Anti-Money Laundering: Using data visualization to identify suspicious activity

Singh, Kishore; Best, Peter

doi:10.1016/j.accinf.2019.06.001

Cited by 65 publications

(71 citation statements)

References 20 publications

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“…Such DVA systems offer users a thorough understanding of a model and hints on how to troubleshoot and develop it. In the recent years, several studies have been conducted, where DVA has proven their effective and noteworthy impact in many fields, especially in big data analysis [115], human cognitive and perception science [154], analyze [43] IEEE VCG Yue, Xuanwu, (2019) [146] PhD diss Singh, (2019) [118] IJAIS 2018 Kwon et al, (2018) [66] IEEE VIS Cashman et al, (2018) [12] IEEE CGA Garcia et al, (2018) [34] CG Peixinho et al, (2018) [100] CGIP [19] IEEE CGA Yue et al, (2018) [147] IEEE VCG 2017 [76] IEEE ITS Pezzotti et al, (2017) [101] IEEE VCG Wongsuphasawat et al, (2017) [141] IEEE VCG Samek et al, (2017) [110] arXiv Leite et al, (2017) [70] IEEE VCG healthcare systems [72], and tourism management system [14]. In addition, DVA has the greatest impact of synthesizing information, deriving insights from massive, ambiguous, unstructured, unexpected patterns, decision making, and communicating assessment for action [59].…”

Section: Impact Of Deep Visual Analyticsmentioning

confidence: 99%

Deep Visual Analytics (DVA): Applications, Challenges and Future Directions

Islam¹,

Akter²,

Ratan³

et al. 2021

HCIS

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Visual interactive system (VIS) has been received significant attention for solving various complex problems. However, designing and implementing a novel VIS with the large scale of data is a challenging task. While existing studies have applied various visual analytics (VA) to analyze and visualize insightful information, deep visual analytics (DVA) have considered as a promising technique to provide input evidences and explain system results. In this study, we present several deep learning (DL) techniques for analyzing data with visualization, which summarizes the state-of-the-art review on (i) big data analysis, (ii) cognitive and perception science, (iii) customer behavior analysis, (iv) natural language processing, (v) recommended system, (vi) healthcare analysis, (vii) fintech ecosystem, and (viii) tourism management. We present open research challenges for emerging DVA in the visualization community. We also highlight some key themes from the existing literature that may help to explore for future study. Thus, our goal is to help readers and researchers in DL and VA to understand key aspects in designing VIS for analysing data.

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Section: Impact Of Deep Visual Analyticsmentioning

confidence: 99%

Deep Visual Analytics (DVA): Applications, Challenges and Future Directions

Islam¹,

Akter²,

Ratan³

et al. 2021

HCIS

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“…The main characteristics of existing approaches are discussed in this section. A prototype application AML2ink provides a visualization of relations between accounts in transactions to further identify suspicious activity [19]. An SQL query is responsible for data processing, while GraphViz renders and produces the visualization.…”

Section: Related Workmentioning

confidence: 99%

Blockchain-Enabled Transaction Scanning Method for Money Laundering Detection

Oad¹,

Razaque²,

Tolemyssov³

et al. 2021

Preprint

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Currently, life cannot be imagined without the use of bank cards for purchases or money transfers; however, their use provides new opportunities for money launders and terrorist organizations. This paper proposes a Blockchain-enabled transaction scanning (BTS) method for the detection of anomalous actions. The BTS method specifies the rules for outlier detection and rapid movements of funds, which restrict anomalous actions in transactions. The specified rules determine the specific patterns of malicious activities in the transactions. Furthermore, the rules of the BTS method scan the transaction history and provide a list of entities that receive money suspiciously. Finally, the Blockchain-enabled process is used to restrict the money laundering. To validate the performance of the proposed BTS, the Spring Boot application is built based on the Java programming language. Based on experimental results, the proposed BTS method automates the process of investigating transactions and restricts the money laundering incident.

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“…Consequently, users confronted with such problems have already established the use of Big Data visualizations. Pioneering examples can be found in fraud detection (Singh and Best 2019;Keim 2001), or when analyzing network traffic (Keim et al 2006) as well as business models to reduce costs but maintain quality (Appelbaum et al 2017). Further, Big Data visualizations are also customary in companies with a high focus on personalized marketing and social media to evaluate the implications of certain initiatives on product satisfaction and innovation (Keim 2001;Appelbaum et al 2017).…”

Section: Theoretical Background and Hypothesesmentioning

confidence: 99%

Does design matter when visualizing Big Data? An empirical study to investigate the effect of visualization type and interaction use

2020

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The need for good visualization is increasing, as data volume and complexity expand. In order to work with high volumes of structured and unstructured data, visualizations, supporting the ability of humans to make perceptual inferences, are of the utmost importance. In this regard, a lot of interactive visualization techniques have been developed in recent years. However, little emphasis has been placed on the evaluation of their usability and, in particular, on design characteristics. This paper contributes to closing this research gap by measuring the effects of appropriate visualization use based on data and task characteristics. Further, we specifically test the feature of interaction as it has been said to be an essential component of Big Data visualizations but scarcely isolated as an independent variable in experimental research. Data collection for the large-scale quantitative experiment was done using crowdsourcing (Amazon Mechanical Turk). The results indicate that both, choosing an appropriate visualization based on task characteristics and using the feature of interaction, increase usability considerably.

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Anti-Money Laundering: Using data visualization to identify suspicious activity

Cited by 65 publications

References 20 publications

Deep Visual Analytics (DVA): Applications, Challenges and Future Directions

Deep Visual Analytics (DVA): Applications, Challenges and Future Directions

Blockchain-Enabled Transaction Scanning Method for Money Laundering Detection

Does design matter when visualizing Big Data? An empirical study to investigate the effect of visualization type and interaction use

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