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Vis Inf
Vis Inf  2018, Vol. 2 Issue (4): 198-212    DOI: 10.1016/j.visinf.2018.11.001
论文     
采用可视化分析来检测欺诈事件
Roger Almeida Leitea, Theresia Gschwandtnera, Silvia Mikscha, Erich Gstreinb, Johannes Kuntnerc
aVienna University of Technology, Austria bs IT Solutions AT Spardat GmbH, Austria  cErste Group IT International, Austria
Visual analytics for event detection: Focusing on fraud
Roger Almeida Leitea, Theresia Gschwandtnera, Silvia Mikscha, Erich Gstreinb, Johannes Kuntnerc
aVienna University of Technology, Austria bs IT Solutions AT Spardat GmbH, Austria  cErste Group IT International, Austria
 全文: PDF 
摘要： 许多领域都在检测大量数据中的异常事件。例如，在金融数据领域，发现可疑事件是识别和防止欺诈的先决条件。因此，各种金融欺诈检测方法已开始利用可视化技术。但是，迄今为止还没有一项研究对这一方面的不同方法进行系统性的概述，以揭示出这些方法的共同策略和差异所在。本文对现有的视觉欺诈检测方法进行了梳理，对不同的任务和解决方案进行分类，以发现和开辟进一步的研究机会。在本项工作中，我们对银行、股票市场、电信公司、保险公司和内部欺诈等五个主要领域的欺诈检测方案进行了分析。这些选定的领域都具有类似的时序和多元数据特征。在调查中，我们（1）分析了这一方面研究的现状; （2）定义了一个可覆盖不同应用领域、可视化方法、交互技术和分析方法的分类方案; （3）根据所提方案，对每种方法逐一进行描述和讨论; （4）辨识挑战性问题，明确下一步的研究课题。

关键词： 视觉知识发现;  时序数据;  商业和金融可视化;  金融诈骗检测
    
Abstract: The detection of anomalous events in huge amounts of data is sought in many domains. For instance, in the context of financial data, the detection of suspicious events is a prerequisite to identify and prevent attempts to defraud. Hence, various financial fraud detection approaches have started to exploit Visual Analytics techniques. However, there is no study available giving a systematic outline of the different approaches in this field to understand common strategies but also differences. Thus, we present a survey of existing approaches of visual fraud detection in order to classify different tasks and solutions, to identify and to propose further research opportunities. In this work, fraud detection solutions are explored through five main domains: banks, the stock market, telecommunication companies, insurance companies, and internal frauds. The selected domains explored in this survey were chosen for sharing similar time-oriented and multivariate data characteristics. In this survey, we (1) analyze the current state of the art in this field; (2) define a categorization scheme covering different application domains, visualization methods, interaction techniques, and analytical methods which are used in the context of fraud detection; (3) describe and discuss each approach according to the proposed scheme; and (4) identify challenges and future research topics.
Key words: Visual knowledge discovery    Time series data    Business and finance visualization    Financial fraud detection
出版日期: 2019-01-08
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Roger Almeida Leite
Theresia Gschwandtner
Silvia Miksch
Erich Gstrein
Johannes Kuntner

引用本文:

Roger Almeida Leite, Theresia Gschwandtner, Silvia Miksch, Erich Gstrein, Johannes Kuntner. Visual analytics for event detection: Focusing on fraud. Vis Inf, 2018, 2(4): 198-212.

链接本文:

http://www.zjujournals.com/vi/CN/10.1016/j.visinf.2018.11.001    或    http://www.zjujournals.com/vi/CN/Y2018/V2/I4/198

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