Authors: Vikash Sharma; Bhavna Pandey; Vipin Kumar
Addresses: Faculty of Computer Studies, Symbiosis International University, Pune, 412115 Maharashtra, India ' Symbiosis School of Banking & Finance, Symbiosis International University, Pune, 412115 Maharashtra, India ' Neurapses Technologies Pvt Ltd., Bangalore, 560100 Karnataka, India
Abstract: Big Data is playing a very significant role to take any industry forward. In the context of the financial sector and fraud detection, automated fraud detection tries to collect useful information to reduce financial frauds by doing analysis and data mining of Big Data, especially structured data. Even a significant attention of Big Data usage has shifted towards supply chain management (SCM). Although, many dimensions of Big Data have been studied and researched in SCM, there lies a missing gap in the understanding of unstructured data for financial fraud detection. With the help of this paper, we would like to propose a theoretical framework to study this dimension and analyse how individual enablers of unstructured data impacts SCM. As such, the paper intends to evaluate how much useful this unstructured data can be in reducing financial frauds. Finally, we outline the limitations and challenges of our study and further research directions.
Keywords: automated fraud detection; big data; financial fraud; unstructured data; supply chain management; SCM.
International Journal of Automation and Logistics, 2016 Vol.2 No.4, pp.332 - 348
Received: 28 May 2016
Accepted: 05 Jul 2016
Published online: 14 Nov 2016 *