Sale Fraud Behavior Detection over Multidimensional Sparse Data Warehouse

被引：0
|
作者：
Zheng J.-L. [1 ,2 ]
Qiao S.-J. [1 ,2 ]
Shu H.-P. [1 ,2 ]
Ying G.-H. [3 ]
Gutierrez L.A. [4 ]
机构：
[1] Sichuan Key Laboratory of Software Automatic Generation and Intelligent Service, Chengdu University of Information Technology, Chengdu
[2] School of Software Engineering, Chengdu University of Information Technology, Chengdu
[3] Alibaba (China) Technology Co. Ltd., Hangzhou
[4] Department of Computer Science, Rensselaer Polytechnic Institute, New York
来源：
Ruan Jian Xue Bao/Journal of Software | 2020年 / 31卷 / 03期
基金：
中国国家自然科学基金;
关键词：
Artificial intelligence; Deal cheating pattern; Distribution channel fraud; Partially ordered lattice; Tensor;
D O I：
10.13328/j.cnki.jos.005905
中图分类号：
学科分类号：
摘要：
In distribution channel system, product manufacturer will often reward retail trader who makes big deal to increase the sales. On the other hand, in order to obtain high reward, retail traders may form alliance, where a cheating retail trader accumulates the deals of other retail traders. This type of commercial fraud is called deal cheating or cross region sale. Because the sales contain a lot of normal big deals, traditional outlier detection methods cannot distinguish the normal extreme value and the true outlier generated by deal cheating behavior. Meanwhile, the sparsity of the multidimensional sales data makes the outlier detection methods based on multidimensional space cannot work effectively. To handle the aforementioned problems, this study proposes deal cheating mining algorithms based on ratio characteristic and tensor reconstruction method. These algorithms combine artificial intelligence and database technique. Meanwhile, because there are multiple types of deal cheating patterns, this study proposes deal cheating pattern classification methods based on the partially ordered lattice of deal cheating patterns. In the experiments on synthetic data, the deal cheating detection algorithm based on the ratio characteristic can achieve an average AUC-value of 65%. The traditional feature extraction methods can only achieve average AUC-values of 36% and 30%. In the experiments on the real data, the results shows the deal cheating detection algorithm is capable of distinguishing normal big deal from abnormal big deal which may be generated by the deal cheating behaviors. © Copyright 2020, Institute of Software, the Chinese Academy of Sciences. All rights reserved.
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页码：710 / 725
页数：15
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