Mining Life Insurance Data for Customer Attrition Analysis

Abstract

Customer attrition is an increasingly pressing issue faced by many insurance providers today. Retaining customers who purchase life insurance policies is an even bigger challenge since the policy duration spans for more than twenty years. Data mining techniques play an important role in facilitating these retention efforts. The objective of this study is to analyze customer retention patterns by classifying policy holders who are likely to terminate their policies. These customers who are at high risk of attrition and prospective customers can then be targeted for promotions to reduce the rate of attrition. In this study, data mining techniques such as Decision trees and Neural Networks are employed. Models generated are evaluated using ROC curves are AUC values. Our research also adopts cost sensitive learning strategies to address issues such as imbalanced class labels and unequal misclassification costs. This study also gives recommendations on how insurance providers can act upon the models built.