Bagging Vs. Boosting in Ensemble Machine Learning? An Integrated Application to Fraud Risk Analysis in the Insurance Sector

References

• Abakarim, Y., M. Lahby, and A. Attioui. 2023. A bagged ensemble convolutional neural networks approach to recognize insurance claim frauds. Applied System Innovation 6 (1):20. doi:10.3390/asi6010020
   Google Scholar
• Ang, J. C., A. Mirzal, H. Haron, and H. Nuzly Abdull Hamed. 2016. Supervised, unsupervised, and semi-supervised feature selection: A review on gene selection. IEEE/ACM Transactions on Computational Biology and Bioinformatics 13 (5):971–34. doi:10.1109/TCBB.2015.2478454
   PubMed Web of Science ®Google Scholar
• Anurag, M., M. Kaur Saggi, S. Rehman, H. Sajjad, S. Inyurt, A. Singh Bhatia, A. Ahsan Farooque, A. Y. Oudah, and Z. Mundher Yaseen. 2022. Deep learning versus gradient boosting machine for pan evaporation prediction. Engineering Applications of Computational Fluid Mechanics 16 (1):570–87. doi:10.1080/19942060.2022.2027273
   Web of Science ®Google Scholar
• Arai, H., C. Maung, K. Xu, and H. Schweitzer. 2016. Unsupervised feature selection by heuristic search with provable bounds on suboptimality. Proceedings of the AAAI Conference on Artificial Intelligence 30 (1). doi:10.1609/aaai.v30i1.10082
   Google Scholar
• Aslam, F., A. Imran, Z. Ftiti, W. Louhichi, and T. Shams. 2022. Research in international business and finance insurance fraud detection: evidence from artificial intelligence and machine learning. Research in International Business and Finance 62 (August):101744. doi:10.1016/j.ribaf.2022.101744
   Google Scholar
• Azzone, M., E. Barucci, G. Giuffra Moncayo, and D. Marazzina. 2022. A machine learning model for lapse prediction in life insurance contracts. Expert Systems with Applications 191 (April 2021):116261. doi:10.1016/j.eswa.2021.116261
   Google Scholar
• Batista, G. E. A. P. A., R. C. Prati, and M. Carolina Monard. 2004. A study of the behavior of several methods for balancing machine learning training data. ACM SIGKDD Explorations Newsletter 6 (1):20–29. doi:10.1145/1007730.1007735
   Google Scholar
• Benedek, B., C. Ciumas, and B. Zsolt Nagy. 2022. Automobile insurance fraud detection in the age of big data – A systematic and comprehensive literature review. Journal of Financial Regulation & Compliance 30 (4):503–23. doi:10.1108/JFRC-11-2021-0102
   Web of Science ®Google Scholar
• Boodhun, N., and M. Jayabalan. 2018. Risk prediction in life insurance industry using supervised learning algorithms. Complex & Intelligent Systems 4 (2):145–54. doi:10.1007/s40747-018-0072-1
   Web of Science ®Google Scholar
• Boyd, K., K. H. Eng, and C. D. Page. 2013. Erratum: Area under the Precision-Recall Curve: Point Estimates and Confidence Intervals. In Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2013. Lecture Notes in Computer Science, ed. H. Blockeel, K. Kersting, S. Nijssen, and F. Železný, vol. 8190. Berlin, Heidelberg: Springer. doi:10.1007/978-3-642-40994-3_55
   Google Scholar
• Bradley, A. P. 1997. The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recognition 30 (7):1145–59. doi:10.1016/S0031-3203(96)00142-2
   Web of Science ®Google Scholar
• Breiman, L. 1996. Bagging predictors. Machine Learning 24 (2):123–40. doi:10.1007/BF00058655
   Web of Science ®Google Scholar
• Caruana, M. A., and L. Grech. 2021. Automobile insurance fraud detection. Communications in Statistics Case Studies, Data Analysis and Applications 7 (4):520–35. doi:10.1080/23737484.2021.1986169
   Google Scholar
