Proceedings of the Twenty-Seventh Hawaii International Conference on System Sciences HICSS-94 1994
DOI: 10.1109/hicss.1994.323314
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Credit card fraud detection with a neural-network

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Cited by 282 publications
(143 citation statements)
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“…More studies focus on supervised techniques using evidence of past fraudulent transactions to infer the suspiciousness of future transactions. The most prevalent technique for supervised credit card fraud detection is artificial neural networks (ANN's) (Ghosh and Reilly, 1994;Aleskerov et al, 1997;Dorronsoro et al, 1997;Brause et al, 1999;Maes et al, 2002;Syeda et al, 2002;Shen et al, 2007). While ANN's generally achieve a high performance, they are black box models which lack interpretability.…”
Section: Accepted Manuscriptmentioning
confidence: 99%
“…More studies focus on supervised techniques using evidence of past fraudulent transactions to infer the suspiciousness of future transactions. The most prevalent technique for supervised credit card fraud detection is artificial neural networks (ANN's) (Ghosh and Reilly, 1994;Aleskerov et al, 1997;Dorronsoro et al, 1997;Brause et al, 1999;Maes et al, 2002;Syeda et al, 2002;Shen et al, 2007). While ANN's generally achieve a high performance, they are black box models which lack interpretability.…”
Section: Accepted Manuscriptmentioning
confidence: 99%
“…Artificial Neural Networks (ANN) being one of the mostly used machine learning techniques, is one of the most suitable mechanisms for identifying anomalies. This area has witnesses a huge contribution towards anomaly detection [7][8][9][10][11][12][13]21]. Several ensemble methods that work well in such applications include random forests [14], SVM [15] genetic algorithms [16] and hidden Markov models [17].…”
Section: Related Workmentioning
confidence: 99%
“…A group researcher from different study have proposed credit card fraud detection with a neural network [4][5][6][7].…”
Section: Single Classification Methodsmentioning
confidence: 99%