Prediction markets are a common instrument in forecasting and corporate knowledge management. Based on the “wisdom of the crowd” its forecasts regularly outperform polls as well as statistical models. In addition, it offers a convenient way to collect dispersed information in organizations and incite employees to reveal private information as well as to stay informed. Although such markets are well established, there still remain open questions regarding their operation and maintenance. Especially the issue of manipulation and fraud, which are reported in many cases, is only rarely addressed; if so, only very theoretical or with complex algorithms, hard to implement for practitioners. Yet, a rigid framework, uncovering weaknesses of prediction markets and offering applicable prevention and detection strategies is missing. We propose the Fraud Cube, a concise framework unveiling fraudster’s thought process and thus potential attack vectors. Additionally, we present an easy to implement detection algorithm based on state of the art detection heuristics. Finally, we show not less than comparable detection rates to established detection algorithms whilst providing superior applicability.

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