Abstract: In some implementations, a computing device can be configured to perform impact-based fraud detection. For example, a computing device (e.g., a network server) can receive user submissions from user devices corresponding to traffic incident reports, point of interest (POI) ratings, product ratings, vendor ratings, and/or other crowd-sourced information. The computing device can identify groups of users based on the user submissions and various grouping criteria. The computing device can determine the impact of these user groups with respect to the targets (e.g., traffic in an area, ratings of a business, etc.) of their user submissions. The computing device can prioritize high impact user groups when attempting to detect fraudulent user group activity (e.g., fraud rings).

Abstract: In some implementations, a computing device can be configured to perform impact-based fraud detection. For example, a computing device (e.g., a network server) can receive user submissions from user devices corresponding to traffic incident reports, point of interest (POI) ratings, product ratings, vendor ratings, and/or other crowd-sourced information. The computing device can identify groups of users based on the user submissions and various grouping criteria. The computing device can determine the impact of these user groups with respect to the targets (e.g., traffic in an area, ratings of a business, etc.) of their user submissions. The computing device can prioritize high impact user groups when attempting to detect fraudulent user group activity (e.g., fraud rings).