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All issues Volume 189 (2018) MATEC Web Conf., 189 (2018) 03002 References
Open Access
Issue
MATEC Web Conf.
Volume 189, 2018
2018 2nd International Conference on Material Engineering and Advanced Manufacturing Technology (MEAMT 2018)
Article Number 03002
Number of page(s) 15
Section Cloud & Network
DOI https://doi.org/10.1051/matecconf/201818903002
Published online 10 August 2018
  1. Oxford Concise English Dictionary, Tenth ed, Publisher, 1999. [Google Scholar]
  2. C. Phua, V. Lee, K. Smith and R. Gayler, "A comprehensive survey of data miningbased fraud detection research," Artificial Intelligence Review, p. 1–14, 2005. [Google Scholar]
  3. Wang, J; Liao, Y; Tsai, T; Hung, G, "Technology-based financial frauds in Taiwan: issue and approaches," IEEE Conference on: Systems, Man and Cyberspace, pp. 1120- 1124, 10 2006. [Google Scholar]
  4. Subudhi, S; Panigrahi, S, "Use of optimized Fuzzy C-Means clustering and supervised classifiers for automobile insurance fraud detection," Journal of King Saud University – Computer and Information Sciences, 2017. [Google Scholar]
  5. Z. Rezaee, Financial Statement Fraud – Prevention and Detection, John Wiley & Sons, Inc., 2002. [Google Scholar]
  6. K. Chaudhary, J. Yadav and B. Mallick, "A review of fraud detection techniques: credit card," International Journal of Computer Applications, 5 2012. [Google Scholar]
  7. Bolton, R J; Hand, D J, "Statistical fraud detection: a review," Statistical Science, vol. 17, no. 3, p. 235–255, 2002. [CrossRef] [Google Scholar]
  8. D. Zhang and L. Zhou, "Discovering golden nuggets: data mining in financial application,"IEEE Transactions on Systems, Man and Cybernetics, vol. 34, no. 4, 11 2004. [Google Scholar]
  9. J. Li, K. Huang, J. Jin and J. Shi, "A survey on statistical methods for health care fraud detection," Health Care Manag. Sci, p. 275–287, 2008. [CrossRef] [Google Scholar]
  10. P. Travaille, D. Thornton, R. M. Mueller and J. V. Hillegersberg, "Electronic Fraud Detection in the U.S. Medicaid Healthcare Program: Lessons Learned from other Industries," in Americas Conference on Information Systems, Detroit, 2011. [Google Scholar]
  11. Q. Liu and V. Miklos, "Healthcare fraud detection: a survey and a clustering model incorporating Geo-location information," in World Continuous Auditing and Reporting Symposium, Brisbane, 2013. [Google Scholar]
  12. P. Richhariya and P. K. Singh, "A Survey on Financial Fraud Detection Methodologies," International Journal of Computer Applications, vol. 45, no. 22, pp. 21-24, 2012. [Google Scholar]
  13. Ngai, E W T; Hu, Y; Chen, Y; Sun, X, "The application of data mining techniques in financial fraud detection: a classification framework and an academic review of literature," Decision Support Systems, vol. 50, no. 3, p. 559–569, 2011. [CrossRef] [Google Scholar]
  14. S. Wang, "A comprehensive survey of data mining-based accounting-fraud detection research," in International Conference on Intelligent Computation Technology and Automation, 2010. [Google Scholar]
  15. L. Sithic and T. Balasubramanian, "Survey of insurance fraud detection using data mining techniques," International Journal of Emerging Computing Engineering, vol. 2, no. 3, pp. 62-65, 2013. [Google Scholar]
  16. S. Viaene, R. Derrig and G. Dedene, "A Case Study of Applying Boosting Naive Bayes to Claim Fraud Diagnosis," IEEE Transactions on Knowledge and Data Engineering, vol. 16, no. 5, pp. 612-620, 2004. [CrossRef] [Google Scholar]
  17. S. Panigrahi, A. Kundu, S. Sural and A. K. Majumdar, "Credit card fraud detection: a fusion approach using Dempster–Shafer theory and Bayesian learning," Information Fusion, no. 10, pp. 354-363, 2009. [CrossRef] [Google Scholar]
