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Implementation of penalized survival models in churn prediction of vehicle insurance

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  • Chen, Yan
  • Zhang, Lei
  • Zhao, Yulu
  • Xu, Bing

Abstract

The insurance industry plays an indispensable role in the financial structure of today’s world. Vehicle insurance accounts for a large part of the income of insurance companies, and retaining regular clients has proved economical, making car insurance clients’ churn management a priority. Meanwhile, clients’ churn will determine companies’ financial status and reflect the service and management level of the company. This paper proposes a new model, i.e., combined the Cox model with variable penalties (Lasso, SCAD, and MCP) to model the clients’ churn problems and determine the crucial factors that affect clients’ decisions, based on their personal information and behavior data, which are provided by a large insurance company in China. This model is proved to be successful in identifying the client churning and making comparisons among the penalties. The variable penalties model reveals the most important factor about clients’ churn problems, which can provide a reliable basis for the product development of insurance companies. MCP creates the greatest sparsity but consequently loses some information. We suggest using SCAD in the model, as it balances the sparsity and information reservation. Furthermore, a new approach is proposed to building a dynamic threshold of churning probabilities, which companies can use to manage their clients more flexibly. In this way, clients who are prone to churn can be identified in advance, and the maintenance cost of client management can be reduced accordingly.

Suggested Citation

  • Chen, Yan & Zhang, Lei & Zhao, Yulu & Xu, Bing, 2022. "Implementation of penalized survival models in churn prediction of vehicle insurance," Journal of Business Research, Elsevier, vol. 153(C), pages 162-171.
    • Handle: RePEc:eee:jbrese:v:153:y:2022:i:c:p:162-171
    DOI: 10.1016/j.jbusres.2022.07.015
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    1. Roel Verbelen & Katrien Antonio & Gerda Claeskens, 2018. "Unravelling the predictive power of telematics data in car insurance pricing," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 67(5), pages 1275-1304, November.
    2. Amin, Adnan & Al-Obeidat, Feras & Shah, Babar & Adnan, Awais & Loo, Jonathan & Anwar, Sajid, 2019. "Customer churn prediction in telecommunication industry using data certainty," Journal of Business Research, Elsevier, vol. 94(C), pages 290-301.
    3. Lee, Chien-Chiang & Chang, Chi-Hung & Arouri, Mohamed & Lee, Chi-Chuan, 2016. "Economic growth and insurance development: The role of institutional environments," Economic Modelling, Elsevier, vol. 59(C), pages 361-369.
    4. Djeundje, Viani Biatat & Crook, Jonathan, 2019. "Dynamic survival models with varying coefficients for credit risks," European Journal of Operational Research, Elsevier, vol. 275(1), pages 319-333.
    5. Coussement, Kristof & De Bock, Koen W., 2013. "Customer churn prediction in the online gambling industry: The beneficial effect of ensemble learning," Journal of Business Research, Elsevier, vol. 66(9), pages 1629-1636.
    6. Fan J. & Li R., 2001. "Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 1348-1360, December.
    7. Li, Yixin & Hou, Bingzhang & Wu, Yue & Zhao, Donglai & Xie, Aoran & Zou, Peng, 2021. "Giant fight: Customer churn prediction in traditional broadcast industry," Journal of Business Research, Elsevier, vol. 131(C), pages 630-639.
    8. Li, Yingying & Li, Ying & Li, Yan, 2019. "What factors are influencing credit card customer’s default behavior in China? A study based on survival analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 526(C).
    9. Holmlund, Maria & Van Vaerenbergh, Yves & Ciuchita, Robert & Ravald, Annika & Sarantopoulos, Panagiotis & Ordenes, Francisco Villarroel & Zaki, Mohamed, 2020. "Customer experience management in the age of big data analytics: A strategic framework," Journal of Business Research, Elsevier, vol. 116(C), pages 356-365.
    10. De Caigny, Arno & Coussement, Kristof & De Bock, Koen W. & Lessmann, Stefan, 2020. "Incorporating textual information in customer churn prediction models based on a convolutional neural network," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1563-1578.
    11. Arno de Caigny & Kristof Coussement & Koen W. de Bock, 2018. "A new hybrid classification algorithm for customer churn prediction based on logistic regression and decision trees," Post-Print hal-01741661, HAL.
    12. Cooper, Marjorie J. & Gwin, Carol F. & Wakefield, Kirk L., 2008. "Cross-functional interface and disruption in CRM projects: Is marketing from Venus and information systems from Mars?," Journal of Business Research, Elsevier, vol. 61(4), pages 292-299, April.
    13. Eling, Martin & Jia, Ruo, 2019. "Efficiency and profitability in the global insurance industry," Pacific-Basin Finance Journal, Elsevier, vol. 57(C).
    14. Ashraf, Badar Nadeem & Zheng, Changjun & Jiang, Chonghui & Qian, Ningyu, 2020. "Capital regulation, deposit insurance and bank risk: International evidence from normal and crisis periods," Research in International Business and Finance, Elsevier, vol. 52(C).
    15. To, Chester K.M. & Chau, K.P. & Kan, Chi Wai, 2020. "The logic of innovative value proposition: A schema for characterizing and predicting business model evolution," Journal of Business Research, Elsevier, vol. 112(C), pages 502-520.
    16. K. Coussement & D. Van Den Poel, 2006. "Churn Prediction in Subscription Services: an Application of Support Vector Machines While Comparing Two Parameter-Selection Techniques," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 06/412, Ghent University, Faculty of Economics and Business Administration.
    17. Ko, Eunju & Kim, Sook Hyun & Kim, Myungsoo & Woo, Ji Young, 2008. "Organizational characteristics and the CRM adoption process," Journal of Business Research, Elsevier, vol. 61(1), pages 65-74, January.
    18. Liu, Hailong & Li, Rui & Yuan, Jinjian, 2018. "Deposit insurance pricing under GARCH," Finance Research Letters, Elsevier, vol. 26(C), pages 242-249.
    19. De Caigny, Arno & Coussement, Kristof & De Bock, Koen W., 2018. "A new hybrid classification algorithm for customer churn prediction based on logistic regression and decision trees," European Journal of Operational Research, Elsevier, vol. 269(2), pages 760-772.
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    1. Yoganathan, Vignesh & Osburg, Victoria-Sophie, 2024. "The mind in the machine: Estimating mind perception's effect on user satisfaction with voice-based conversational agents," Journal of Business Research, Elsevier, vol. 175(C).

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