References
- 1Aggarwal, CC. 2015.
Outlier analysis . In Data mining. Springer, pp. 237–263. DOI: 10.1007/978-3-319-14142-8_8 - 2Ahuja, MS and Singh, L. 2017. ‘Online fraud detection-a review’. International Research Journal of Engineering and Technology, 4(7): 2509–2515.
- 3Aleskerov, E, Freisleben, B and Rao, B. 1997.
Cardwatch: A neural network based database mining system for credit card fraud detection . In Computational Intelligence for Financial Engineering (CIFEr). IEEE, pp. 220–226. - 4Barnett, V and Lewis, T. 1994. Outliers in statistical data. Wiley Chichester.
- 5Belhadji, EB, Dionne, G and Tarkhani, F. 2000. ‘A model for the detection of insurance fraud’. The Geneva Papers on Risk and Insurance-Issues and Practice, 25(4): 517–538. DOI: 10.1111/1468-0440.00080
- 6Bentley, PJ. 2000. Evolutionary, my dear Watson investigating committee-based evolution of fuzzy rules for the detection of suspicious insurance claims. In Proceedings of the 2nd Annual Conference on Genetic and Evolutionary Computation.
Morgan Kaufmann Publishers Inc ., pp. 702–709. - 7Bollinger, CR and David, MH. 1997. ‘Modeling discrete choice with response error: Food stamp participation’. Journal of the American Statistical Association, 92(439): 827–835. DOI: 10.1080/01621459.1997.10474038
- 8Breiman, L. 2001. ‘Random forests’. Machine learning, 45(1): 5–32. DOI: 10.1023/A:1010933404324
- 9Brockett, PL, Derrig, RA, Golden, LL, Levine, A and Alpert, M. 2002. ‘Fraud classification using principal component analysis of ridits’. Journal of Risk and insurance, 69(3): 341–371. DOI: 10.1111/1539-6975.00027
- 10Brockett, PL, Xia, X and Derrig, RA. 1998. ‘Using Kohonen’s self-organizing feature map to uncover automobile bodily injury claims fraud’. Journal of Risk and Insurance, pp. 245–274. DOI: 10.2307/253535
- 11Cox, E. 1995. ‘A fuzzy system for detecting anomalous behaviors in healthcare provider claims’. Intelligent Systems for Finance and Business, pp. 111–134.
- 12Ester, M, Kriegel, H-P, Sander, J and Xu, X. 1996. A density-based algorithm for discovering clusters in large spatial databases with noise. In Kdd, vol. 96, pp. 226–231.
- 13Europe, I. 2013.
‘The impact of insurance fraud’ . Brussels: Insurance Europe. - 14Figueiredo, MAT and Jain, AK. 2002. ‘Unsupervised learning of finite mixture models’. Transactions on pattern analysis and machine intelligence, 24(3): 381–396. DOI: 10.1109/34.990138
- 15Foster, DP and Stine, RA. 2004. ‘Variable selection in data mining: Building a predictive model for bankruptcy’. Journal of the American Statistical Association, 99(466): 303–313. DOI: 10.1198/016214504000000287
- 16Freund, Y and Schapire, RE. 1996.
Experiments with a new boosting algorithm . In ICML, vol. 96. Citeseer, pp. 148–156. - 17Geurts, P, Ernst, D and Wehenkel, L. 2006. ‘Extremely randomized trees’. Machine learning, 63(1): 3–42. DOI: 10.1007/s10994-006-6226-1
- 18He, H, Bai, Y, Garcia, EA and Li, S. 2008.
ADASYN: Adaptive synthetic sampling approach for imbalanced learning . In IJCNN (World Congress on Computational Intelligence). IEEE, pp. 1322–1328. - 19Hodge, V and Austin, J. 2004. ‘A survey of outlier detection methodologies’. Artificial intelligence review, 22(2): 85–126. DOI: 10.1023/B:AIRE.0000045502.10941.a9
- 20Ke, G, Meng, Q, Finley, T, Wang, T, Chen, W, Ma, W, Ye, Q and Liu, T-Y. 2017. Lightgbm: A highly efficient gradient boosting decision tree. In Advances in Neural Information Processing Systems, pp. 3146–3154.
- 21Kim, J, Ong, A and Overill, RE. 2003.
Design of an artificial immune system as a novel anomaly detector for combating financial fraud in the retail sector . In Evolutionary Computation, vol. 1. IEEE, pp. 405–412. - 22Kokkinaki, AI. 1997. On atypical database transactions: identification of probable frauds using machine learning for user profiling. In Knowledge and Data Engineering Exchange Workshop, 1997. Proceedings.
