Based Big Data Analysis of Fraud Detection for Online Transaction Orders
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Fraud control is important for the online marketplace. This study addresses the problem of detecting attempts to deceive orders in Internet transactions. Our goal is to generate an algorithm model to detect and prevent the fraudulent orders. First, after analyzing the real historical data of customers’ orders from Dangdang Website (http://www.dangdang.com. E-commerce China Dangdang Inc (Dangdang) is a leading e-commerce company in China. Dangdang officially listed on the New York Stock Exchange on December 8th, 2010, and is the first Chinese B2C e-commerce company which is completely based on online business to list on New York Stock.), we described characteristics related to transactions that may indicate frauds orders. We presented fraudulent orders characteristic matrix through comparing the normal and abnormal orders. Secondly, we apply Logic Regression model to identify frauds based on the characteristic matrix. We used real data from Dang company to train and evaluate our methods. Finally we evaluated the validity of solutions though analyzing feedback data.
KeywordsInternet translation Fraud order Big data Fraud detection Fraud prevention Logistic regression
The data of this work is provided by E-commerce China Dangdang Inc (http://www.dangdang.com). We thank Qiang Fu and QI Ju who are employees of Dangdang and for discussing the results with us. We thank Michael Wagner for revising the paper and editing the manuscript. We also thank, Jian Li, Jie Shen, Weiwei Yang and Daobo Wang, Who are the associate editor for providing a lot of helpful comments.
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