Ad Fraud Detection
“Is this impression from a bot?”
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A real-world example
Is this impression from a bot?
Ad fraud costs the industry $84B annually. Rule-based filters catch known patterns but miss sophisticated bot networks that mimic human behavior. These bots share IP ranges, rotate device fingerprints, and generate realistic click patterns that pass individual-level checks. For an ad network processing $2B in spend, a 10% fraud rate means $200M lost to bots.
How KumoRFM solves this
Graph-powered intelligence for advertising
Kumo builds a graph connecting impressions, devices, IPs, publishers, and click patterns. Bot networks that appear legitimate in isolation form conspicuous clusters in the graph: shared IP subnets, correlated click timing, device fingerprint cycling, and abnormal publisher concentration. The GNN detects these structural anomalies without hand-crafted rules, adapting as fraud tactics evolve.
From data to predictions
See the full pipeline in action
Connect your tables, write a PQL query, and get predictions with built-in explainability — all in minutes, not months.
Your data
The relational tables Kumo learns from
IMPRESSIONS
| impression_id | device_id | ip_address | publisher_id | timestamp |
|---|---|---|---|---|
| IMP801 | DEV001 | 192.168.1.50 | PUB01 | 2025-03-01 02:14 |
| IMP802 | DEV002 | 192.168.1.51 | PUB01 | 2025-03-01 02:14 |
| IMP803 | DEV003 | 10.0.0.88 | PUB02 | 2025-03-01 09:30 |
DEVICES
| device_id | device_type | os | fingerprint_hash |
|---|---|---|---|
| DEV001 | Mobile | Android | FP-AA1 |
| DEV002 | Mobile | Android | FP-AA2 |
| DEV003 | Desktop | Windows | FP-BB1 |
IPS
| ip_address | asn | geo | datacenter |
|---|---|---|---|
| 192.168.1.50 | AS12345 | US-East | True |
| 192.168.1.51 | AS12345 | US-East | True |
| 10.0.0.88 | AS67890 | US-West | False |
PUBLISHERS
| publisher_id | name | category | fraud_history_rate |
|---|---|---|---|
| PUB01 | QuickClicks | News | 12.4% |
| PUB02 | TechReview | Technology | 0.8% |
CLICK_PATTERNS
| device_id | clicks_last_hour | avg_time_between_clicks | unique_ads |
|---|---|---|---|
| DEV001 | 147 | 0.4s | 3 |
| DEV002 | 132 | 0.5s | 3 |
| DEV003 | 4 | 45s | 4 |
Write your PQL query
Describe what to predict in 2–3 lines — Kumo handles the rest
PREDICT BOOL(IMPRESSIONS.is_fraud, 0, 1, hours) FOR EACH IMPRESSIONS.impression_id
Prediction output
Every entity gets a score, updated continuously
| IMPRESSION_ID | DEVICE_ID | FRAUD_PROB | VERDICT |
|---|---|---|---|
| IMP801 | DEV001 | 0.96 | Fraud |
| IMP802 | DEV002 | 0.94 | Fraud |
| IMP803 | DEV003 | 0.03 | Legitimate |
Understand why
Every prediction includes feature attributions — no black boxes
Impression IMP801 -- Device DEV001
Predicted: 96% fraud probability
Top contributing features
IP subnet cluster size
47 devices on /24
31% attribution
Click velocity (last hour)
147 clicks
26% attribution
Datacenter IP flag
True
20% attribution
Publisher historical fraud rate
12.4%
14% attribution
Device fingerprint rotation frequency
3 per hour
9% attribution
Feature attributions are computed automatically for every prediction. No separate tooling required. Learn more about Kumo explainability
PQL Documentation
Learn the Predictive Query Language — SQL-like syntax for defining any prediction task in 2–3 lines.
Python SDK
Integrate Kumo predictions into your pipelines. Train, evaluate, and deploy models programmatically.
Explainability Docs
Understand feature attributions, model evaluation metrics, and how to build trust with stakeholders.
Bottom line: An ad network processing $2B in annual spend recovers $120-160M by catching sophisticated bot networks that rule-based systems miss. Kumo's graph reveals coordinated fraud clusters across devices, IPs, and publishers that appear legitimate in isolation.
Related use cases
Explore more ad tech use cases
Topics covered
One Platform. One Model. Predict Instantly.
KumoRFM
Relational Foundation Model
Turn structured relational data into predictions in seconds. KumoRFM delivers zero-shot predictions that rival months of traditional data science. No training, feature engineering, or infrastructure required. Just connect your data and start predicting.
For critical use cases, fine-tune KumoRFM on your data using the Kumo platform and Data Science Agent for 30%+ higher accuracy than traditional models.
Book a demo and get a free trial of the full platform: data science agent, fine-tune capabilities, and forward-deployed engineer support.
