Paper Info
Title
Visual analytics for event detection: Focusing on fraud.
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.
Year
DOI
Venue
2018
10.1016/j.visinf.2018.11.001
Visual Informatics
Keywords
Field
DocType
Visual knowledge discovery,Time series data,Business and finance visualization,Financial fraud detection
Data science,Categorization,Time series,Financial fraud,Visualization,Computer science,Visual analytics,Exploit,Stock market
Journal
Volume
Issue
ISSN
2
4
2468-502X
Citations 
PageRank 
References 
2
0.36
33
Authors
5
Name
Order
Citations
PageRank
Roger A. Leite1194.67
Theresia Gschwandtner217117.43
Silvia Miksch32212174.85
Erich Gstrein480.75
Johannes Kuntner521.38