Open Access
ARTICLE
A Distributed Approach of Big Data Mining for Financial Fraud Detection in a Supply Chain
Hangjun Zhou1, *, Guang Sun1, 2, Sha Fu1, Xiaoping Fan1, Wangdong Jiang1, Shuting Hu1, Lingjiao Li1
1 Hunan University of Finance and Economics, Changsha, 410205, China.
2 College of Engineering, The University of Alabama, Tuscaloosa, USA.
* Corresponding Author: Hangjun Zhou. Email: .
Computers, Materials & Continua 2020, 64(2), 1091-1105. https://doi.org/10.32604/cmc.2020.09834
Received 21 January 2020; Accepted 10 April 2020; Issue published 10 June 2020
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.
Keywords
Cite This Article
APA Style
Zhou, H., Sun, G., Fu, S., Fan, X., Jiang, W. et al. (2020). A distributed approach of big data mining for financial fraud detection in a supply chain. Computers, Materials & Continua, 64(2), 1091-1105. https://doi.org/10.32604/cmc.2020.09834
Vancouver Style
Zhou H, Sun G, Fu S, Fan X, Jiang W, Hu S, et al. A distributed approach of big data mining for financial fraud detection in a supply chain. Comput Mater Contin. 2020;64(2):1091-1105 https://doi.org/10.32604/cmc.2020.09834
IEEE Style
H. Zhou et al., "A Distributed Approach of Big Data Mining for Financial Fraud Detection in a Supply Chain," Comput. Mater. Contin., vol. 64, no. 2, pp. 1091-1105. 2020. https://doi.org/10.32604/cmc.2020.09834
Citations