Detection of fraud in banking transactions using big data clustering technique customer behavior indicators

Kian, R ORCID: https://orcid.org/0000-0001-8786-6349 and Obaid, HS, 2022. Detection of fraud in banking transactions using big data clustering technique customer behavior indicators. Journal of Applied Research on Industrial Engineering, 9 (3), pp. 264-273. ISSN 2538-5100

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Abstract

Human life today is intertwined with abundant trade and economic exchanges, and life would not be possible without trade and commerce. One of the main pillars of financial exchanges are banks and financial and credit institutions, which, as the vital arteries of the economy, are responsible for transferring funds and keeping the economy alive. In the world of economic competition between organizations, profitability and proper performance for stakeholders are the basic principles of the organization's survival. To increase profitability, banks must take measures that, in addition to reducing costs, increase the level of service and customer satisfaction. The best way to do this is to use new technologies and orient the bank's policies to provide services in person and independent of time and place. The use of new technologies in the banking system sometimes leads to customers' distrust and distrust of the bank. Therefore, solutions to detect fraud in banking transactions should be provided. This article aims to discover a model for face-to-face transactions and to establish a system to block fraudulently issued transactions. Therefore, a big data clustering method is designed to timely identify bribery in banking transactions. The results show that using the big data clustering method in the fastest time can detect and stop possible fraud in customers' banking transactions.

Item Type:	Journal article
Publication Title:	Journal of Applied Research on Industrial Engineering
Creators:	Kian, R. and Obaid, H.S.
Publisher:	Ayandegan Institute of Higher Education, Iran
Date:	July 2022
Volume:	9
Number:	3
ISSN:	2538-5100
Identifiers:	Number Type 10.22105/jarie.2021.307635.1387 DOI 1601339 Other
Rights:	Licensee Journal of Applied Research on Industrial Engineering. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0).
Divisions:	Schools > Nottingham Business School
Record created by:	Laura Ward
Date Added:	23 Sep 2022 10:59
Last Modified:	23 Sep 2022 10:59
URI:	https://irep.ntu.ac.uk/id/eprint/47097

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