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An analysis of customer retention and insurance claim patterns using data mining: a case study

Author

Listed:
  • K A Smith

    (Monash University)

  • R J Willis

    (Monash University)

  • M Brooks

    (Australian Associated Motor Insurers Limited)

Abstract

The insurance industry is concerned with many problems of interest to the operational research community. This paper presents a case study involving two such problems and solves them using a variety of techniques within the methodology of data mining. The first of these problems is the understanding of customer retention patterns by classifying policy holders as likely to renew or terminate their policies. The second is better understanding claim patterns, and identifying types of policy holders who are more at risk. Each of these problems impacts on the decisions relating to premium pricing, which directly affects profitability. A data mining methodology is used which views the knowledge discovery process within an holistic framework utilising hypothesis testing, statistics, clustering, decision trees, and neural networks at various stages. The impacts of the case study on the insurance company are discussed.

Suggested Citation

  • K A Smith & R J Willis & M Brooks, 2000. "An analysis of customer retention and insurance claim patterns using data mining: a case study," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 51(5), pages 532-541, May.
    • Handle: RePEc:pal:jorsoc:v:51:y:2000:i:5:d:10.1057_palgrave.jors.2600941
    DOI: 10.1057/palgrave.jors.2600941
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    Citations

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    Cited by:

    1. K. W. De Bock & D. Van Den Poel, 2012. "Reconciling Performance and Interpretability in Customer Churn Prediction using Ensemble Learning based on Generalized Additive Models," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 12/805, Ghent University, Faculty of Economics and Business Administration.
    2. Yann Braouezec, 2015. "Public versus Private Insurance System with (and without) Transaction Costs: Optimal Segmentation Policy of an Informed monopolistPublic versus Private Insurance System with (and without) Transaction ," Working Papers 2013-ECO-23, IESEG School of Management, revised May 2014.
    3. Mohamed Hanafy & Ruixing Ming, 2021. "Machine Learning Approaches for Auto Insurance Big Data," Risks, MDPI, vol. 9(2), pages 1-23, February.
    4. 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.
    5. Chhaya Dubey & Dharmendra Kumar & Ashutosh Kumar Singh & Vijay Kumar Dwivedi, 2024. "Applying machine learning models on blockchain platform selection," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 15(8), pages 3643-3656, August.
    6. Sebastian Baran & Przemys{l}aw Rola, 2022. "Prediction of motor insurance claims occurrence as an imbalanced machine learning problem," Papers 2204.06109, arXiv.org.
    7. Pradeep Kautish & Arpita Khare & Rajesh Sharma, 2022. "Health insurance policy renewal: an exploration of reputation, performance, and affect to understand customer inertia," Journal of Marketing Analytics, Palgrave Macmillan, vol. 10(3), pages 261-278, September.
    8. David L. Olson, 2007. "Data mining in business services," Service Business, Springer;Pan-Pacific Business Association, vol. 1(3), pages 181-193, September.
    9. K. W. De Bock & D. Van Den Poel, 2011. "An empirical evaluation of rotation-based ensemble classifiers for customer churn prediction," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 11/717, Ghent University, Faculty of Economics and Business Administration.
    10. Z Hua & S Li & Z Tao, 2006. "A rule-based risk decision-making approach and its application in China's customs inspection decision," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 57(11), pages 1313-1322, November.
    11. Joseph Levitas & Konstantin Yavilberg & Oleg Korol & Genadi Man, 2022. "Prediction of Auto Insurance Risk Based on t-SNE Dimensionality Reduction," Papers 2212.09385, arXiv.org, revised Mar 2023.
    12. Emer Owens & Barry Sheehan & Martin Mullins & Martin Cunneen & Juliane Ressel & German Castignani, 2022. "Explainable Artificial Intelligence (XAI) in Insurance," Risks, MDPI, vol. 10(12), pages 1-50, December.
    13. Bass, Pablo & Donoso, Pedro & Munizaga, Marcela, 2011. "A model to assess public transport demand stability," Transportation Research Part A: Policy and Practice, Elsevier, vol. 45(8), pages 755-764, October.
    14. Huang, Tony Cheng-Kui & Liu, Chuang-Chun & Chang, Dong-Cheng, 2012. "An empirical investigation of factors influencing the adoption of data mining tools," International Journal of Information Management, Elsevier, vol. 32(3), pages 257-270.
    15. Ballings, Michel & Van den Poel, Dirk, 2015. "CRM in social media: Predicting increases in Facebook usage frequency," European Journal of Operational Research, Elsevier, vol. 244(1), pages 248-260.
    16. Glady, Nicolas & Baesens, Bart & Croux, Christophe, 2009. "Modeling churn using customer lifetime value," European Journal of Operational Research, Elsevier, vol. 197(1), pages 402-411, August.
    17. Şerafettin SEVİM & Birol YILDIZ & Nilüfer DALKILIÇ, 2016. "Risk Assessment for Accounting Professional Liability Insurance," Sosyoekonomi Journal, Sosyoekonomi Society, issue 24(29).
    18. Ai Cheo Yeo & Kate A. Smith & Robert J. Willis & Malcolm Brooks, 2001. "Clustering technique for risk classification and prediction of claim costs in the automobile insurance industry," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 10(1), pages 39-50, March.
    19. Dutang, Christophe & Albrecher, Hansjoerg & Loisel, Stéphane, 2013. "Competition among non-life insurers under solvency constraints: A game-theoretic approach," European Journal of Operational Research, Elsevier, vol. 231(3), pages 702-711.
    20. Abdul-Fatawu Majeed, 2020. "Accelerated Failure Time Models: An Application in Insurance Attrition [Modèles de temps de défaillance accéléré: une application dans l'attrition de l'assurance]," Post-Print hal-02953269, HAL.
    21. Nils Mahlow & Joël Wagner, 2016. "Evolution of Strategic Levers in Insurance Claims Management: An Industry Survey," Risk Management and Insurance Review, American Risk and Insurance Association, vol. 19(2), pages 197-223, September.
    22. Meltem Denizel & Behlul Usdiken & Deniz Tuncalp, 2003. "Drift or Shift? Continuity, Change, and International Variation in Knowledge Production in OR/MS," Operations Research, INFORMS, vol. 51(5), pages 711-720, October.
    23. R Fildes & K Nikolopoulos & S F Crone & A A Syntetos, 2008. "Forecasting and operational research: a review," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 59(9), pages 1150-1172, September.

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