• Chawla, N. V., K. W. Bowyer, L. O. Hall, and W. P. Kegelmeyer. 2002. SMOTE: Synthetic minority over-sampling technique. Journal of Artificial Intelligence Research 16:321–57. doi:10.1613/jair.953
   Web of Science ®Google Scholar
• Chizi, B., and O. Maimon. 2009. Dimension reduction and feature selection. In Data mining and knowledge discovery handbook, 83–100. Boston, MA: Springer US. doi:10.1007/978-0-387-09823-4_5
   Google Scholar
• Cinaroglu, S. 2020. Modelling unbalanced catastrophic health expenditure data by using machine-learning methods. Intelligent Systems in Accounting, Finance and Management 27 (4):168–81. doi:10.1002/isaf.1483
   Web of Science ®Google Scholar
• Cutler, A., D. R. Cutler, and R. S. John. 2012. Random forests. In Ensemble machine learning, 157–75. Boston, MA: Springer US. doi:10.1007/978-1-4419-9326-7_5
   Google Scholar
• Davis, J., and M. Goadrich. 2006. The relationship between precision-recall and ROC curves. In Proceedings of the 23rd international conference on Machine learning - ICML ’06, New York, New York, USA, 233–40. ACM Press.
   Google Scholar
• Dhieb, N., H. Ghazzai, H. Besbes, and Y. Massoud. 2019. Extreme gradient boosting machine learning algorithm for safe auto insurance operations. In 2019 IEEE International Conference on Vehicular Electronics and Safety, ICVES 2019, 1–5. doi: 10.1109/ICVES.2019.8906396
   Google Scholar
• Dhieb, N., H. Ghazzai, H. Besbes, and Y. Massoud. 2020. A secure AI-Driven architecture for automated insurance systems: Fraud detection and risk measurement. IEEE Access 8:58546–58. doi:10.1109/ACCESS.2020.2983300
   Web of Science ®Google Scholar
• Farquad, M. A. H., V. Ravi, and S. Bapi Raju. 2012. Analytical CRM in banking and finance using SVM: A modified active learning-based rule extraction approach. International Journal of Electronic Customer Relationship Management 6 (1):48. doi:10.1504/IJECRM.2012.046470
   Google Scholar
• Friedman, J. H. 2001. Greedy function approximation: A gradient boosting machine. The Annals of Statistics 29 (5):1189–232. doi:10.1214/aos/1013203451
   Web of Science ®Google Scholar
• Gomes, C., Z. Jin, and H. Yang. 2021. Insurance fraud detection with unsupervised deep learning. Journal of Risk and Insurance 88 (3):591–624. doi:10.1111/jori.12359
   Web of Science ®Google Scholar
• Gowda, K., and G. Krishna. 1979. The condensed nearest neighbor rule using the concept of mutual nearest neighborhood (Corresp.). IEEE Transactions on Information Theory 25 (4):488–90. doi:10.1109/TIT.1979.1056066
   Web of Science ®Google Scholar
• Guoming, Z., X. Zhang, M. Bilal, W. Dou, X. Xu, and J. J. P. C. Rodrigues. 2022. Identifying fraud in medical insurance based on blockchain and deep learning. Future Generation Computer Systems 130:140–54. doi:10.1016/j.future.2021.12.006
   Web of Science ®Google Scholar
• Guo, J., and W. Zhu. 2018. Dependence guided unsupervised feature selection. Proceedings of the AAAI Conference on Artificial Intelligence 32 (1). doi:10.1609/aaai.v32i1.11904
   Google Scholar
• Hanafy, M., and R. Ming. 2021a. Improving imbalanced data classification in auto insurance by the data level approaches. International Journal of Advanced Computer Science and Applications 12 (6):493–99. doi:10.14569/IJACSA.2021.0120656
   Web of Science ®Google Scholar
• Hanafy, M., and R. Ming. 2021b. Machine learning approaches for auto insurance big data. Risks 9 (2):1–23. doi:10.3390/risks9020042
   Web of Science ®Google Scholar
• Hanafy, M., and R. Ming. 2021c. Using machine learning models to compare various resampling methods in predicting insurance fraud. Journal of Theoretical and Applied Information Technology 99 (12):2819–33.
