critical-validation

To validate contract analysis anomalies before reporting, you must check each finding for data artifacts like NULL ICOs or duplicate entries and cross-reference against innocent explanations like legitimate freelancer payments, assigning a status of CONFIRMED, DISMISSED, or INCONCLUSIVE.

Dismissing false positives in risk assessment workflows involves a multi-step validation process that identifies data quality problems such as nonsensical dates and checks for non-suspicious patterns like grants or large infrastructure projects before classifying the finding.

To check if a flagged contract splitting anomaly is a data artifact, verify whether the data contains missing ICOs, duplicate entries, or if it represents a legitimate grouping of freelancers rather than a confirmed suspicious issue.

Yes, anomaly detection validation applies to post-query workflows by filtering out issues caused by data quality problems like NULL ICOs, ensuring that only verified anomalies with no innocent explanations are escalated as confirmed findings.

Without checking innocent explanations, validating investigative findings risks reporting false positives, as legitimate patterns such as grants, large infrastructure projects, or freelancer payments may be incorrectly classified as confirmed anomalies.

Your risk assessment flags legitimate infrastructure projects as anomalies because the data lacks initial innocent explanation validation, which is required to cross-reference findings with common non-suspicious patterns before assigning a confirmed status.