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

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

Yan Quan Liu

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Qinghong Yang

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Wei Xing

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Xiangquan Hu

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Southern Connecticut State University

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

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Abstract

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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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.

How to Cite 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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GJCST Volume 16 Issue C4
Pg. 7- 16

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Journal Specifications

Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

Keywords

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Classification

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

Submission ReceivedDecember 12, 2015
Peer Review Double Blind
Handling Editor
Accepted January 2, 2016
Published January 15, 2016

Version of record

v1.2

Issue date

November 6, 2016

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Article Matrices

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): 283

Total Views (Real + Logic): 7262

Total Downloads (simulated): 1864

Publish Date: 2016 11, Sun

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Total Views: 7262

Total Downloads: 1864

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