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.
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Kirlidog, M., Asuk, C.: A fraud detection approach with data mining in health insurance. Proc.-Soc. Behav. Sci. 62, 989–994 (2012)
Xiaoyun, W., Danyue, L.: Hybrid outlier mining algorithm based evaluation of client moral risk in insurance company. In: 2010 The 2nd IEEE International Conference on Information Management and Engineering (ICIME), pp. 585–589. IEEE (2010)
Yan, Y., Xie, H.: Research on the application of data mining technology in insurance informatization. In: Ninth International Conference on Hybrid Intelligent Systems, 2009. HIS’09, vol. 3, pp. 202–205 (2009)
Goonetilleke, T.O., Caldera, H.A.: Mining life insurance data for customer attrition analysis. J. Ind. Intell. Inf. 1(1)
Thakur, S.S., Sing, J.K.: Mining customer’s data for vehicle insurance prediction system using k-means clustering—an application. Int. J. Comput. Appl. Eng. Sci. 3(4), 148 (2013)
Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: SMOTE: synthetic minority over-sampling technique. J. Artif. Intell. Res. 321–357 (2002)
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Saidur Rahman, M., Arefin, K.Z., Masud, S., Sultana, S., Rahman, R.M. (2017). Analyzing Life Insurance Data with Different Classification Techniques for Customers’ Behavior Analysis. In: Król, D., Nguyen, N., Shirai, K. (eds) Advanced Topics in Intelligent Information and Database Systems. ACIIDS 2017. Studies in Computational Intelligence, vol 710. Springer, Cham. https://doi.org/10.1007/978-3-319-56660-3_2
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DOI: https://doi.org/10.1007/978-3-319-56660-3_2
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