Analyzing Data Sets for ML-driven Fraud Detection in SAP Systems
Author:
Affiliation:
1. University of Applied Sciences Karlsruhe,Faculty of Management Science and Engineering,Karlsruhe,Germany
2. SIVIS GmbH,Karlsruhe,Germany
Funder
Ministry of Education
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10385234/10386078/10386379.pdf?arnumber=10386379
Reference46 articles.
1. Occupational Fraud 2022 - A Report to the Nations,2022
2. Report to the Nations - 2020 Global Study on Occupational Fraud and Abuse,2020
3. Report to the Nations - 2018 Global Study on Occupational Fraud and Abuse,2018
4. Using Process Mining to Identify Fraud in the Purchase-to-Pay Process;Baader;Hasso-Plattner-Institut,2015
5. Specification and Implementation of a Data Generator to Simulate Fraudulent User Behavior
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