信用卡欺诈检测的机器学习技术

Hossam Eldin Mohammed Abd El-Hamid Ahmed Abdou, W. Khalifa, Mohamed Roushdy, A. Salem
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引用次数: 2

摘要

“诈骗”一词,在我们的脑海中总是让人关注到信用卡诈骗。而在信用卡交易量大幅增加之后,近年来信用卡诈骗案件急剧增加。因此,欺诈检测应包括监视个人/客户的消费态度,以确定,避免和发现不必要的行为。由于信用卡是人们在网上和日常购物中最主要的支付方式,信用卡诈骗案也随之高发。欺诈检测不仅涉及捕获欺诈行为,而且还涉及尽快发现欺诈行为,因为欺诈行为造成数百万美元的商业损失,并且随着时间的推移而增加,这极大地影响了全球经济。在本文中,我们介绍了14种不同的数据挖掘技术如何成功地结合起来,以获得高欺诈覆盖率和高或低错误率,每种技术的优点和缺点,以及研究人员在研究中使用的数据集
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Machine Learning Techniques for Credit Card Fraud Detection
The term “fraud”, it always concerned about credit card fraud in our minds. And after the significant increase in the transactions of credit card, the fraud of credit card increased extremely in last years. So the fraud detection should include surveillance of the spending attitude for the person/customer to the determination, avoidance, and detection of unwanted behavior. Because the credit card is the most payment predominant way for the online and regular purchasing, the credit card fraud raises highly. The Fraud detection is not only concerned with capturing of the fraudulent practices, but also, discover it as fast as they can, because the fraud costs millions of dollar business loss and it is rising over time, and that affects greatly the worldwide economy. . In this paper we introduce 14 different techniques of how data mining techniques can be successfully combined to obtain a high fraud coverage with a high or low false rate, the Advantage and The Disadvantages of every technique, and The Data Sets used in the researches by researcher
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