Digital Asset Claims AI Investigation Tooling
AI tooling surfaces candidates for review — patterns in transaction data, text or metadata that would take an analyst far longer to find by hand. It is a triage and acceleration layer, never a decision-making one.
Where Machine Assistance Is Applied
Transaction pattern detection
Structuring, layering and fan-out behaviour is flagged where a sequence resembles a known obfuscation shape, ranked by similarity for individual inspection.
Entity and text matching
Language matching surfaces likely name, alias and entity matches across document sets and public records too large to search manually.
Anomaly flagging
Outlier detection is applied to wallet activity and infrastructure data, routing deviations from an established baseline to a named analyst.
The Review Gate Before A Finding
No model output enters a case report until an analyst has examined the underlying evidence independently of the flag and reached the same conclusion from primary sources.
Where the analyst cannot reproduce the result from source records, the flag is retained as an investigative lead and recorded as unverified.
Limitations
Models produce false positives, miss context available only to a human reviewer, and reflect the limits of their inputs. No model output is presented as a standalone finding.