  18. B. Hooi, N. Shah, A. Beutel, S. Gunneman, L. Akoglu, M. Kumar, D. Makhija and C. Faloutsos, "BIRDNEST: Bayesian Inference for Ratings-Fraud Detection," in Siam International Conference on Data Mining, 2016. [Google Scholar]
  19. Y. Sahin, S. Bulkan and E. Duman, "A cost-sensitive decision tree approach for fraud detection," Expert Systems with Applications, no. 40, pp. 5916-5923, 2013. [CrossRef] [Google Scholar]
  20. M. Anis, M. Ali and A. Yadav, "A comparative study of decision tree algorithms for class imbalanced learning in credit card fraud detection," International Journal of Economics, Commerce and Management, vol. 3, no. 12, pp. 86-102, 2015. [Google Scholar]
  21. R. Jain, B. Gour and S. Dubey, "A Hybrid Approach for Credit Card Fraud Detection using Rough Set and Decision Tree Technique," International Journal of Computer Applications, vol. 139, no. 10, pp. 1-6, 2016. [CrossRef] [Google Scholar]
  22. P. Save, P. Tiwarekar, N. Ketan and N. Mahyavanshi, "A Novel Idea for Credit Card Fraud Detection using Decision Tree," International Journal of Computer Applications, vol. 161, no. 13, pp. 6-9, 2017. [CrossRef] [Google Scholar]
  23. G. Rushin, C. Stancil, M. Sun, S. Adams and P. Beling, "Horse race analysis in credit card fraud—deep learning, logistic regression, and Gradient Boosted Tree," in Systems and Information Engineering Design Symposium, Charlottesville, 2017. [Google Scholar]
  24. X. Yu, "Machine Learning Application in Online Leading Credit Risk Prediction," 16 7 2017. [Online]. Available: https://arxiv.org/abs/1707.04831. [Accessed 23 2 2018]. [Google Scholar]
  25. Pinquet, J; Ayuso, M; Guillen, M;, "Selection bias and auditing policies for insurance claims," Journal of Risk and Insurance, no. 74, pp. 425-440, 2007. [CrossRef] [Google Scholar]
  26. S. Viaene, M. Ayuso, M. Guillen, D. Van Gheel and G. Dedene, "Strategies for detecting fraudulent claims in the automobile insurance industry," European Journal of Operational Research, no. 176, pp. 565-583, 2007. [CrossRef] [Google Scholar]
  27. S. Bhattacharyya, S. Jha, K. Tharakunnel and J. C. Westland, "Data mining for credit card fraud: a comparative study," Decision Support Systems, no. 50, pp. 602-6613, 2011. [CrossRef] [Google Scholar]
  28. A. Kibekbaev and E. Duman, "Profit-based Logistic Regression: A case study in Credit Card Fraud Detection," The Fourth International Conference on Data Analytics, pp. 101-105, 2015. [Google Scholar]
  29. P. Kulkarni and R. Ade, "Logistic Regression Learning Model for Handling Concept Drift with Unbalanced Data in Credit Card Fraud Detection System," in International Conference on Computer and Communication Technologies, 2016. [Google Scholar]
  30. S. Patel and S. Gond, "Supervised Machine (SVM) Learning for Credit Card Fraud Detection," International Journal of Engineering Trends & Technology, vol. 8, no. 3, pp. 137-139, 2014. [CrossRef] [Google Scholar]
  31. C. Whitrow, D. J. Hand, P. Juszczak, D. Weston and N. M. Adams, "Transaction aggregation as a strategy for credit card fraud detection," Data Mining & Knowledge Discovery, vol. 18, no. 1, pp. 30-55, 2009. [CrossRef] [Google Scholar]
  32. S. Maldonado, R. Weber and F. Famili, "Feature selection for high-dimensional classimbalanced data sets using Support Vector Machines," Information Sciences, no. 286, 2014. [Google Scholar]
  33. S. O. Moepya, S. S. Akhoury and F. V. Nelwamondo, "Applying Cost-Sensitive Classification for Financial Fraud Detection under High Class-Imbalance," in 2015, IEEE International Conference on Data Mining Workshop. [Google Scholar]
