Learning of neural networks for fraud detection based on a partial area under curve

Authors:
Lae-Jeong Park
Affiliations:
Department of Electrical Engineering, Kangnung National University, Kangnung Gangwon-do, Korea
Venue:
ISNN'05 Proceedings of the Second international conference on Advances in neural networks - Volume Part II
Year:
2005

Citing 4
Cited 1

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Distributed Data Mining in Credit Card Fraud Detection

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The use of the area under the ROC curve in the evaluation of machine learning algorithms

Pattern Recognition
Neural fraud detection in credit card operations

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A New Approach to Outlier Detection

ICCS '07 Proceedings of the 7th international conference on Computational Science, Part III: ICCS 2007

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Abstract

This paper addresses an effective approach of training a neural network (NN) classifier for real-world credit card fraud detection. In the proposed approach, an evolutionary search algorithm is used to directly improve the performance of a NN classifier in a local operating range in terms of the detection rate of fraudulent usages by optimizing a partial area under a domain-specific curve. The experimental results on 'real' credit card transactions data demonstrate that the proposed approach produces classifiers of a higher detection rate in a desired range of false detection rates.