Fake reviews are distinguishable by patterns that are hard to spot manually when you are reviewing a live feed of incoming reviews but become obvious in aggregate: multiple reviews posted on the same day, reviews from accounts with no history, reviews using language inconsistent with genuine customers, or a sudden volume spike on a competitor platform. The problem is that the manual process of identifying these reviews, compiling evidence, and navigating each platform's reporting process is slow and resource-intensive. Deploy FeedbackRobot's review monitoring automation and the pattern detection runs continuously in the background. Reviews that match the signals associated with fraudulent activity are flagged automatically. The flag includes the review content, the account details, the posting timestamp relative to other recent reviews, and a summary of the suspicious indicators. Your team receives a notification with everything needed to evaluate the flag and initiate a platform report without starting from scratch. For businesses that are targets of coordinated negative review campaigns, the monitoring layer is the difference between catching an attack within hours and discovering the damage after it has already affected your rating. The platform reporting process is initiated from within FeedbackRobot, with the evidence structured in the format each platform requires, reducing the appeal process from hours of manual work to a review and submit.