IEEE , pp. 107–113. - 23Lei, JZ and Ghorbani, AA. 2012. ‘Improved competitive learning neural networks for network intrusion and fraud detection’. Neurocomputing, 75(1): 135–145. DOI: 10.1016/j.neucom.2011.02.021
- 24Liu, FT, Ting, KM and Zhou, ZH. 2008. Isolation forest. In International Conference on Data Mining.
IEEE , pp. 413–422. DOI: 10.1109/ICDM.2008.17 - 25Major, JA and Riedinger, DR. 1992. ‘A hybrid knowledge/statistical-based system for the detection of fraud’. International Journal of Intelligent Systems, 7(7): 687–703. DOI: 10.1002/int.4550070709
- 26Manevitz, LM and Yousef, M. 2001. ‘One-class svms for document classification’. Journal of Machine Learning research, 2: 139–154.
- 27Murad, U and Pinkas, G. 1999. Unsupervised profiling for identifying superimposed fraud. In ‘European Conference on Principles of Data Mining and Knowledge Discovery’.
Springer , pp. 251–261. DOI: 10.1007/978-3-540-48247-5_27 - 28Nian, K, Zhang, H, Tayal, A, Coleman, T and Li, Y. 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
- 29Phua, C, Alahakoon, D and Lee, V. 2004. ‘Minority report in fraud detection: classification of skewed data’. Acm sigkdd explorations newsletter, 6(1): 50–59. DOI: 10.1145/1007730.1007738
- 30Phua, C, Lee, V, Smith, K and Gayler, R. 2010. ‘A comprehensive survey of data mining-based fraud detection research’. arXiv preprint arXiv:1009.6119.
- 31Rousseeuw, PJ and Driessen, KV. 1999. ‘A fast algorithm for the minimum covariance determinant estimator’. Technometrics, 41(3): 212–223. DOI: 10.1080/00401706.1999.10485670
- 32Schölkopf. B, Platt. JC, Shawe-Taylor. J, Smola. AJ and Williamson. RC. 2001. ‘Estimating the support of a high-dimensional distribution’. Neural computation, 13(7). DOI: 10.1162/089976601750264965
- 33Sculley, D. 2010. Web-scale k-means clustering, In Proceedings of the 19th international conference on World wide web.
ACM , pp. 1177–1178. DOI: 10.1145/1772690.1772862 - 34Stefano, B and Gisella, F. 2001. Insurance fraud evaluation: a fuzzy expert system. In Fuzzy Systems, 2001. The 10th IEEE International Conference, vol. 3.
IEEE , pp. 1491–1494. - 35Trivedi, S, Pardos, ZA and Heffernan, NT. 2015. ‘The utility of clustering in prediction tasks’. arXiv preprint arXiv:1509.06163.
- 36Van Der Maaten, L, Postma, E and Van den Herik, J. 2009. ‘Dimensionality reduction: a comparative’. Journal of Machine Learning Research, 10(66–71): 13.
- 37Viaene, S, Ayuso, M, Guillen, M, Van Gheel, D and Dedene, G. 2007. ‘Strategies for detecting fraudulent claims in the automobile insurance industry’. European Journal of Operational Research, 176(1): 565–583. DOI: 10.1016/j.ejor.2005.08.005
- 38Weiss, GM. 2004. ‘Mining with rarity: a unifying framework’. ACM Sigkdd Explorations Newsletter, 6(1): 7–19. DOI: 10.1145/1007730.1007734
- 39Williams, GJ. 1999. Evolutionary hot spots data mining. In Pacific-Asia Conference on Knowledge Discovery and Data Mining.
Springer , pp. 184–193. DOI: 10.1007/3-540-48912-6_26 - 40Williams, GJ and Huang, Z. 1997. Mining the knowledge mine. In Australian Joint Conference on Artificial Intelligence.
Springer , pp. 340–348. DOI: 10.1007/3-540-63797-4_87 - 41Wilson, JH. 2009. ‘An analytical approach to detecting insurance fraud using logistic regression’. Journal of Finance and Accountancy, 1: 1.
- 42Zhou, D, Bousquet, O, Navin Lal, T, Weston, J and Scholkopf, B. 2004. ‘Learning with local and global consistency’. In Advances in neural information processing systems, pp. 321–328.
- 43Zhu, X and Ghahramani, Z. 2002.
Learning from labeled and unlabeled data with label propagation . Technical report, CMU-CALD-02-107, Carnegie Mellon University.