  34. V. Mareeswari and G. Gunasekaran, "Prevention of credit card fraud detection based on HSVM," in IEEE International Conference on Information Communication and Embedded Systems, 2016. [Google Scholar]
  35. M. Kolalikhormuji, M. Bazrafkan, M. Sharifian, S. Javad Mirabedini and A. Harounabadi, "Credit Card Fraud Detection with a Cascade Artificial Neural Network and Imperialist Competitive Algorithm," International Journal of Computer Applications, vol. 96, no. 25, pp. 1-9, 2014. [CrossRef] [Google Scholar]
  36. K. Fu, D. Cheng, Y. Tu and L. Zhang, "Credit Card Fraud Detection Using Convolutional Neural Networks," in International Conference on Neural Information, 2016. [Google Scholar]
  37. A. Gulati, D. Prakash, MdFuzailC, J. Norman and M. R, "Credit card fraud detection using neural network and geolocation," IOP Conferece Series: Materials Science and Engineering, no. 263, 2017. [Google Scholar]
  38. K. Modi and R. Dayma, "Fraud Detection Technique in Credit Card Transactions using Convolutional Neural Network," International Journal of Advance Research in Engineering, Science & Technology, vol. 8, no. 4, pp. 1-7, 2017. [Google Scholar]
  39. M. Celebi, H. Kingravi and P. Vela, "A comparative study of efficient initialization methods for the K-Means clustering algorithm," Expert Systems with Applications, vol. 40, no. 1, pp. 200-210, 2013. [CrossRef] [Google Scholar]
  40. X. Huang and W. Su, "An improved K-means clustering algorithm," Journal of networks, vol. 9, no. 1, 2014. [Google Scholar]
  41. A. A. Ramaki, "Credit Card Fraud Detection Based on Ontology Graph," International Journal of Security, Privacy and Trust Management, vol. 5, no. 1, pp. 1-12, 2012. [CrossRef] [Google Scholar]
  42. B. Lebichot, F. Braun, O. Caelen and e. al., "A graph-based, semi-supervised, credit card fraud detection system," in Complex Networks & Their Applications V. Springer International Publishing, 2016. [Google Scholar]
  43. B. Cao, M. Mao, S. Viidu and Y. S. Philip, "Collective Fraud Detection Capturing Inter-Transaction Dependency," in Proceedings of Machine Learning Research, 2017. [Google Scholar]
  44. Y. Wang and W. Xu, "Leveraging deep learning with LDA-based text analytics to detect automobile insurance fraud," Decision Support Systems, no. 105, pp. 87-95, 2018. [CrossRef] [Google Scholar]
  45. D. M. Blei, A. Y. Ng and M. I. Jordan, "Latent dirichlet allocation," Journal of Machine Learning Research, no. 3, pp. 993-1922, 2003. [Google Scholar]
  46. G. G. Sundarkumar and V. Ravi, "A novel hybrid undersampling method for mining unbalanced datasets in banking and insurance," Engineering Applications of Artificial Intelligence, no. 37, pp. 368-377, 2014. [CrossRef] [Google Scholar]
  47. M. S. Beasley, J. V. Carcello, D. R. Hermanson and T. I. Neal, "Fraudulent Financial Reporting," in Committee of Sponsoring Organizations of the Treadway Commission, Jersey City, 2010. [Google Scholar]
  48. M. Beneish, "The detection of earnings manipulation," Financial Analysis Journal, no. 5, pp. 24-36, 1999. [Google Scholar]
  49. E. Kirkos, C. Spathis and Y. Manolopoulos, "Data mining techniques for the detection of fraudulent financial statements," Expert System Applications, no. 32, pp. 995-1003, 2007. [CrossRef] [Google Scholar]
  50. C. C. Lin, S. Y. Chiu, S. Y. Huang and D. C. Yen, "Detecting the financial statement fraud: the analysis of the differences between data mining techniques and experts' judgements," Knowledge Based Systems, no. 89, pp. 459-470, 2015. [CrossRef] [Google Scholar]
  51. S. Kotsiantis, E. Koumanakos and D. Tzelepis, "Forecasting fraudulent financial statements using data mining," International Journal of Computional Intelligence, no. 3, pp. 104-110, 2006. [Google Scholar]
