Open Access

ARTICLE

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

by Hangjun Zhou¹, Guang Sun¹, Sha Fu¹, Wangdong Jiang¹, Juan Xue¹

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

* Corresponding Author: Hangjun Zhou. Email: .

Computers, Materials & Continua 2019, 60(1), 179-192. https://doi.org/10.32604/cmc.2019.05214

Abstract

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.

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Citations