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This study aims to determine the best practices and provide a model of the technical solutions that can effectively and systematically limit fraudulent transactions of online orders in ecommerce services, using the methods of analytical mining and case studies. Based on a process of fraud prevention and detection performed in the e-business Dangdang, Inc., a leading online retailer in China, twelve identifying features of fraudulent order data were extracted and compiled into a feature matrix. Logistic regression with this matrix was then used to build a model to judge if an order was fraudulent. The model was tested using various order data with machine learning techniques to meet the requirements of being effective, correct, adaptive, and persistent. Then an online detection and prevention schema was established and the hypothesis of so-called Behavior Pattern Change Assumption (BPCA) was proven.
Yan Quan Liu. 2016. "Anti-Fraud Schema System for Identification and Prevention of Fraud Behaviors in E-Commerce Services". Global Journal of Computer Science and Technology - C: Software & Data Engineering GJCST-C Volume 16 (GJCST Volume 16 Issue C4).
Crossref Journal DOI 10.17406/gjcst
Print ISSN 0975-4350
e-ISSN 0975-4172
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Total Score: 134
Country: China
Subject: Global Journal of Computer Science and Technology - C: Software & Data Engineering
Authors: Qinghong Yang, Wei Xing, Xiangquan Hu, Yan Quan Liu (PhD/Dr. count: 0)
View Count (all-time): 518
Total Views (Real + Logic): 3301
Total Downloads (simulated): 152
Publish Date: 2016 01, Fri
Monthly Totals (Real + Logic):
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