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

§ Beihang University; Southern Connecticut State Unive...

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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.

References

17 Cites in Article
  1. 1. M Jans,N Lybaert,K Vanhoof (2009). A framework for internal fraud risk reduction at IT integrating business processes: The IFR framework. The International Journal of Digital Accounting Research, 9, 1-29.
  2. 2. T Kim,Y Lim,J Nah (2013). Analysis on fraud detection for internet service. International Journal of Security and Its Applications, 7(6), 275-284.
  3. 3. Seonyoung Shim,Byungtae Lee (2010). An economic model of optimal fraud control and the aftermarket for security services in online marketplaces. Electronic Commerce Research and Applications, 9(5), 435-445.
  4. 4. Chris Hogan,Zabihollah Rezaee,Richard Riley,Uma Velury (2008). Financial Statement Fraud: Insights from the Academic Literature. AUDITING: A Journal of Practice & Theory, 27(2), 231-252.
  5. 5. G Trompeter,T Carpenter,N Desai,K Jones,R Riley (2013). A synthesis of fraudrelated research. Auditing: A Journal of Practice & Theory, 32(1), 287-321.
  6. 6. Benjamin Klein,Keith Leffler (1981). The Role of Market Forces in Assuring Contractual Performance. Journal of Political Economy, 89(4), 615-641.
  7. 7. Y Lou,M Wang (2009). Fraud risk factor of the fraud triangle assessing the likelihood of fraudulent financial reporting. Journal of Business & Economics Research, 7(2), 61-78.
  8. 8. Adler Michael (1971). the sole concern of fraud risk factor studies was the consideration of fraudulent behavior or how todetect or to deter fraud. the sole concern of fraud risk factor studies was the consideration of fraudulent behavior or how todetect or to deter fraud
  9. 9. J Lach (1999). Weir Group digs up multiple mining contracts. World Pumps, 1997(370), 45-46.
  10. 10. M Eining,D Jones,J Loebbecke (1997). Reliance on decision aids: an examination of auditors' assessment of management fraud. Auditing: A Journal of Practice and Theory, 16(2), 1-19.
  11. 11. B Green,J Choi (1997). Assessing the risk of management fraud through neural network technology. Auditing: A Journal of Practice and Theory, 16(1), 14-28.
  12. 12. J Ohlson (1980). Financial ratios and the probabilistic prediction of bankruptcy. Journal of Accounting Research, 18, 109-131.
  13. 13. M Lenard,P Alam (2009). An historical perspective on fraud detection: From bankruptcy models to most effective indicators of fraud in recent incidents. Journal of Forensic & Investigative Accounting, 1(1), 1-5.
  14. 14. H Zhang,Z Lin,X Hu The effectiveness of the escrow model: an experimental framework for dynamic online environments. Journal of Organizational Computing and Electronic Commerce, 17(2), 119-143.
  15. 15. D Hosmer,S Lemeshow,R Sturdivant (2013). Applied Logistic Regression. Applied Logistic Regression
  16. 16. Rafael Maranzato,Adriano Pereira,Alair Do Lago,Marden Neubert (2010). Fraud detection in reputation systems in e-markets using logistic regression. Proceedings of the 2010 ACM Symposium on Applied Computing, 1454-1455.
  17. 17. M Lek,B Anandarajah,N Cerpa,R Jamieson (2001). Data mining prototype for detecting ecommerce fraud. The 9th European Conference on Information Systems

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. "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).

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Journal Specifications

Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

Keywords
Classification
GJCST-C Classification K.4.4
H.2.1
Version of record
v1.2

Issue date
November 6, 2016

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

Yan Liu
Yan Liu Beihang University; Southern Connecticut State University