信用卡欺诈检测的卷积神经网络模型

Muhammad Liman Gambo, A. Zainal, Mohamad Nizam Kassim
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引用次数: 0

摘要

如今,通过各种电子商务平台进行的网上交易越来越普遍,信用卡(CC)在各种网上交易中被大量使用。然而,随着业务转型,信用卡欺诈(CCF)策略不断发展,导致客户和金融机构每年损失数十亿美元。因此,有必要从大量正常交易中有效地发现欺诈者发起的欺诈性交易。因此,本研究提出了一种卷积神经网络(CNN)信用卡欺诈检测模型,使用自适应合成(ADASYN)采样技术来解决不平衡数据集。与已有研究相比,该模型的准确率、精密度和召回率分别达到0.9982、0.9965和0.9999。
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A Convolutional Neural Network Model for Credit Card Fraud Detection
Nowadays, online transactions through various ecommerce platforms are becoming more prevalent, and Credit Card (CC) is significantly used in various online transactions. However, Credit Card Fraud (CCF) strategies continue to evolve with the business transformation, causing customers as well as the financial institutions to lose billions of dollars annually. Hence, effective detection of fraudulent transactions initiated by fraudsters from the voluminous array of normal transactions is ever necessary. Hence, a Convolutional Neural Network (CNN) model for credit card fraud detection is proposed in this study using Adaptive Synthetic (ADASYN) sampling technique to address the imbalance dataset. The proposed model has achieved 0.9982, 0.9965, and 0.9999, accuracy, precision, and recall, respectively compared to other existing studies.
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