This audit proves the synthetic origin of 2025 Bitcoin scam lures. Instead of just searching for “scammy” keywords, we are identifying the “robotic fingerprints” left behind by Large Language Models (LLMs).

This project replaces traditional, static audit schedules with a Predictive Oversight model. By utilizing Random Forest machine learning, the analysis identifies the mathematical "Risk Signatures" that actually lead to failures—specifically isolating Systemic Risk Factors as the primary driver over simple asset value. The resulting framework segments operations into three strategic tiers, allowing the organization to stop wasting resources on stable units and instead concentrate 70% of audit capacity on high-risk clusters where failure is statistically most likely to escalate.

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