Open Access

ARTICLE

A Distributed Approach of Big Data Mining for Financial Fraud Detection in a Supply Chain

by Hangjun Zhou, Guang Sun, Sha Fu, Xiaoping Fan, Wangdong Jiang, Shuting Hu, Lingjiao Li

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.

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Citations