FRAUD DETECTION IN BANKING ELECTRONIC PAYMENT USING DATA MINING

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HOSSEINI MARZIEH

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Seminar Paper

Paper Information

Seminar: INTERNATIONAL CONFERENCE ON SCIENCE AND ENGINEERING
Year:2015 | Issue:1

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Title

FRAUD DETECTION IN BANKING ELECTRONIC PAYMENT USING DATA MINING

Author(s)

HOSSEINI MARZIEH

Keywords

FRAUD

ELECTRONIC PAYMENT SYSTEMS

FINANCIAL TRANSACTIONS

DATA MINING

GMDH NEURAL NETWORK

Abstract

FRAUD CONTINUING VARIATION IS ONE OF THE MOST IMPORTANT FRAUD DETECTION CHALLENGE IN ELECTRONIC PAYMENT SYSTEMS. ELECTRONIC MARKETS NEED HIGH PERFORMANCE FRAUD DETECTION METHODS WITH TOP ACCURACY. IN THIS PROJECT WE HAVE USED LOGISTIC REGRESSION, BP NEURAL NETWORK AND GMDH NEURAL NETWORK TO MAKE A USEFUL METHOD DETECTING FRAUD IN A BANK FINANCIAL TRANSACTIONS. WE HAVE IMPLEMENTED THESE METHODS ON REAL DATASET AND HAVE MEASURED THEIR RESULTS. THE RESULTS ARE GMDH NEURAL NETWORK 91.73%, LOGISTIC REGRESSION 89.26% AND BP NEURAL NETWORK 90.54%. THEREFORE OUR RECOMMENDED METHOD HAS THE BEST ACCURACY.

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APA: Copy

HOSSEINI, MARZIEH. (2015). FRAUD DETECTION IN BANKING ELECTRONIC PAYMENT USING DATA MINING. INTERNATIONAL CONFERENCE ON SCIENCE AND ENGINEERING. SID. https://sid.ir/paper/909104/en

Vancouver: Copy

HOSSEINI MARZIEH. FRAUD DETECTION IN BANKING ELECTRONIC PAYMENT USING DATA MINING. 2015. Available from: https://sid.ir/paper/909104/en

IEEE: Copy

MARZIEH HOSSEINI, “FRAUD DETECTION IN BANKING ELECTRONIC PAYMENT USING DATA MINING,” presented at the INTERNATIONAL CONFERENCE ON SCIENCE AND ENGINEERING. 2015, [Online]. Available: https://sid.ir/paper/909104/en

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