   Google Scholar
• Hanafy, M., and R. Ming. 2022. Classification of the insureds using integrated machine learning algorithms: a comparative study. Applied Artificial Intelligence 36 (1). doi: 10.1080/08839514.2021.2020489
   Web of Science ®Google Scholar
• Harjai, S., S. Kumar Khatri, and G. Singh. 2019. Detecting fraudulent insurance claims using random forests and synthetic minority oversampling technique. In 2019 4th International Conference on Information Systems and Computer Networks, ISCON 2019, 123–28. doi: 10.1109/ISCON47742.2019.9036162
   Google Scholar
• Hassan, A. K. I., and A. Abraham. 2016. Modeling insurance fraud detection using imbalanced data classification. Advances in Intelligent Systems & Computing 419:117–27. doi:10.1007/978-3-319-27400-3_11
   Google Scholar
• He, H., Y. Bai, E. A. Garcia, and S. Li. 2008. ADASYN: Adaptive synthetic sampling approach for imbalanced learning. In 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence), 1322–28. IEEE. doi:10.1109/IJCNN.2008.4633969
   Google Scholar
• Henckaerts, R., M. Pier Côté, K. Antonio, and R. Verbelen. 2020. Boosting insights in insurance tariff plans with tree-based machine learning methods. North American Actuarial Journal 25 (2):1–31. doi:10.1080/10920277.2020.1745656
   Web of Science ®Google Scholar
• Hu, C., Z. Quan, and W. Fung Chong. 2022. Imbalanced learning for insurance using modified loss functions in tree-based models. Insurance: Mathematics and Economics 106:13–32. doi:10.1016/j.insmatheco.2022.04.010
   Web of Science ®Google Scholar
• Kate, P., V. Ravi, and A. Gangwar. 2022. FinGAN: Chaotic generative adversarial network for analytical customer relationship management in banking and insurance. Neural Computing and Applications 35 (8):6015–28. doi:10.1007/s00521-022-07968-x
   Web of Science ®Google Scholar
• Kaushik, K., A. Bhardwaj, A. Dhar Dwivedi, and R. Singh. 2022. Machine learning-based regression framework to predict health insurance premiums. International Journal of Environmental Research Public Health 19 (13):7898. doi:10.3390/ijerph19137898
   PubMed Web of Science ®Google Scholar
• Kotb, M. H., and R. Ming. 2021. Comparing SMOTE family techniques in predicting insurance premium defaulting using machine learning models. International Journal of Advanced Computer Science and Applications 12 (9):621–29. doi:10.14569/IJACSA.2021.0120970
   Web of Science ®Google Scholar
• Kubat, M., and S. Matwin. 1997. Addressing the curse of imbalanced data sets: one-sided sampling. In Proceedings of the Fourteenth International Conference on Machine Learning, Nashville, TN, USA, July 8–12, 179–86.
