@Article{cmc.2019.05214,
AUTHOR = {Hangjun  Zhou, Guang  Sun, Sha  Fu, Wangdong  Jiang, Juan  Xue},
TITLE = {A Scalable Approach for Fraud Detection in Online E-Commerce Transactions with Big Data Analytics},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {60},
YEAR = {2019},
NUMBER = {1},
PAGES = {179--192},
URL = {http://www.techscience.com/cmc/v60n1/28371},
ISSN = {1546-2226},
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.},
DOI = {10.32604/cmc.2019.05214}
}