AI-powered fraud intelligence infrastructure

Stop Fraud Before It Wastes Spend and Pollutes Attribution

Detect invalid traffic, fake leads, bot clicks, suspicious installs, duplicate conversions, and low-quality sources before they corrupt CAC, ROAS, audiences, lifecycle journeys, and revenue reporting.

Invalid traffic, GIVT, and SIVT quality checks
Fraud-adjusted attribution and clean ROAS
Clean Growth Signal scoring
Audience, journey, and CRM suppression
Live fraud cockpit
Source 14 contained
94 risk
2.9m
scored
18.7k
blocked
3.9x
clean ROAS
1
Detect
Click spam + fake leads flagged
2
Decide
Suppress cohort before sync
3
Protect
$48k spend exposure protected
The fraud problem

Fraud Turns Growth Data Into Expensive Fiction

When fraudulent traffic enters your growth stack, attribution, ROAS, audiences, journeys, CRM, and revenue reports start optimizing around signals that should never have been trusted.

app.drivemetadata.com / fraud/disconnected-growth-stack
Ad platforms reporting inflated conversions
Polluted signal · unreliable growth logic
Bots generating fake clicks
Polluted signal · unreliable growth logic
Click flooding polluting attribution
Polluted signal · unreliable growth logic
Install hijacking stealing credit
Polluted signal · unreliable growth logic
Fake leads entering CRM
Polluted signal · unreliable growth logic
Invalid traffic entering CDP audiences
Polluted signal · unreliable growth logic
Journeys targeting low-quality users
Polluted signal · unreliable growth logic
Finance seeing mismatched revenue
Polluted signal · unreliable growth logic
Campaign teams optimizing polluted ROAS
Polluted signal · unreliable growth logic
Verified fraud intelligence layer
Clean Growth Signals routed to attribution, audiences, journeys, analytics, warehouses, and finance reporting.
Wasted media spend
Unreliable attribution
Inflated ROAS
Polluted audiences
Poor retention targeting
Fake lead volume
Misleading campaign decisions
Disconnected fraud reporting
Search answer

What Is Ad Fraud Detection Software?

Ad fraud detection software identifies invalid traffic, bot clicks, fake leads, suspicious installs, duplicate conversions, and low-quality acquisition activity before it wastes spend or distorts attribution. DriveMetaData connects fraud detection to customer identity, attribution, audiences, journeys, analytics, and revenue reporting so growth teams optimize from clean performance data.

See how clean signals improve measurement
Detect

Find IVT, fake leads, click spam, install fraud, and duplicate conversions.

Score

Rank source, campaign, partner, identity, and conversion quality.

Suppress

Stop risky users from entering audiences, journeys, CRM, and activation.

Reconcile

Report clean CAC, ROAS, LTV, payback, and revenue truth.

Real-time decision engine

Turn Every Event Into a Decision Before It Becomes a Metric

Instead of listing raw clicks, leads, installs, and purchases, DriveMetaData converts each signal into a trust decision: use it, review it, or suppress it before attribution, audiences, journeys, analytics, and finance ever see it.

app.drivemetadata.com / fraud/realtime-risk-pipeline
Signal arrives
`purchase_completed` from paid cohort
live
SourceTikTok partner 14
Identitydevice + CRM match
Value$184 revenue event
Contextfirst purchase · paid install
Trust model
Five checks decide where the event can go
22ms
decision
Traffic quality96 score
IVT · bots · click spam
Source quality88 score
partner · geo · placement
Identity confidence91 score
device · CRM · duplicate
Attribution trust84 score
credit · postback · timing
Revenue quality79 score
CAC · ROAS · payback
Trust
Route to attribution, CDP, analytics, and finance
Review
Hold for partner, source, or lead-quality inspection
Suppress
Exclude from audiences, journeys, CRM, and budget logic
99.9%
clean signal routing
live
suppression updates
1 view
measurement + finance
Decision output
This event is trusted for attribution and finance, but excluded from lookalike seed audiences until retention quality is known.
trust + limit activation
Paid media signals
Score before attribution
Source quality + click velocity + placement risk
Mobile acquisition
Validate before MMP sync
Install integrity + device risk + post-attribution quality
Web and app behavior
Verify before cohorting
Human activity + identity consistency + event plausibility
Lead and CRM flow
Route before sales action
Fake lead risk + duplicate profile + consent eligibility
Revenue events
Reconcile before finance view
Conversion quality + revenue match + payback confidence
Server-side signals
Audit before activation
Schema validity + source trust + suppression state
Mobile ad fraud

Protect Mobile Attribution From Install and Event Fraud

Detect mobile-specific fraud patterns before they distort install attribution, in-app event reporting, re-engagement performance, and mobile ROAS.