   Google Scholar
• Kumar, S., S. Bhatachharya, R. Pradhan, S. Biswal, S. M. Thampi, and E.-S. M. El-Alfy. 2019. Fuzzy clustering using salp swarm algorithm for automobile insurance fraud detection. Journal of Intelligent & Fuzzy Systems 36 (3):2333–44. doi:10.3233/JIFS-169944
   Web of Science ®Google Scholar
• Laurikkala, J. 2001. Improving Identification of Difficult Small Classes by Balancing Class Distribution. In Artificial Intelligence in Medicine. AIME 2001. Lecture Notes in Computer Science, ed. S. Quaglini, P. Barahona, and S. Andreassen, vol. 2101. Berlin, Heidelberg: Springer. doi:10.1007/3-540-48229-6_9
   Google Scholar
• Li, Q., H. Chen, H. Huang, X. Zhao, Z. Cai, C. Tong, W. Liu, and X. Tian. 2017. An enhanced grey wolf optimization based machine for medical diagnosis. Computational & Mathematical Methods in Medicine 2017:1–15. doi:10.1155/2017/9512741
   Web of Science ®Google Scholar
• Li, Y., C. Yan, W. Liu, and M. Li. 2018. A principle component analysis-based random forest with the potential nearest neighbor method for automobile insurance fraud identification. Applied Soft Computing Journal 70:1000–09. doi:10.1016/j.asoc.2017.07.027
   Web of Science ®Google Scholar
• Maiano, L., A. Montuschi, M. Caserio, E. Ferri, F. Kieffer, C. Germanò, L. Baiocco, L. R. Celsi, I. Amerini, and A. Anagnostopoulos. 2023. A deep-learning–based antifraud system for car-insurance claims. Expert Systems with Applications 231:120644. doi:10.1016/j.eswa.2023.120644
   Web of Science ®Google Scholar
• Majhi, S. K. 2021. Fuzzy clustering algorithm based on modified whale optimization algorithm for automobile insurance fraud detection. Evolutionary Intelligence 14 (1):35–46. doi:10.1007/s12065-019-00260-3
   Web of Science ®Google Scholar
• Mohamed, H., and M. Omar. 2021. Predict health insurance cost by using machine learning and DNN regression models. International Journal of Innovative Technology and Exploring Engineering 10 (3):137–43. doi:10.35940/ijitee.C8364.0110321
   Google Scholar
• Nian, K., H. Zhang, A. Tayal, T. Coleman, and Y. Li. 2016. Auto insurance fraud detection using unsupervised spectral ranking for anomaly. The Journal of Finance and Data Science 2 (1):58–75. doi:10.1016/j.jfds.2016.03.001
   Google Scholar
• Oyedele, A., A. Ajayi, L. O. Oyedele, J. Manuel Davila Delgado, L. Akanbi, O. Akinade, H. Owolabi, and M. Bilal. 2021. Deep learning and boosted trees for injuries prediction in power infrastructure projects. Applied Soft Computing 110:107587. doi:10.1016/j.asoc.2021.107587
   Web of Science ®Google Scholar
• Ozenne, B., F. Subtil, and D. Maucort-Boulch. 2015. The precision–recall curve overcame the optimism of the receiver operating characteristic curve in rare diseases. Journal of Clinical Epidemiology 68 (8):855–59. doi:10.1016/j.jclinepi.2015.02.010
   PubMed Web of Science ®Google Scholar
• Pesantez-Narvaez, J., M. Guillen, and M. Alcañiz. 2019. Predicting motor insurance claims using telematics data—XGboost versus logistic regression. Risks 7 (2). doi:10.3390/risks7020070
   Web of Science ®Google Scholar
• Robnik-Sikonja, M., and I. Kononenko. 2008. Explaining classifications for individual instances. IEEE Transactions on Knowledge and Data Engineering 20 (5):589–600. doi:10.1109/TKDE.2007.190734
   Web of Science ®Google Scholar
• Saito, T., R. Marc, and G. Brock. 2015. The precision-recall plot is more informative than the ROC plot when evaluating binary classifiers on imbalanced datasets edited by G. Brock. PLOS ONE 10 (3):e0118432. doi:10.1371/journal.pone.0118432
   PubMed Web of Science ®Google Scholar
• Salmi, M. and D. Atif. 2022. Using a Data Mining Approach to Detect Automobile Insurance Fraud. In Proceedings of the 13th International Conference on Soft Computing and Pattern Recognition (SoCPaR 2021). SoCPaR 2021. Lecture Notes in Networks and Systems, ed. A. Abraham, vol. 417. Springer, Cham. doi:10.1007/978-3-030-96302-6_5
