使用机器学习和数据挖掘技术的信用卡欺诈检测-文献综述

Devicharan Rai M., Jagadeesha S. N.
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引用次数: 1

摘要

目的:了解使用机器学习(ML)和数据挖掘(DM)技术的信用卡欺诈检测(CCFD)中使用的算法,回顾该领域的主要发现,并提出研究空白或未解决的问题。为了了解ML和DM领域的当前讨论。设计/方法论/方法:基于学术论文、网络文章、会议记录、期刊和其他来源的数据,对使用ML和DM的CCFD进行了调查。对信息进行审查和分析。结果/发现:识别信用卡欺诈对于保护个人或组织的资产至关重要。即使我们有各种各样的保护措施来防止欺诈活动,骗子也可能想出一种绕过检查点的方法。我们必须创建简单有效的算法,使用ML和DM来提前预测欺诈活动。独创性/价值:从不同的来源研究了CCFD中的ML和DM算法。由于欺诈者在数字犯罪中的最新方法已经发展,这一领域需要研究。所获得的信息将有助于创建新的方法或改进当前算法的结果。论文类型:文献综述。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Credit Card Fraud Detection using Machine Learning and Data Mining Techniques - a Literature Survey
Purpose: To understand the algorithms used in Credit Card Fraud Detection (CCFD) using Machine Learning (ML) and Data Mining (DM) techniques, Review key findings in the area and come up with research gaps or unresolved problem. To become knowledgeable about the current discussions in the area of ML and DM. Design/Methodology/Approach: The survey on CCFD using ML and DM was conducted based on data from academic papers, web articles, conference proceedings, journals and other sources. Information is reviewed and analysed. Results/Findings: Identification of credit card fraud is essential for protecting a person's or an organization's assets. Even though we have various safeguards in place to prevent fraudulent activity, con artists may develop a method to get around the checkpoints. We must create straightforward and efficient algorithms employing ML and DM to anticipate fraudulent activities in advance. Originality/Value: Study of ML and DM algorithms in CCFD from diverse sources is done. This area needs study due to recent methods by fraudsters in digital crime have developed. The information acquired will be helpful for creating new methodologies or improving the outcomes of current algorithms. Type of Paper: Literature Review.
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