Fraud Score

What Is Fraud Score

A Fraud Score is a numerical risk rating assigned to an advertising interaction—such as an impression, click, install, or conversion—that estimates the probability of the interaction being fraudulent or invalid. Rather than classifying traffic as simply “valid” or “invalid,” modern anti-fraud platforms calculate a score that reflects the overall level of risk based on multiple technical and behavioral signals.

Fraud Scores are generated by evaluating hundreds of indicators, including device characteristics, IP reputation, user behavior, attribution data, network anomalies, and historical fraud patterns. The higher the score, the greater the likelihood that the interaction represents invalid traffic.

Risk scoring enables advertisers to automate fraud prevention while maintaining flexibility in how traffic is filtered and evaluated.

How Fraud Score Works

Fraud scoring engines analyze every advertising interaction using multiple detection layers.

Typical inputs include:

• Device Fingerprinting and Device Intelligence.
• IP reputation and network quality.
• Behavioral analysis.
• Browser and operating system consistency.
• Attribution validation.
• Historical fraud patterns.
• Machine learning models and anomaly detection.

Each detected anomaly contributes to the overall risk score. Depending on predefined thresholds, the interaction may be accepted, flagged for investigation, or automatically blocked.

Why It Matters for Your Campaigns

Fraud Scores allow advertisers to make smarter decisions than simple allow-or-block rules.

For businesses, they help:

• Prioritize high-risk traffic.
• Automate fraud prevention workflows.
• Reduce false positives.
• Improve campaign optimization.
• Protect advertising budgets.
• Increase attribution accuracy.
• Make risk-based decisions across acquisition channels.

Instead of treating every suspicious interaction equally, advertisers can apply different actions based on the calculated level of fraud risk.

How to Use Fraud Scores Effectively

Fraud Scores are most effective when combined with a broader fraud prevention strategy.

Recommended best practices include:

• Evaluate every advertising interaction in real time.
• Define risk thresholds based on campaign objectives.
• Combine Fraud Scores with behavioral analysis and Device Intelligence.
• Continuously retrain scoring models using new fraud patterns.
• Monitor score distributions across traffic sources.
• Use automated rules for blocking or reviewing high-risk traffic.
• Integrate fraud scoring into campaign optimization and reporting workflows.

Combining machine learning, behavioral analytics, and multi-layer fraud detection allows Fraud Scores to provide a reliable foundation for automated advertising fraud prevention.