Antifraud, score & MMM

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Evidence before decision

✓

Anti-fraud

Signs observed

CTIT

extreme ranges and distribution by channel.

Replay

click ID duplicated or reused in multiple sessions.

Conflict

Incompatible native IDs in the same click.

Automation

burst, cadence and incoherence of interaction.

Journey

active time, PDP, search and funnel consented.

Integrity

separate low-quality collection failures.

Score

Components, not black boxes

trust_score = f(
  attribution_confidence,
  traffic_quality,
  measurement_integrity,
  fraud_risk,
  score_coverage,
  model_version
)

The components and reason codes are the operational decision. The aggregate number is a versioned summary.

Responsible use

Operating rules

Situation	Action
high score + high coverage	maintain monitoring; do not assume causality
low score + high fraud	investigate evidence and partner before blocking
low score + low integrity	correct collection; do not penalize channel
behavioral unavailable	use only technical risk
version change	compare shadow and recalibrate baseline

MMM

From quality to model

The MMM works on a channel × day/week × geography basis. Use trusted installs, fraud rate, score coverage, physical signs, investment and external controls. Perform adstock, saturation, backtesting and uncertainty intervals.

Do not multiply income by trust score. Use quality as a feature, observation weight or sensitivity analysis and document the specification.