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REALTIME FRAUD DETECTION IN THE BANKING SECTOR USING DATA MINING TECHNIQUES/ALGORITHM

John, S. N. and Okokpujie, Kennedy O. and Anele, C. and Olajide, Funminiyi and Kennedy, Chinyere Grace REALTIME FRAUD DETECTION IN THE BANKING SECTOR USING DATA MINING TECHNIQUES/ALGORITHM. In: International Conference on Computational Science and Computational Intelligence, 2016.

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Abstract

Abstract—The banking sector is a very important sector in our present day generation where almost every human has to deal with the bank either physically or online. In dealing with the banks, the customers and the banks face the chances of been trapped by fraudsters. Examples of fraud include insurance fraud, credit card fraud, accounting fraud, etc. Detection of fraudulent activity is thus critical to control these costs. This paper hereby addresses bank fraud detection via the use of data-mining techniques; association, clustering, forecasting, and classification to analyze the customer data in order to identify the patterns that can lead to frauds. Upon identification of the patterns, adding a higher level of verification/authentication to banking processes can be added Keywords: Data mining techniques, banking sector, fraud, and authentication.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Data Mining, Authentication, Real Time Fraud, Banking Sector.
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions: Faculty of Engineering, Science and Mathematics > School of Electronics and Computer Science
Faculty of Engineering, Science and Mathematics > School of Engineering Sciences
Depositing User: Mrs Patricia Nwokealisi
Date Deposited: 22 Jun 2017 15:41
Last Modified: 22 Jun 2017 15:41
URI: http://eprints.covenantuniversity.edu.ng/id/eprint/8449

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