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Mining Life Insurance Data for Customer Attrition Analysis

T. L. Oshini Goonetilleke 1 and H. A. Caldera 2
1. Informatics Institute of Technology/Department of Computing, Colombo, Sri Lanka
2. University of Colombo/School of Computing, Colombo, Sri Lanka
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. Companies are eager to reduce these attrition rates in the customer-base by analyzing operational data. Data mining techniques play an important role in facilitating these retention efforts. The objective of this study is to analyse customer attrition by classifying all policy holders who are likely to terminate their policies. These customers who are at high risk of attrition can then be targeted for promotions to reduce the rate of attrition. Data mining techniques such as Decision trees and Neural Networks are employed in this study. Models generated are evaluated using ROC curves and AUC values. Our research also adopts cost sensitive learning strategies to address issues such as imbalanced class labels and unequal misclassification costs. 

Index Terms—classification, cost sensitive learning, customer attrition, data mining, life insurance.

Cite: T. L. Oshini Goonetilleke and H. A. Caldera, "Mining Life Insurance Data for Customer Attrition Analysis," Journal of Industrial and Intelligent Information, Vol. 1, No. 1, pp. 52-58, March 2013. doi: 10.12720/jiii.1.1.52-58
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