Anti-Fraud Schema System for Identification and Prevention of Fraud Behaviors in E-Commerce Services

1
Yan Quan Liu
Yan Quan Liu
2
Qinghong Yang
Qinghong Yang
3
Wei Xing
Wei Xing
4
Xiangquan Hu
Xiangquan Hu
1 Beihang University; Southern Connecticut State University

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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.

Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

Data Availability

Not applicable for this article.

Yan Quan Liu. 2016. \u201cAnti-Fraud Schema System for Identification and Prevention of Fraud Behaviors in E-Commerce Services\u201d. Global Journal of Computer Science and Technology - C: Software & Data Engineering GJCST-C Volume 16 (GJCST Volume 16 Issue C4): .

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Journal Specifications

Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

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GJCST-C Classification: K.4.4, H.2.1
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v1.2

Issue date

November 6, 2016

Language

English

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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.

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Anti-Fraud Schema System for Identification and Prevention of Fraud Behaviors in E-Commerce Services

Qinghong Yang
Qinghong Yang
Wei Xing
Wei Xing
Xiangquan Hu
Xiangquan Hu
Yan Quan Liu
Yan Quan Liu Beihang University; Southern Connecticut State University

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