Graph-based anomaly detection (GBAD) approaches are among the most popular techniques used to analyze connectivity patterns in communication networks and identify suspicious behaviors. Given the different GBAD approaches proposed for fraud detection, in this study, we develop a framework to synthesize the existing literature on the application of GBAD methods in fraud detection published between 2007 and 2018. This study aims to investigate the present trends and identify the key challenges that require significant research efforts to increase the credibility of the technique. Additionally, we provide some recommendations to deal with these challenges.

中文翻译：

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欺诈检测：基于图的异常检测方法的系统文献综述

基于图的异常检测（GBAD）方法是用于分析通信网络中的连接模式并识别可疑行为的最受欢迎的技术之一。鉴于针对欺诈检测提出的GBAD方法不同，在本研究中，我们建立了一个框架，以综合有关2007年至2018年出版的GBAD方法在欺诈检测中的应用的现有文献。本研究旨在调查当前趋势并确定关键这些挑战需要大量的研究工作才能提高技术的可信度。此外，我们提供了一些应对这些挑战的建议。