@Article{cmc.2020.09834,
AUTHOR = {Hangjun Zhou, Guang Sun, Sha Fu, Xiaoping Fan, Wangdong Jiang, Shuting Hu, Lingjiao Li},
TITLE = {A Distributed Approach of Big Data Mining for Financial Fraud Detection in a Supply Chain},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {64},
YEAR = {2020},
NUMBER = {2},
PAGES = {1091--1105},
URL = {http://www.techscience.com/cmc/v64n2/39348},
ISSN = {1546-2226},
ABSTRACT = {Supply Chain Finance (SCF) is important for improving the effectiveness of 
supply chain capital operations and reducing the overall management cost of a supply 
chain. In recent years, with the deep integration of supply chain and Internet, Big Data, 
Artificial Intelligence, Internet of Things, Blockchain, etc., the efficiency of supply chain 
financial services can be greatly promoted through building more customized risk pricing 
models and conducting more rigorous investment decision-making processes. However, 
with the rapid development of new technologies, the SCF data has been massively 
increased and new financial fraud behaviors or patterns are becoming more covertly 
scattered among normal ones. The lack of enough capability to handle the big data 
volumes and mitigate the financial frauds may lead to huge losses in supply chains. In 
this article, a distributed approach of big data mining is proposed for financial fraud 
detection in a supply chain, which implements the distributed deep learning model of 
Convolutional Neural Network (CNN) on big data infrastructure of Apache Spark and 
Hadoop to speed up the processing of the large dataset in parallel and reduce the 
processing time significantly. By training and testing on the continually updated SCF 
dataset, the approach can intelligently and automatically classify the massive data 
samples and discover the fraudulent financing behaviors, so as to enhance the financial 
fraud detection with high precision and recall rates, and reduce the losses of frauds in a 
supply chain.},
DOI = {10.32604/cmc.2020.09834}
}