  Recent estimates report that companies lose 5% of their revenue to
occupational fraud. Since most medium-sized and large companies employ
Enterprise Resource Planning (ERP) systems to track vast amounts of information
regarding their business process, researchers have in the past shown interest
in automatically detecting fraud through ERP system data. Current research in
this area, however, is hindered by the fact that ERP system data is not
publicly available for the development and comparison of fraud detection
methods. We therefore endeavour to generate public ERP system data that
includes both normal business operation and fraud. We propose a strategy for
generating ERP system data through a serious game, model a variety of fraud
scenarios in cooperation with auditing experts, and generate data from a
simulated make-to-stock production company with multiple research participants.
We aggregate the generated data into ready to used datasets for fraud detection
in ERP systems, and supply both the raw and aggregated data to the general
public to allow for open development and comparison of fraud detection
approaches on ERP system data.
