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

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Title:A Meta-Model for Real-Time Fraud Detection in ERP Systems
Authors:Fuchs, Anna
Fuchs, Kevin
Gwinner, Fabian
Winkelmann, Axel
Keywords:Machine Learning and Cyber Threat Intelligence and Analytics
design science research
enterprise resource planning
erp system
fraud detection
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Date Issued:05 Jan 2021
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.
Pages/Duration:10 pages
Rights:Attribution-NonCommercial-NoDerivatives 4.0 International
Appears in Collections: Machine Learning and Cyber Threat Intelligence and Analytics

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