Click spamClick floodingInstall hijackingSDK spoofingDevice farmsBot installs+4 more
app.drivemetadata.com / mobile-attribution/fraud-quality
91%
clean installs
12.4k
blocked clicks
-28%
fraud-adjusted CAC
app.drivemetadata.com / traffic-quality/invalid-traffic-filter
Bot traffic
risk 2
blocked before reporting83%
General invalid traffic
risk 3
blocked before reporting79%
Sophisticated invalid traffic
risk 4
blocked before reporting75%
Automated clicks
risk 5
blocked before reporting71%
Non-human activity
risk 6
blocked before reporting67%
Proxy and VPN anomalies
risk 7
blocked before reporting63%
Abnormal click velocity
risk 8
blocked before reporting59%
Suspicious geo/device patterns
risk 9
blocked before reporting55%
Traffic quality

Filter Invalid Traffic Before It Reaches Reporting

Identify general invalid traffic, sophisticated invalid traffic, bot behavior, and suspicious source patterns before they enter reporting, optimization, audiences, CRM, and lifecycle systems.

Clean Growth Signal methodology

Verify Media Quality, Identity, Attribution, and Revenue Quality Together

DoubleVerify and IAS help buyers think about media quality. DriveMetaData extends that logic into the full growth stack by turning clean traffic, trusted source quality, identity confidence, trusted campaign credit, and revenue quality into one action-ready growth signal.

app.drivemetadata.com / fraud/clean-growth-signal-methodology
Clean Growth Signal
Raw event quality checked before reporting or activation
94 confidence
Human traffic1
GIVT/SIVT and bot patterns excluded
Valid source2
Partner, placement, geo, and campaign quality scored
Identity confidence3
Device, CRM, and customer signals checked together
Trusted campaign credit4
Duplicate and suspicious conversion credit removed
Audience eligibility5
Risky cohorts held back from activation
Revenue quality6
CAC, ROAS, LTV, and payback measured on clean signals
Media quality

IVT, GIVT, SIVT, viewability context, geo quality, and source accountability.

Growth quality

Identity confidence, trusted campaign credit, audience eligibility, and LTV quality.

Revenue quality

Fraud-adjusted CAC, clean ROAS, payback, finance reporting, and budget actions.

AI fraud strategist

See the Evidence, the Business Impact, and the Next Best Action

DriveMetaData AI does not stop at flagging fraud. It explains why a source is risky, shows the revenue impact, recommends what to suppress or reallocate, and keeps the action connected to attribution, audiences, journeys, and finance reporting.

Pattern engine
Impact model
Action planner
app.drivemetadata.com / ai-fraud/cockpit
AI priority recommendation

Source 14 is creating volume without customer quality.

Expected impact
-18% CAC risk
$48k spend exposure protected
Evidence graph
Signals used to explain the recommendation
confidence 94
Click velocity
High
4.8x above cohort baseline
Device repetition
High
312 installs share repeated identifiers
Retention quality
Medium
D3 retained users down 42%
Revenue quality
High
Clean ROAS falls from 4.8x to 3.1x
Action plan
Ranked by impact, confidence, and urgency
1
Suppress
2
Reallocate
3
Review
4
Reconcile
Fraud-adjusted measurement

Connect Fraud Detection to Attribution, ROAS, and Revenue Truth

DriveMetaData Fraud Detection is stronger than standalone fraud tools because fraud intelligence connects directly to measurement, mobile attribution, CDP, journey builder, analytics, campaign intelligence, and data pipelines.

MeasurementMobile AttributionCDPJourney BuilderAnalytics+2 connected systems
app.drivemetadata.com / measurement/fraud-adjusted-revenue-truth
Reported ROAS4.8x
Clean ROAS3.9x
CAC correction-18%
Fraud-adjusted attribution
Clean ROAS reporting
CAC correction
LTV quality scoring
Retention impact analysis
Channel quality reporting
Finance-ready revenue protection
Raw event audit trails
app.drivemetadata.com / audiences/journey-protection
Fraud-score audience suppression
Low-quality source exclusion
Duplicate profile prevention
Suspicious lead suppression
Journey eligibility filters
CRM quality routing
Activation suppression rules
Clean audience sync
Audience and journey protection

Keep Fraud Out of Audiences and Customer Journeys

Prevent polluted signals from becoming customer experiences by applying fraud scores before CDP audience sync, CRM routing, journey eligibility, and activation exports.

