Logistic Regression Model for Lapse Analysis of Life Insurance Policy
Abstract
The purpose of this research is to create the logistic regression model for forecasting life insurance lapse policy. The dependent variable is lapse class that is dichotomous qualitative variable. There are 18 independent variables which are qualitative or quantitative. The data set is divided into 2 parts; training set of 1,864 policies for building the predicting equation and testing set of 466 policies. The results show that the predicting equation consist of 6 independent variables which are age, face amount (between 50,001 – 100,000), duration of payment (more than 3 years), square root of duration of protection and occupation class (class 3 and class 4). The logistic regression equation predict that the lapse rate of insured is 31.76% and the accuracy performance of forecasting is 66.95%.References
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Application of Predictive Modeling. Society of Actuaries, 1-22.
Zian, J., Miller, A. & Ducuroir, F. (2016). Lapse Rate Models in Life Insurance and a Practical Method to Foresee
Interest Rate Dependencies. A Reacfin White Paper on Life Insurance, 1-20.
Attention to Nursing Domain. Journal of Korean Academy of Nursing, 43(2), 154-164.
Allison, D.P. (2014). Measures of Fit for Logistic Regression. SAS Global Forum, 1-12.
Hilbe, M.J. (2009). Logistic Regression Models. (1). United States of America: CRC Press.
Josephat, K.P. & Ame, A. (2018). Effect on Testing Logistic Regression Assumption on the Improvement of the
Propensity Scores. International Journal of Statistics and Applications, 8(1), 9-17.
Kaiyawan, Y. (2012). Principle and Using Logistic Regression Analysis for Research. Journal of Rajamongala
University of Technology Srivijaya, 4(1), 1-12. (in Thai)
Kleinbaum, G.D. & Klein, M. (2002) Logistic Regression: A Self-Learning Text. (2). United States of America:
Springer.
Office of Insurance Commission. (2016). Life insurance. Retrieved August 1, 2018, from http://www.oic.or.th/en/
consumer/insurance/about/life.
Peng, J., Lee L.K. & Ingersoll M.G. (2002). An Introduction to Logistic Regression Analysis and Reporting.
Journal of Educational Research, 96(1), 3-14.
Sinsomboonthong, S. (2016). Multivariate Analysis. (1). Bangkok: Chulalongkorn University Press. (in Thai)
Tabachnick, G.B. & Fidell, S.L. (2013). Using Multivariate Statistics. (6). United States of America: Courier
Companies.
Thai Life Assurance Association, (2018). Overview of Thai Life Assurance business in 2017 and Trend of Thai
Life Assurance business in 2018. Journal of Life Assurance, 38(1), 1-32. (In Thai)
Xu, R., Lai, D., Cao, M., Rushing, S. & Rozar, T. (2015). Lapse Modeling for the Post-Level Period: A Practical
Application of Predictive Modeling. Society of Actuaries, 1-22.
Zian, J., Miller, A. & Ducuroir, F. (2016). Lapse Rate Models in Life Insurance and a Practical Method to Foresee
Interest Rate Dependencies. A Reacfin White Paper on Life Insurance, 1-20.
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Published
2019-06-21
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Research Article
