Taxonomy of Fraud Detection Metrics for Business Processes Article Swipe
Badr Omair
,
Ahmad Alturki
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1109/access.2020.2987337
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1109/access.2020.2987337
A business process is a set of connected events, activities, and decision points, including actors and objects, which collectively produce a beneficial outcome for the customer. The success of an organization's strategic goals and performance depends on how well these business processes are implemented and executed. However, process-based fraud (PBF), a type of fraud that occurs in business processes, can be an obstacle to achieving this. Literature analysis shows that to date PBF detection metrics have not been sufficiently addressed. Specifically, there is overlap, confusion, and no standard for fraud definitions and categories that can affect our understanding of fraud mechanisms. This study develops a taxonomy to expose the dimensions, characteristics, and objects of PBF detection and to determine their relationships by using the design science research methodology. The developed taxonomy identifies four PBF dimensions with the following characteristics: (1) process perspective {time, function, data, resource, and location}, (2) presentation layer {process map, process stream, process model, process instance, and process activity}, (3) fraud data scheme {anomalous, discrepant, missing, and wrong}, and (4) fraud domain {generic and specific}. The objective of this taxonomy is to offer a useful tool to anyone seeking to classify, develop, and evaluate PBF detection metrics, along with a holistic view of PBF detection and the determination of its borders. Additionally, it may help in standardizing the concepts of PBF detection metrics to ensure consistency between stakeholders.
Related Topics
Concepts
Computer science
Taxonomy (biology)
Process (computing)
Consistency (knowledge bases)
Business process
Data science
Obstacle
Set (abstract data type)
Process management
Knowledge management
Work in process
Artificial intelligence
Business
Operating system
Political science
Law
Botany
Programming language
Biology
Marketing
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/access.2020.2987337
- https://ieeexplore.ieee.org/ielx7/6287639/8948470/09064571.pdf
- OA Status
- gold
- Cited By
- 5
- References
- 63
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3016945566
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3016945566Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1109/access.2020.2987337Digital Object Identifier
- Title
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Taxonomy of Fraud Detection Metrics for Business ProcessesWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2020Year of publication
- Publication date
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2020-01-01Full publication date if available
- Authors
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Badr Omair, Ahmad AlturkiList of authors in order
- Landing page
-
https://doi.org/10.1109/access.2020.2987337Publisher landing page
- PDF URL
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https://ieeexplore.ieee.org/ielx7/6287639/8948470/09064571.pdfDirect link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://ieeexplore.ieee.org/ielx7/6287639/8948470/09064571.pdfDirect OA link when available
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Computer science, Taxonomy (biology), Process (computing), Consistency (knowledge bases), Business process, Data science, Obstacle, Set (abstract data type), Process management, Knowledge management, Work in process, Artificial intelligence, Business, Operating system, Political science, Law, Botany, Programming language, Biology, MarketingTop concepts (fields/topics) attached by OpenAlex
- Cited by
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5Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1, 2021: 2, 2020: 2Per-year citation counts (last 5 years)
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63Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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