A Meta-Model for Real-Time Fraud Detection in ERP Systems

Date
2021-01-05
Authors
Fuchs, Anna
Fuchs, Kevin
Gwinner, Fabian
Winkelmann, Axel
Journal Title
Journal ISSN
Volume Title
Publisher
Volume
Number/Issue
Starting Page
7112
Ending Page
Alternative Title
Abstract
Fraud is a worldwide issue affecting almost every organization once in a time. Recent studies have shown that fraudulent behavior impacts up to 5 % of a companies annual revenue. Information systems (IS) have become an integral part of every modern organization. They contain the data foundation of the entire company and thereby supporting business processes and day-to-day transactions. Although an IS usually contains control mechanisms to prevent different kinds of fraud, these mechanisms look insufficient, considering the role of IS in many fraud cases. Since many cases from different companies have shown the need for an appropriate countermeasure, we want to develop an application that efficiently detects fraud and fraudulent behavior. Therefore, we conducted a structured literature review and a qualitative survey to apply the design science research (DSR) methodology and derive requirements for a fraud detection system (FDS). As a result, we present a meta-model for a FDS for enterprise resource planning (ERP) systems. We also provide application requirements, principles, and features that define areas for further research.
Description
Keywords
Machine Learning and Cyber Threat Intelligence and Analytics, design science research, enterprise resource planning, erp system, fraud detection, meta-model
Citation
Extent
10 pages
Format
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Time Period
Related To
Proceedings of the 54th Hawaii International Conference on System Sciences
Rights
Attribution-NonCommercial-NoDerivatives 4.0 International
Rights Holder
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