As an alternative that uses no private data, business researchers have in the past moved to developing **data** generators that are capable of generating both normal operation and frauds: Yannikos et al. [22] introduce 3LSPG, a generator that produces synthetic ERP **data** through a probabilistic approach using discrete time Markov chains. While the resulting **data** can mimic the transactions taken from an ERP system, the data's quality and realism are strongly dependent on the expert knowledge put into the simulation. With no data, code, and chosen simulation parameters available, modeling realistic ERP system **data** through this approach is challenging. Similarly, game based approaches may be used to model business processes with player interaction [21]. While this is a promising aspect for **data** generation, expert knowledge is required to ensure that the complex behavior of real ERP systems are mimicked in the resulting data.