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A Scalable Approach for Fraud Detection in Online E-Commerce Transactions with Big Data Analytics

Hangjun Zhou1,2,*, Guang Sun1,3, Sha Fu1, Wangdong Jiang1, Juan Xue1

Hunan University of Finance and Economy, Changsha, 410205, China.
Nanjing University of Science and Technology, Nanjing, 210094, China.
College of Engineering, The University of Alabama, Box 870200, Tuscaloosa, Alabama, USA.

* Corresponding Author: Hangjun Zhou. Email: email.

Computers, Materials & Continua 2019, 60(1), 179-192.


With the rapid development of mobile Internet and finance technology, online e-commerce transactions have been increasing and expanding very fast, which globally brings a lot of convenience and availability to our life, but meanwhile, chances of committing frauds also come in all shapes and sizes. Moreover, fraud detection in online e-commerce transactions is not totally the same to that in the existing areas due to the massive amounts of data generated in e-commerce, which makes the fraudulent transactions more covertly scattered with genuine transactions than before. In this article, a novel scalable and comprehensive approach for fraud detection in online e-commerce transactions is proposed with majorly four logical modules, which uses big data analytics and machine learning algorithms to parallelize the processing of the data from a Chinese e-commerce company. Groups of experimental results show that the approach is more accurate and efficient to detect frauds in online e-commerce transactions and scalable for big data processing to obtain real-time property.


Cite This Article

H. Zhou, G. Sun, S. Fu, W. Jiang and J. Xue, "A scalable approach for fraud detection in online e-commerce transactions with big data analytics," Computers, Materials & Continua, vol. 60, no.1, pp. 179–192, 2019.


cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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