Previous works on ERP system fraud detection may be divided into approaches that rely on entirely private **data** and frauds, private **data** with synthetically injected frauds, or entirely synthetic **data** and frauds. While there have been works that use real ERP system **data** to develop and evaluate fraud detection systems [18,17,12], details about the **data** and the **data** itself are kept under wraps to avoid revealing company trade secrets and privacy information. For scenarios where real frauds are not available, Islam et al. [8] generate synthetic fraud cases within private ERP system **data** through randomly creating changes to normal transactions while limiting changes and timings with given intervals. While this generates anomalous transactions through not yet observed peaks in single entries, the generated anomalies have no inherent meaning or interpretation with respect to real-life occurrences or fraud.