comparative analysis of the performance of the classifiers is also reported in this research.