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Predicting Fraud in Electronic Commerce: Fraud Detection Techniques in E-Commerce

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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2017
Authors:
Amitha Raghava-Raju
10.5120/ijca2017914977

Amitha Raghava-Raju. Predicting Fraud in Electronic Commerce: Fraud Detection Techniques in E-Commerce. International Journal of Computer Applications 171(2):18-22, August 2017. BibTeX

@article{10.5120/ijca2017914977,
	author = {Amitha Raghava-Raju},
	title = {Predicting Fraud in Electronic Commerce: Fraud Detection Techniques in E-Commerce},
	journal = {International Journal of Computer Applications},
	issue_date = {August 2017},
	volume = {171},
	number = {2},
	month = {Aug},
	year = {2017},
	issn = {0975-8887},
	pages = {18-22},
	numpages = {5},
	url = {http://www.ijcaonline.org/archives/volume171/number2/28153-2017914977},
	doi = {10.5120/ijca2017914977},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

Electronic commerce is a commercial transaction that involves the process of buying and selling goods, services and transfer of information between businesses and customers over an electronic medium. Electronic commerce has expanded rapidly and continues to grow at an unforeseen rate. With the advent of e-commerce, the potential fraudulent activities are prevalent, and therefore hundreds of millions of dollars are lost to fraud every year. The goal of this paper is to implement and evaluate several anomaly detection methods for making predictions using, or finding patterns in, heterogeneous e-commerce data to detect fraudulent activities of users. Various Machine Learning algorithms – K-nearest neighbors, Random-Forest and Isolated forest algorithms are employed to train the model in order to detect fraud and anomalous techniques in e-commerce.

References

  1. “U.S. Online Retail Forecast, 2009 to 2014,” Forrester Research, Inc., March 2010.
  2. Online Fraud Report, 11th Annual Edition, Cybersource, February 2010.
  3. S´ergio Moro, Paulo Cortez, Paulo Rita , “A Data-Driven Approach to Predict the Success of Bank Telemarketing”, Dec 16, 2016.
  4. Kevin Zakka, “A Complete Guide to K-Nearest-Neighbors with Python and R”, Kevin Zakka’s Blog, July 13, 2016.
  5. Zhiguo Ding , Minrui Fei, “An Anomaly Detection Approach Based on Isolation Forest Algorithm for Streaming Data using Sliding Window”, March 13, 2015.
  6. Fei Tony Liu, Kai Ming Ting, , Zhi-Hua Zhou, “Isolation Forest”, 2010.
  7. HackerEarth, “Practical tutorial on Random Forest and Parameter tuning in R”, 2017.
  8. Sonya Sawtelle, “Anomaly Detection in Scikit-Learn”, 2017.
  9. Magic Quadrant for Web Fraud Detection, Gartner, Inc., January 2010.
  10. Priya J Rana, Jwalant Baria, “A Survey on Fraud Detection Techniques in Ecommerce”, International Journal of Computer Applications, Volume 113 – No. 14, March 2015.

Keywords

Electronic Commerce, Fraudulent activities, Anomaly detection, K-nearest neighbors, Random-Forest, Isolated Forest.