Media quality to revenue quality

Go Beyond Verification and Connect Fraud-Free Signals to Growth Outcomes

Monitor invalid traffic, human activity, viewability context, brand suitability, in-geo quality, and source accountability, then connect those signals to attribution, audiences, CAC, ROAS, LTV, and finance reporting.

Human traffic verification
GIVT and SIVT classification
Channel quality scoring
Placement risk monitoring
Brand safety signals
Brand suitability rules
Viewability context
In-geo quality checks
CTV/video risk scoring
Social traffic quality
Partner accountability
Revenue quality connection
app.drivemetadata.com / media-quality/placement-risk
Human traffic96%
GIVT/SIVT filterclean
Viewability82%
Brand suitability94%
In-geo quality89%
CTV risklow
Social quality87%
Placement risk12%
Revenue quality3.9x
Enterprise infrastructure

Fraud Intelligence Infrastructure Built for Scale

Run fraud detection on low-latency infrastructure designed to score streaming events, enforce suppression rules, sync clean signals, export raw audit trails, and support enterprise governance.

Streaming event scoring
Layer 1
Low-latency risk engine
Layer 2
Clean Growth Signal scoring
Layer 3
API-first architecture
Layer 4
Webhook enforcement
Layer 5
event streaming pipelines
Layer 6
ClickHouse analytics
Layer 7
Warehouse sync
Layer 8
Raw event exports
Layer 9
Audit trails
Layer 10
Enterprise governance
Layer 11
Integrations

Connect Fraud Intelligence Across Your Growth Stack

Send fraud intelligence, clean signals, risk scores, suppression rules, partner postbacks, and raw events across ad platforms, attribution systems, CRM, warehouses, analytics, and activation tools.

Search ad platforms, MMPs, warehouses, CRMs, APIs, and destinations
Meta
Google Ads
TikTok
Apple Search Ads
Snapchat
AppLovin
Liftoff
Moloco
Shopify
Salesforce
HubSpot
ClickHouse
AppsFlyer
Adjust
Kochava
Branch
Braze
MoEngage
WebEngage
Segment
Protected growth

Turn Fraud Detection Into Protected Growth

Give marketing, product, finance, and data teams one trusted fraud-adjusted view of spend quality, attribution quality, source quality, audience cleanliness, and revenue confidence.

-34%
Reduced wasted spend
+41%
Cleaner attribution
+29%
Improved Verified ROAS
-46%
Lower fake lead volume
+32%
Cleaner source quality
1 clean sync
Cleaner audience activation
100%
Improved finance alignment
2.8x
Faster fraud response
Fraud detection FAQ

Questions Growth Teams Ask Before Evaluating Fraud Detection

Short answers for marketers, analytics teams, media buyers, and finance stakeholders comparing DriveMetaData with MMP fraud tools, ad verification products, CDPs, and internal BI workflows.

What is DriveMetaData Fraud Detection?+

DriveMetaData Fraud Detection identifies invalid traffic, fake leads, bot clicks, suspicious installs, duplicate conversions, and low-quality acquisition sources before they corrupt attribution, audiences, journeys, analytics, and revenue reporting.

How does DriveMetaData improve Verified ROAS?+

DriveMetaData compares raw platform-reported performance against fraud risk, source quality, identity confidence, conversion plausibility, and revenue quality so teams can evaluate clean ROAS instead of inflated campaign numbers.

What is a Clean Growth Signal?+

A Clean Growth Signal is an acquisition, conversion, or customer event that passes traffic quality, source quality, identity confidence, trusted campaign credit, audience eligibility, and revenue-quality checks before it is used for reporting or activation.

How is DriveMetaData different from DoubleVerify and IAS?+

DoubleVerify and Integral Ad Science focus strongly on media quality, invalid traffic, viewability, brand suitability, and ad verification. DriveMetaData complements that category by connecting fraud-free and source-quality signals to attribution, CDP audiences, journeys, CRM routing, CAC, ROAS, LTV, and finance-ready revenue truth.

Can DriveMetaData suppress fraudulent audiences?+

Yes. Fraud scores and source-quality signals can be used to exclude risky users, duplicate profiles, fake leads, and low-quality cohorts before CDP sync, journey eligibility, CRM routing, advertising activation, and webhook actions.

Stop Scaling Growth on Polluted Signals.

DriveMetaData gives growth, marketing, product, finance, and data teams one AI-powered fraud intelligence foundation for invalid traffic detection, Clean Growth Signal scoring, fraud-adjusted measurement, clean audiences, journey protection, and revenue-trusted optimization.