Explore AI-ready traffic-quality analysis using verified quality and anomaly signals without presenting automated scoring as a complete fraud verdict.
Traffic-quality analysis looks for patterns that deserve filtering, review or investigation. Machine-assisted anomaly signals can support operations, but they do not replace policy, supply-chain transparency, measurement standards or human review.
Traffic-quality systems combine request, supply-path, viewability and interaction signals to identify patterns that deserve filtering, reconciliation or human review.
Each stage narrows the valid decision space before an outcome is measured and fed back into the next cycle.
Use request timing, placement, supply path, viewability, interaction and network patterns that can be verified.
Compare domain, seller, placement and traffic behavior for contradictions or unusual changes.
Filter or flag traffic only through defined rules, thresholds and review procedures.
Measure what was filtered, why it was filtered and how the decision affects reporting and partners.
Request, impression and click sequences that may indicate unusual automation or measurement behavior.
Whether domain, seller and supply-path information align with expected inventory.
Whether delivered impressions have plausible opportunity to be seen.
Unusual click or engagement patterns that merit further investigation.
AdExchange.bd uses AI-ready language here unless a specific automated model, data flow and production behavior have been implemented and verified. This page explains the architecture and decision framework without claiming unsupported live automation.
Combine ads.txt, sellers.json and request metadata to investigate inconsistent selling paths.
Prioritize suspicious patterns for review without treating an anomaly score as a fraud verdict.
Keep filtering reasons and volume visible so publisher and advertiser reporting can be explained.
No. Anomaly or risk scores are signals. They should be combined with standards, documented rules, measurement evidence and review.
They help describe authorized selling relationships and seller identities, which can support supply-path consistency checks.
