Abstract
In order to predict the potential fraud users of B2B platform, this paper constructed an anti-fraud system by employing the Decision tree and Association analysis. Firstly, based on the research of the platform users’ operation behavior a predictive model was built by using the Decision tree and in the model each user was given a fraud warning score. Secondly, to improve the accuracy of the predictive model, the FP-growth algorithm was adopted. The similarity identification of the correlation analysis was applied to further amend the warning score of the model. The members were divided into high-risk fraud group and low-risk one according to a certain threshold limit. In the end, whether the users had high-risk fraud rating was identified through two iteration of the Association analysis. It turned out that the effect of the model application can meet demand well in B2B platform and the anti-fraud system we built is more targeted and convinced.
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References
Jang, A., Ismail, R.: The beta reputation system. In: Proceedings of the 15th Bled Electronic Commerce Conference, pp. 41–55 (2002); Transaction aggregation as a strategy for credit card fraud detection
Liu, S., Zhang, J., Miao, C., Theng, Y., Kot, A.: iclub: an integrated clusteringbased approach to improve the robustness of reputation systems. In: The 10th International Conference on Autonomous Agents and Multiagent Systems, vol. 3, pp. 1151–1152 (2011)
Teacy, W., Patel, J., Jennings, N., Luck, M.: Travos: Trust and reputation in the context of inaccurate information sources. Autonomous Agents and Multi-Agent Systems 12(2), 183–198 (2006)
Zhang, J., Cohen, R.: Evaluating the trustworthiness of advice about seller agents in e-marketplaces: A personalized approach. Electronic Commerce Research and Applications 7(3), 330–340 (2008)
Zhang, L., Jiang, S., Zhang, J., Ng, W.K.: Robustness of Trust Models and Combinations for Handling Unfair Ratings. In: Dimitrakos, T., Moona, R., Patel, D., McKnight, D.H. (eds.) Trust Management VI. IFIP AICT, vol. 374, pp. 36–51. Springer, Heidelberg (2012)
Kwan, M., Overill, R., Chow, K.-P., Silomon, J., Tse, H., Law, F., La, P.: Evaluation of Evidence in Internet Auction Fraud Investigations. In: Chow, K.-P., Shenoi, S. (eds.) Advances in Digital Forensics VI. IFIPAICT, vol. 337, pp. 121–132. Springer, Heidelberg (2010)
Ye, X.: Data mining in fraud risk analysis of application,Commercial science and technology (2006)
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© 2012 Springer-Verlag Berlin Heidelberg
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Jiang, Q., Hu, C., Xu, L. (2012). Fraud Detection in B2B Platforms Using Data Mining Techniques. In: Zhou, S., Zhang, S., Karypis, G. (eds) Advanced Data Mining and Applications. ADMA 2012. Lecture Notes in Computer Science(), vol 7713. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35527-1_51
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DOI: https://doi.org/10.1007/978-3-642-35527-1_51
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-35526-4
Online ISBN: 978-3-642-35527-1
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