Cost sensitive modeling of credit card fraud using neural network strategy
Cost sensitive modeling of credit card fraud using neural network strategy
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DOI：
10.1109/icspis.2016.7869880
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发表时间：
2016-12
期刊：
影响因子：
--
通讯作者：
F. Ghobadi;M. Rohani
中科院分区：
文献类型：
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作者：
F. Ghobadi;M. Rohani
由于电子商务和电子支付系统的快速发展，与信用卡相关的银行交易中的欺诈行为正在上升。本文旨在开发一种基于人工神经网络（ANN）和元成本程序的信用卡欺诈检测（CCFD）模型，以降低风险声誉和损失风险。人工神经网络策略已被用于信用卡欺诈的预防和检测。由于数据的不平衡性质（欺诈和非欺诈案件），欺诈交易的检测很难实现。为了解决数据不平衡的问题，增加了Meta Cost过程。该模型基于误用检测方法，称为代价敏感神经网络（CSNN）。与基于人工免疫系统（AIS）的模型相比，该模型节省了成本，提高了检出率。本研究的数据取自巴西一家大型信用卡发行商提供的真实交易数据。
Due to the rapid growth in e-business and electronic payment systems, Fraud is rising in banking transactions associated with credit cards. This paper intends to develop a credit card fraud detection (CCFD) model based on Artificial Neural Networks (ANN) and Meta Cost procedure to reduce risk reputation and risk of loss. ANN strategy have been used for credit card fraud prevention and detection. Because of the unbalanced nature of the data (Fraud and Non-Fraud cases), the detection of fraudulent transactions is difficult to achieve. To deal with the problem of imbalanced data, Meta Cost procedure is added. The proposed model, which is called Cost Sensitive Neural Network (CSNN), is based on misuse detection approach. Compared to the model based on Artificial Immune System (AIS), this model showed cost saving and increased detection rate. Data of this study is taken from real transactional data provided by a big Brazilian credit card issuer.