EVA: Visual Analytics to Identify Fraudulent Events

Leite, Roger A.; Gschwandtner, Theresia; Miksch, Silvia; Kriglstein, Simone; Pohl, Margit; Gstrein, Erich; Kuntner, Johannes

doi:10.1109/tvcg.2017.2744758

Cited by 51 publications

(48 citation statements)

References 22 publications

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“…Clients may collude with employees in financial institutes in activities of money laundering, unauthorized transactions, and embezzlement, etc. [101]. Other anomalies include unexpected business processes [102], [100] and high default group in a network of guaranteed loans [103].…”

Section: Transactionmentioning

confidence: 99%

“…Multidimensional data is often used in conjunction with spatiotemporal data to detect anomalous transactions. By probing into time series along with details of the amount of money transferred [101], [98], [105], the number of transactions within a period of time [99], [105], and number of the activities that are new to the user [106], analysts can gain an overall picture of the histories of financial transactions. An example of using multidimensional and spatiotemporal information is VisImpact [105].…”

Section: A Data Typesmentioning

confidence: 99%

“…New transactions are then evaluated against the profiles to see if they are anomalous. The visualization tool EVA [101] generates customer profiles and provides different statistical measures for new transactions. The statistical profiles combine histograms and rules specified by experts to provide references.…”

Section: B Anomaly Detection Techniquesmentioning

confidence: 99%

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Visual Analytics of Anomalous User Behaviors: A Survey

Shi

Liu

Tong

et al. 2020

IEEE Trans. Big Data

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The increasing accessibility of data provides substantial opportunities for understanding user behaviors. Unearthing anomalies in user behaviors is of particular importance as it helps signal harmful incidents such as network intrusions, terrorist activities, and financial frauds. Many visual analytics methods have been proposed to help understand user behaviorrelated data in various application domains. In this work, we survey the state of art in visual analytics of anomalous user behaviors and classify them into four categories including social interaction, travel, network communication, and transaction. We further examine the research works in each category in terms of data types, anomaly detection techniques, and visualization techniques, and interaction methods. Finally, we discuss findings and potential research directions.• We categorize four user behaviors, including social interaction, travel, network communication, and transaction based on the data collected from specific data sources. We extract four common data types from these four behaviors, including text, network, spatiotemporal information, and multidimensional data.• We review how research works use visualization techniques combined with interaction methods to analyze anomalous user behaviors. We extract six

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Section: Transactionmentioning

confidence: 99%

Section: A Data Typesmentioning

confidence: 99%

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Visual Analytics of Anomalous User Behaviors: A Survey

Shi

Liu

Tong

et al. 2020

IEEE Trans. Big Data

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“…Clustering algorithms support the analysis, and the visual exploration of the networks is facilitated by abstraction and elaboration interaction techniques. A recent approach by Leite et al [13] proposes "EVA" (Event detection with Visual Analytics), a VA approach to identify fraudulent events based on bank transactions logs. EVA combines well-known visualization techniques with a profile-based detection algorithm, using real-world data and experts to evaluate the approach.…”

Section: Related Workmentioning

confidence: 99%

Sabrina: Modeling and Visualization of Financial Data over Time with Incremental Domain Knowledge

Arleo

Sorger

Tsigkanos

et al. 2019

2019 IEEE Visualization Conference (VIS)

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Figure 1: The different visualizations composing the Sabrina system. The three views (a, b, c) display the firm density and transaction data of an Austrian economy dataset [3]: (a) gradient encoding in blue/red indicates whether the firm density in a hexagonal area increased/decreased in respect to the previous time step; (b) inferred transactions between individual companies; (c) regionally clustered firm data. The interface allows the user to (d) navigate the temporal data dimension, (e) show aggregated details on selected transaction flows, and (f) adjust encoding and filtering options. Tooltips (g) show details on firms within a bin. ABSTRACTInvestment planning requires knowledge of the financial landscape on a large scale, both in terms of geo-spatial and industry sector distribution. There is plenty of data available, but it is scattered across heterogeneous sources (newspapers, open data, etc.), which makes it difficult for financial analysts to understand the big picture. In this paper, we present Sabrina, a financial data analysis and visualization approach that incorporates a pipeline for the generation of firm-to-firm financial transaction networks. The pipeline is capable of fusing the ground truth on individual firms in a region with (incremental) domain knowledge on general macroscopic aspects of the economy. Sabrina unites these heterogeneous data sources within a uniform visual interface that enables the visual analysis process. In a user study with three domain experts, we illustrate the usefulness of Sabrina, which eases their analysis process.

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“…In order to successfully benefit from the wealth of information in large and complex datasets, interactive visual data exploration and analysis is used in a variety of application areas such as text analysis [MCCD13], fraud detection [LGM∗18], machine learning [PHvG∗18], and life sciences [OKB∗08, LvUH∗18]. Multidimensional data is often a core challenge in these processes and dimensionality reduction is regularly an essential part of the approach.…”

Section: Introductionmentioning

confidence: 99%

Focus+Context Exploration of Hierarchical Embeddings

Höllt

Vilanova

Pezzotti

et al. 2019

Computer Graphics Forum

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Hierarchical embeddings, such as HSNE, address critical visual and computational scalability issues of traditional techniques for dimensionality reduction. The improved scalability comes at the cost of the need for increased user interaction for exploration. In this paper, we provide a solution for the interactive visual Focus+Context exploration of such embeddings. We explain how to integrate embedding parts from different levels of detail, corresponding to focus and context groups, in a joint visualization. We devise an according interaction model that relates typical semantic operations on a Focus+Context visualization with the according changes in the level‐of‐detail‐hierarchy of the embedding, including also a mode for comparative Focus+Context exploration and extend HSNE to incorporate the presented interaction model. In order to demonstrate the effectiveness of our approach, we present a use case based on the visual exploration of multi‐dimensional images.

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EVA: Visual Analytics to Identify Fraudulent Events

Cited by 51 publications

References 22 publications

Visual Analytics of Anomalous User Behaviors: A Survey

Visual Analytics of Anomalous User Behaviors: A Survey

Sabrina: Modeling and Visualization of Financial Data over Time with Incremental Domain Knowledge

Focus+Context Exploration of Hierarchical Embeddings

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