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A Review of Credit Card Fraud Detection Using Machine Learning Techniques

机译:用机器学习技术审查信用卡欺诈检测

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Big Data technologies concern several critical areas such as Healthcare, Finance, Manufacturing, Transport, and E-Commerce. Hence, they play an indispensable role in the financial sector, especially within the banking services which are impacted by the digitalization of services and the evolvement of e-commerce transactions. Therefore, the emergence of the credit card use and the increasing number of fraudsters have generated different issues that concern the banking sector. Unfortunately, these issues obstruct the performance of Fraud Control Systems (Fraud Detection Systems & Fraud Prevention Systems) and abuse the transparency of online payments. Thus, financial institutions aim to secure credit card transactions and allow their customers to use e-banking services safely and efficiently. To reach this goal, they try to develop more relevant fraud detection techniques that can identify more fraudulent transactions and decrease frauds. The purpose of this article is to define the fundamental aspects of fraud detection, the current systems of fraud detection, the issues and challenges of frauds related to the banking sector, and the existing solutions based on machine learning techniques.
机译:大数据技术涉及若干关键领域,如医疗保健,金融,制造,运输和电子商务。因此,他们在金融部门发挥不可或缺的作用,特别是在银行服务范围内受到服务数字化和电子商务交易的演变影响的银行服务。因此,信用卡使用的出现和越来越多的欺诈者已经产生了关注银行业的不同问题。不幸的是,这些问题妨碍了欺诈控制系统(欺诈检测系统和欺诈预防系统)的表现,并滥用在线支付的透明度。因此,金融机构旨在确保信用卡交易,并允许客户安全有效地使用电子银行服务。为了实现这一目标,他们试图开发更多相关的欺诈检测技术,可以识别更多的欺诈性交易和减少欺诈。本文的目的是确定欺诈检测的基本方面,目前的欺诈检测系统,与银行业相关的欺诈问题和欺诈挑战,以及基于机器学习技术的现有解决方案。

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