Advertisers often think of antifraud as binary: on or off, protected or exposed. The reality is far more nuanced. Every antifraud decision involves tradeoffs between catching fraud and losing legitimate traffic.

The False Positive Problem

Antifraud systems make two types of errors:

False Negatives (Missed Fraud)

Fraud that slips through detection:

Sophisticated bots that mimic human behavior
Residential proxies that look like real users
Click farms using real devices
New fraud techniques not yet fingerprinted

False Positives (Blocked Legitimate Traffic)

Real users incorrectly flagged as fraud:

Privacy-conscious users on VPNs
Corporate users behind proxies
Users with unusual but legitimate behavior
New devices or browsers with thin history

The Sensitivity Dial

Imagine antifraud sensitivity as a dial from 1-10:

Low Sensitivity (1-3)

Blocks: Only obvious, certain fraud
Misses: Sophisticated fraud gets through
False positives: Very few legitimate users blocked
Use case: Maximize reach, accept some fraud cost

Medium Sensitivity (4-6)

Blocks: Most common fraud patterns
Misses: Some sophisticated attacks
False positives: Some VPN users, edge cases blocked
Use case: Balanced approach for most campaigns

High Sensitivity (7-10)

Blocks: Anything suspicious
Misses: Very little gets through
False positives: Many legitimate users blocked
Use case: High-value conversions where quality is critical

The Business Tradeoff

Choosing sensitivity is a business decision, not a technical one:

Example: CPA Campaign at $5 Payout

Option A: Loose antifraud, 15% fraud rate, 100 conversions = $75 lost to fraud
Option B: Strict antifraud, 5% fraud rate, 70 conversions = $17.50 lost to fraud

Option A: Net 85 valid conversions. Option B: Net 66.5 valid conversions.

Loose antifraud wins - unless the 30 blocked conversions were actually valid (false positives).

The Hidden Cost of Strict Filtering

When you block aggressively:

Real customers are turned away
Lifetime value is lost, not just one conversion
Competitors capture users you rejected
Publishers stop sending you traffic

Why "Just Block All Fraud" Doesnt Work

Perfect Detection Doesnt Exist

No algorithm can perfectly separate humans from bots. The sophistication arms race never ends. Theres always a gray zone.

Fraud Definitions Vary

Is a VPN user fraud? What about someone who clicked accidentally? A user who converted but disputed the charge? "Fraud" isnt always clear-cut.

Context Matters

Traffic that looks fraudulent for one offer is fine for another:

Datacenter IP might be fraud for local services but fine for B2B software
Fast clicks might be bot fraud or just an eager user
International IP might be VPN fraud or legitimate traveler

The Right Approach

Match Sensitivity to Value

High-value conversions ($100+): Strict filtering justified
Low-value conversions ($1-5): Volume matters more
Brand campaigns: Reach usually beats purity

Test Different Levels

Run parallel campaigns with different antifraud settings. Measure actual ROI, not just fraud rates. Let data decide.

Layer Your Approach

Soft filtering at ad serving (block obvious fraud)
Harder filtering at conversion (validate valuable actions)
Post-analysis to refine rules

Accept Imperfection

Budget for some fraud. Trying to eliminate 100% costs more (in lost legitimate traffic) than accepting a small fraud rate.

What This Means for Platforms

Good platforms let you choose your tradeoff:

Adjustable sensitivity levels
Visibility into whats being blocked and why
Ability to recover false positives
Data to optimize your settings

Bad platforms make the choice for you:

One-size-fits-all antifraud
No visibility into decisions
No way to adjust sensitivity
Trust us approach

Antifraud isnt about being "protected" or "exposed." Its about finding the right balance for your specific situation - and having the tools to adjust when that situation changes.

Antifraud Is Not a Switch: Why Fraud Detection Is Always a Tradeoff