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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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
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