The system utilizes machine learning to flag abnormal spikes in price and volume, comparing these against historical data from past market abuse investigations. It specifically monitors for "racehorse" patterns, characterized by rapid short-term token appreciation, and "cage" scenarios, where prices surge while deposits or withdrawals are restricted. To identify potential wash trading or coordinated activity, the regulator integrates Benford’s Law with machine-learning models to isolate anomalous numerical datasets.
Beyond technical trading metrics, the platform uses generative AI to analyze news cycles and exchange announcements, distinguishing between verified network updates and unjustified volatility. The surveillance scope extends to online influence, as the system converts audio and text from YouTube, forums, and private chat rooms to identify front-running or coordinated campaigns intended to mislead retail traders. Although the AI generates comprehensive reports for human review, final decisions regarding formal investigations remain with FSS personnel. Future updates are expected to incorporate on-chain transaction tracking and cross-exchange fund-flow analysis.
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