In this paper, we address these problems by extending the work of Baader and Krcmar [3] in multiple ways: We first extend the requirements for synthetic ERP **data** layed out by Baader et al. [4] to include further requirements of ERP fraud detection approaches [7]. We secondly propose to use an established serious game [9] to simulate not only fraudulent scenarios but also normal operation of a make-to-stock production company through user interaction in a real ERP system, generating both normal and fraudulent behavior simultaneously. Additionally, this allows us to extend the business processes investigated by previous **data** generators from the purchase-to-pay (P2P) process to modeling normal and fraudulent activity in the well established order-to-cash (O2C) process as well. Based on our extended requirements, we then design multiple fraud scenarios in cooperation with auditing experts. We conduct multiple runs of our proposed **data** generation scheme and produce ERP system **data** of multiple fiscal years of operation, extracting raw **data** from the ERP system. The resulting **data** contains many multi-relational tables that offer different views on the recorded company's business process. Since many fraud detection approaches require single tables to operate, we additionally create ready to use datasets from a subset of our multi-relational **data** that can be directly used for measuring and comparing the performance of fraud detection systems. We further extend these datasets by providing expert-created annotations for fraud cases that highlight the problematic entries of individual frauds for use in debugging and assessing performance of algorithms that focus on the detection of anomalous entries specifically.