Chainalysis Chief Scientist writes about a customer report where two analytics tools gave conflicting labels for the same address: one flagged it as a gambling service, the other as CSAM (child sexual abuse material). This dangerous error occurred because statistical pattern matching confused small, regular payments from a gambler with similarly frequent payments linked to illegal content. The incident underscores the risk of relying on opaque, low-evidence attributions in blockchain analytics. The author, with a background in formal verification of distributed systems, explains how Chainalysis applies academic rigor: deterministic, reproducible, and auditable methods for structural analysis (e.g., linking addresses under common control) and a structured confidence framework for attribution (e.g., naming an entity). The ontology paper defines these tiers, provides vocabulary for accountability, and invites industry-wide adoption. The author emphasizes that trust in blockchain data is vital for law enforcement, compliance, and courts; therefore, standards must be published and open to peer review, as was validated in the Sterlingov case and an independent Delft study.

Key facts

• Two analytics tools labeled same address gambling and CSAM, showing pattern-matching flaws.
• Small, regular payments can mimic each other, leading to dangerous false positives.
• Chainalysis ontology defines deterministic structural analysis vs. confidence-based attribution.
• Methods withstand Daubert scrutiny and independent peer review studies.
• Paper calls for industry-wide adoption of transparent, rigorous standards.