Visual analytics for event detection: Focusing on fraud

Leite, Roger A.; Gschwandtner, Theresia; Miksch, Silvia; Gstrein, Erich; Kuntner, Johannes

doi:10.1016/j.visinf.2018.11.001

Cited by 42 publications

(33 citation statements)

References 23 publications

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“…Some researchers consider the aid of visual analytics for understanding AI models, also in the fraud domain [43,44]. In this context, [21] provides a visual analytics tool to support domain experts in their fraud detection workflow.…”

Section: Related Workmentioning

confidence: 99%

“…This is an open access post-print version; the final authenticated version is available online at https://link.springer.com/chapter/10.1007/978-3-030-57321-8_18 by © IFIP International Federation for Information Processing 2020. [22,36,43,44,55,68,71,72,80,88,89,92,93,95,96,97,98,101,102,103,104,105,106,107] 24 Yes…”

Section: Usage Of Scenarios For Requirements Elicitation For Explanatmentioning

confidence: 99%

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Scenario-Based Requirements Elicitation for User-Centric Explainable AI

Cirqueira

Nedbal

Helfert

et al. 2020

Lecture Notes in Computer Science

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Explainable Artificial Intelligence (XAI) develops technical explanation methods and enable interpretability for human stakeholders on why Artificial Intelligence (AI) and machine learning (ML) models provide certain predictions. However, the trust of those stakeholders into AI models and explanations is still an issue, especially domain experts, who are knowledgeable about their domain but not AI inner workings. Social and user-centric XAI research states it is essential to understand the stakeholder's requirements to provide explanations tailored to their needs, and enhance their trust in working with AI models. Scenariobased design and requirements elicitation can help bridge the gap between social and operational aspects of a stakeholder early before the adoption of information systems and identify its real problem and practices generating user requirements. Nevertheless, it is still rarely explored the adoption of scenarios in XAI, especially in the domain of fraud detection to supporting experts who are about to work with AI models. We demonstrate the usage of scenario-based requirements elicitation for XAI in a fraud detection context, and develop scenarios derived with experts in banking fraud. We discuss how those scenarios can be adopted to identify user or expert requirements for appropriate explanations in his daily operations and to make decisions on reviewing fraudulent cases in banking. The generalizability of the scenarios for further adoption is validated through a systematic literature review in domains of XAI and visual analytics for fraud detection.

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Section: Related Workmentioning

confidence: 99%

Section: Usage Of Scenarios For Requirements Elicitation For Explanatmentioning

confidence: 99%

Scenario-Based Requirements Elicitation for User-Centric Explainable AI

Cirqueira

Nedbal

Helfert

et al. 2020

Lecture Notes in Computer Science

View full text Add to dashboard Cite

show abstract

“…Financial institutions are interested in ensuring that illicit operations, that is, fraudulent money transactions, are detected and prosecuted in short time. Fraudulent schemes have nowadays a huge impact on the financial system, impacting the economy and the trustworthiness of the institutions [KCA*16, LGM*18]. To tackle such incidents, financial institutions analyse on average millions of transactions (money movements) per year, the majority of which are legitimate, to detect possibly unlawful schemes and behaviours.…”

Section: Designing Guidance: Three Scenariosmentioning

confidence: 99%

Guide Me in Analysis: A Framework for Guidance Designers

Ceneda

Andrienko

et al. 2020

Computer Graphics Forum

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Guidance is an emerging topic in the field of visual analytics. Guidance can support users in pursuing their analytical goals more efficiently and help in making the analysis successful. However, it is not clear how guidance approaches should be designed and what specific factors should be considered for effective support. In this paper, we approach this problem from the perspective of guidance designers. We present a framework comprising requirements and a set of specific phases designers should go through when designing guidance for visual analytics. We relate this process with a set of quality criteria we aim to support with our framework, that are necessary for obtaining a suitable and effective guidance solution. To demonstrate the practical usability of our methodology, we apply our framework to the design of guidance in three analysis scenarios and a design walk‐through session. Moreover, we list the emerging challenges and report how the framework can be used to design guidance solutions that mitigate these issues.

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“…The importance of using the visual channel to identify and analyze economic and financial frauds is well described in the literature [41], [42]; see also the survey in [43]. Application examples of visualization approaches in the economic and financial fields include: systems for financial fraud and money laundering detection based on the analysis of banking data [11], [12], [44], [45]; visual analytics techniques for financial stability monitoring and fraud detection in financial markets [46], [47]; decision support systems for tax evasion discovery [9], [48].…”

Section: Visualization Approachesmentioning

confidence: 99%

Combining Network Visualization and Data Mining for Tax Risk Assessment

et al. 2020

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This paper presents a novel approach, called MALDIVE, to support tax administrations in the tax risk assessment for discovering tax evasion and tax avoidance. MALDIVE relies on a network model describing several kinds of relationships among taxpayers. Our approach suitably combines various data mining and visual analytics methods to support public officers in identifying risky taxpayers. MALDIVE consists of a 4-step pipeline: (i) A social network is built from the taxpayers data and several features of this network are extracted by computing both classical social network indexes and domain-specific indexes; (ii) an initial set of risky taxpayers is identified by applying machine learning algorithms; (iii) the set of risky taxpayers is possibly enlarged by means of an information diffusion strategy and the output is shown to the analyst through a network visualization system; (iv) a visual inspection of the network is performed by the analyst in order to validate and refine the set of risky taxpayers. We discuss the effectiveness of the MALDIVE approach through both quantitative analyses and case studies performed on real data in collaboration with the Italian Revenue Agency. INDEX TERMS Tax risk assessment, tax evasion discovery, network visualization, data mining, human-computer interaction.

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Visual analytics for event detection: Focusing on fraud

Cited by 42 publications

References 23 publications

Scenario-Based Requirements Elicitation for User-Centric Explainable AI

Scenario-Based Requirements Elicitation for User-Centric Explainable AI

Guide Me in Analysis: A Framework for Guidance Designers

Combining Network Visualization and Data Mining for Tax Risk Assessment

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