An analysis of customer retention and insurance claim patterns using data mining: a case study
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DOI: 10.1057/palgrave.jors.2600941
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- 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.
- 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.
- 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.
- 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.
- 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.
- Şerafettin SEVİM & Birol YILDIZ & Nilüfer DALKILIÇ, 2016. "Risk Assessment for Accounting Professional Liability Insurance," Sosyoekonomi Journal, Sosyoekonomi Society, issue 24(29).
- 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.
- 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.
- Christophe Dutang & Hansjoerg Albrecher & Stéphane Loisel, 2013. "Competition among non-life insurers under solvency constraints: A game-theoretic approach," Post-Print hal-00746245, HAL.
- Christophe Dutang & Hansjoerg Albrecher & Stéphane Loisel, 2013. "Competition among non-life insurers under solvency constraints: A game-theoretic approach," Post-Print hal-01616156, HAL.
- 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.
- K.W. de Bock & D. van den Poel, 2011. "An empirical evaluation of rotation-based ensemble classifiers for customer churn prediction," Post-Print hal-00800160, HAL.
- 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.
- 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.
- K. Coussement & K.W. de Bock, 2013. "Customer Churn Prediction in the Online Gambling Industry: The Beneficial Effect of Ensemble Learning," Post-Print hal-00788063, HAL.
- 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.
- Sebastian Baran & Przemys{l}aw Rola, 2022. "Prediction of motor insurance claims occurrence as an imbalanced machine learning problem," Papers 2204.06109, arXiv.org.
- David L. Olson, 2007. "Data mining in business services," Service Business, Springer;Pan-Pacific Business Association, vol. 1(3), pages 181-193, September.
- 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.
- 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.
- 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.
- 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.
- 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.
- Mohamed Hanafy & Ruixing Ming, 2021. "Machine Learning Approaches for Auto Insurance Big Data," Risks, MDPI, vol. 9(2), pages 1-23, February.
- 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.
- 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.
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Keywords
data mining; insurance; neural networks; classification; clustering; case study;All these keywords.
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