   Google Scholar
• Seema, R., A. Rawat, D. Kumar, and A. Sai Sabitha. 2021. Application of machine learning and data visualization techniques for decision support in the insurance sector. International Journal of Information Management Data Insights 1 (2):100012. doi:10.1016/j.jjimei.2021.100012
   Google Scholar
• Severino, M. K., and Y. Peng. 2021. Machine learning algorithms for fraud prediction in property insurance: Empirical evidence using real-world microdata. Machine Learning with Applications 5 (June):100074. doi:10.1016/j.mlwa.2021.100074
   Google Scholar
• Solorio-Fernández, S., J. A. Carrasco-Ochoa, and J. F. Martínez-Trinidad. 2020. A review of unsupervised feature selection methods. Artificial Intelligence Review 53 (2):907–48. doi:10.1007/s10462-019-09682-y
   Web of Science ®Google Scholar
• Sundarkumar, G., V. R. Ganesh, and V. Siddeshwar. 2016. One-class support vector machine based undersampling: application to churn prediction and insurance fraud detection. In 2015 IEEE International Conference on Computational Intelligence and Computing Research, ICCIC 2015 (ii). doi: 10.1109/ICCIC.2015.7435726
   Google Scholar
• Sundarkumar, G. G., and V. Ravi. 2015. A novel hybrid undersampling method for mining unbalanced datasets in banking and insurance. Engineering Applications of Artificial Intelligence 37:368–77. doi:10.1016/j.engappai.2014.09.019
   Web of Science ®Google Scholar
• Taha, A., B. Cosgrave, and S. McKeever. 2022. Using feature selection with machine learning for generation of insurance insights. Applied Sciences (Switzerland) 12 (6):3209. doi:10.3390/app12063209
   Web of Science ®Google Scholar
• Ul Hassan, C. A., J. Iqbal, S. Hussain, H. AlSalman, M. A. A. Mosleh, S. Sajid Ullah, and E. Rak. 2021. A computational intelligence approach for predicting medical insurance cost. Mathematical Problems in Engineering 2021:1–13. doi:10.1155/2021/1162553
   Web of Science ®Google Scholar
• Uysal, A. K., and S. Gunal. 2012. A novel probabilistic feature selection method for text classification. Knowledge-Based Systems 36:226–35. doi:10.1016/j.knosys.2012.06.005
   Web of Science ®Google Scholar
• Vasu, M., and V. Ravi. 2011. A hybrid under-sampling approach for mining unbalanced datasets: Applications to banking and insurance. International Journal of Data Mining, Modelling and Management 3 (1):75. doi:10.1504/IJDMMM.2011.038812
   Google Scholar
• Vijaya, J. N. J., and J. Vijaya. 2022. Boost customer churn prediction in the insurance industry using meta heuristic models. International Journal of Information Technology 14 (5):2619–31. doi:10.1007/s41870-022-01017-5
   Google Scholar
• Vosseler, A. 2022. Unsupervised insurance fraud prediction based on anomaly detector ensembles. Risks 10 (7):132. doi:10.3390/risks10070132
   Web of Science ®Google Scholar
• Wang, S., J. Tang, and H. Liu. 2015. Embedded unsupervised feature selection. Proceedings of the AAAI Conference on Artificial Intelligence 29 (1). doi: 10.1609/aaai.v29i1.9211
   Google Scholar
• Wilson, D. L. 1972. Asymptotic properties of nearest neighbor rules using edited data. IEEE Transactions on Systems, Man, and Cybernetics SMC 2 (3):408–21. doi:10.1109/TSMC.1972.4309137
   Web of Science ®Google Scholar
• Xu, B., Y. Wang, X. Liao, and K. Wang. 2023. Efficient fraud detection using deep boosting decision trees. Decision Support Systems 175:114037. doi:10.1016/j.dss.2023.114037
   Web of Science ®Google Scholar
• Yankol-Schalck, M. 2022. The value of cross-data set analysis for automobile insurance fraud detection. Research in International Business and Finance 63 (August):101769. doi:10.1016/j.ribaf.2022.101769
   Google Scholar
• Yan, C., Y. Li, W. Liu, M. Li, J. Chen, and L. Wang. 2020. An artificial bee colony-based kernel ridge regression for automobile insurance fraud identification. Neurocomputing 393:115–25. doi:10.1016/j.neucom.2017.12.072
   Web of Science ®Google Scholar