  52. P. F. Pai, M. F. Hsu and M. C. Wang, "A support vector machine-based model for detecting top management fraud," Knowledge Based Systems, no. 24, pp. 314-321, 2011. [CrossRef] [Google Scholar]
  53. S. Huang, "Fraud detection model by using support vector machine techniques," International Journal of Digital Content Technology and Its Applications, no. 7, pp. 32-42, 2013. [Google Scholar]
  54. P. Hajek and R. Henriques, "Mining corporate annual reprots for intelligent detection of financial statement fraud - A comparative study of machine learning methods," Knowledge Based Systems, no. 128, pp. 139-152, 2017. [CrossRef] [Google Scholar]
  55. M. Fahmi, A. Hamdy and K. Nagati, "Data Mining Techniques for Credit Card Fraud Detection: Empirical Study," Sustainable Vital Technologies in Engineering & Informatics, pp. 1-9, 2016. [Google Scholar]
  56. J. West and M. Bhattacharya, "Some Experimental Issues in Financial Fraud Mining," Procedia Computer Science, no. 80, pp. 1734-1744, 2016. [CrossRef] [Google Scholar]
  57. R. Emekter, Y. Tu, B. Jirasakuldech and M. Lu, "Evaluating credit risk and loan performance in online Peer-to-Peer (p2P) lending," Applied Economics, 2015. [Google Scholar]
  58. A. Byanjankar, M. Heikkila and J. Mezei, "Predicting Credit Risk in Peer-to-Peer Lending: A Neural Network Approach," in IEEE Symposium Series on Computational Intelligence, 2015. [Google Scholar]
  59. Y. Jin and Y. Zhu, "A Data-Driven Approach to Predict Default Risk of Loan for Online Peer-to-Peer (p2P) Lending," in International Conference on Communication Systems and Network Technologies, Gwalior, 2015. [Google Scholar]
  60. J. J. Xu, Y. Lu and M. Chau, "p2P Lending Fraud Detection: A Big Data Approach," Intelligence and Security Informatics, pp. 77-81, 2015. [Google Scholar]
  61. X. Lin, X. Li and Z. Zheng, "Evaluating borrower’s default risk in peer-to-peer lending: evidence from a lending platform in China," Applied Economics, vol. 49, no. 35, 2017. [Google Scholar]
  62. Y. Zhang, D. Wang, Y. Chen, H. Shang and Q. Tian, "Credit Risk Assessment Based on Long Short-Term Memory Model". [Google Scholar]
  63. Z. Huang, J. Deng, M. Xiong, Y. Ren and Y. Qiao, "A comparison of US and UK p2P lending regulation systems and study on patterns of p2P lending regulation in China," Studies on Financial Regulation, no. 10, pp. 45-58, 2014. [Google Scholar]
  64. Y. Xia, C. Liu, Y. Li and N. Liu, "A boosted decision tree approach using Bayesian hyperparameter optimization for credit scoring," Expert Systems With Applications, no. 78, pp. 225-241, 2017. [CrossRef] [Google Scholar]
  65. D. S. Nascimento, A. L. Coelho and A. M. Canuto, "Integrating complementary techniques for promoting diversity in classifier ensembles: A systematic study," Neurocomputing, no. 138, pp. 347-357, 2014. [CrossRef] [Google Scholar]
  66. L. Nanni and A. Lumini, "An experimental comparison of ensemble of classifiers for bankruptcy prediction and credit scoring," Expert Systems with Applications, no. 36, pp. 3028-3033, 2009. [CrossRef] [Google Scholar]
  67. S. Lessmann, B. Baesens, H. V. Seow and I. C. Thomas, "Benchmarking state-of-theart classification algorithms for credit scoring: An update of research," European Journal of Operational Research, no. 247, pp. 124-136, 2015. [CrossRef] [Google Scholar]
  68. "Lending Club," [Online]. Available: https://www.lendingclub.com/. [Accessed 23 2 2018]. [Google Scholar]
  69. "WE," [Online]. Available: https://www.we.com/. [Accessed 23 2 2018]. [Google Scholar]

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