Last year, I helped a gaming company discover they'd been paying $400,000 per month to fraudsters. The signs were there all along: impossibly high click-to-install rates, suspiciously perfect geo distributions, users who installed but never opened. They just didn't know what to look for.
Mobile ad fraud is a $65 billion problem. And if you're spending on user acquisition, you're almost certainly losing money to it. The question isn't whether fraud exists in your campaigns—it's how much, and what you're going to do about it.
Understanding the Fraud Ecosystem
Fraudsters aren't random hackers in basements. Mobile ad fraud is a sophisticated industry with specialized players, tooling, and supply chains. Understanding the ecosystem helps you understand the threats.
Who Commits Fraud?
- Device farms: Warehouses of phones that simulate real users at scale
- Bot operators: Sophisticated software that mimics human behavior
- Shady publishers: Apps and websites designed primarily to generate fraudulent clicks
- Resellers and intermediaries: Middlemen who obscure the source of traffic
- Organized crime: Yes, actual criminal organizations—fraud is lucrative
Why Fraud Happens
Fraud exists wherever there's an economic incentive. In mobile advertising, that incentive is huge:
- CPI rates often exceed $2-5 for quality users
- Attribution is complex and imperfect
- Detection lags behind innovation
- Enforcement is difficult across jurisdictions
- Advertisers often don't know what to look for
Types of Mobile Ad Fraud
Fraud comes in many flavors. Here are the most common:
Click Injection
Malicious apps listen for new app installations on a device, then fire fake clicks just before the install completes. This "steals" attribution from the real source. Click injection is one of the most common and damaging fraud types.
How to detect: Look for impossibly short click-to-install times (under 10 seconds). Real users don't click and install that fast.
Click Spamming (Click Flooding)
Fraudsters generate massive volumes of fake clicks from real device IDs, hoping to claim credit when those users eventually install organically. It's a numbers game—send enough clicks, some will match real installs.
How to detect: Abnormally high click volumes with low conversion rates. Click-to-install times of 24+ hours.
SDK Spoofing
Fraudsters reverse-engineer your MMP's SDK and send fake install signals without any real device involvement. This is sophisticated and hard to detect without proper security measures.
How to detect: MMPs offer signature verification. Enable it. Also watch for installs from impossible device configurations.
Device Farms
Physical or virtual farms of devices that install apps repeatedly, sometimes with real human operators clicking through. Device farms produce "real" installs that never monetize.
How to detect: Look at post-install behavior. Device farm users don't act like real users—zero retention, no purchases, repetitive patterns.
Install Hijacking
Similar to click injection, but the malware intercepts the install broadcast and claims attribution through fake referrer data. Particularly common on Android.
How to detect: Unusual referrer strings, mismatched click and install metadata.
Incentivized Fraud
Real users who install apps only for rewards, with no intent to use them. While incentivized traffic can be legitimate, it often gets mixed with non-incentivized campaigns.
How to detect: Very high early retention that drops to near-zero. Users who install but never engage meaningfully.
The Cost of Ignoring Fraud
Every dollar paid to fraudsters is a dollar not spent on real users. Worse, fraud corrupts your data—fake installs skew your metrics, mislead your optimization, and make it harder to find what actually works. Fraud doesn't just steal money; it blinds you.
Building Your Anti-Fraud Defense
Fraud prevention isn't a single tool—it's a system of overlapping defenses:
Layer 1: MMP Fraud Protection
Your Mobile Measurement Partner (MMP) is your first line of defense. All major MMPs—AppsFlyer, Adjust, Singular, Branch—offer fraud prevention tools:
- Click validation: Filtering obviously fake clicks
- Install validation: Verifying install signals are genuine
- Behavioral analysis: Flagging suspicious post-install patterns
- Device profiling: Identifying emulators and suspicious devices
Enable every fraud protection feature your MMP offers. Most are free or included in your plan.
Layer 2: Network Quality Management
Not all ad networks are equal. Some are cesspools of fraud; others are relatively clean. Build a tiered approach:
- Tier 1 (Low Risk): Major platforms like Meta, Google, Apple Search Ads—fraud exists but is manageable
- Tier 2 (Medium Risk): Established ad networks with good reputations—monitor closely
- Tier 3 (High Risk): Unknown or newer networks—test with small budgets and intense scrutiny
Layer 3: Contractual Protections
Your insertion orders and contracts should include fraud protection clauses:
- Definition of what constitutes fraud
- Requirement for traffic transparency
- Right to reject and not pay for fraudulent installs
- Audit rights for suspicious activity
- Clawback provisions for discovered fraud
The Clawback Clause
Always negotiate clawback provisions. If fraud is discovered within 30-60 days, you should be able to recover payment. Networks that refuse this clause are a red flag.
Layer 4: Real-Time Monitoring
Fraud detection is useless if it happens after you've already paid. Set up real-time monitoring:
- Alerts for anomalies: Sudden spikes in installs, CTR, or conversion rates
- Geo distribution checks: Traffic from unexpected locations
- Time-to-install distribution: Unusual patterns in click-to-install timing
- Device fingerprint analysis: Repeated device IDs or suspicious configurations
Layer 5: Post-Install Quality Analysis
The ultimate fraud test is whether users behave like real humans:
- Retention curves: Do users come back? Device farms don't.
- In-app events: Do users complete meaningful actions?
- Revenue attribution: Do users ever pay?
- Session patterns: Do usage patterns look human?
Red Flags to Watch For
After years of fighting fraud, these are the warning signs that always make me investigate:
- CTR over 2%: Organic banner CTR is typically 0.1-0.5%. Much higher suggests fake clicks.
- CVR over 30%: When 1 in 3 clicks converts, something is wrong.
- Perfect geo match: If targeting is USA and you get 99% USA, be suspicious. Real traffic leaks.
- Overnight performance changes: Fraud often turns on/off suddenly.
- Installs without sessions: Users who install but never open = likely fraud.
- Day 1 retention near zero: Real users return at least occasionally.
- Identical time-to-install: Clustering around specific intervals suggests automation.
What to Do When You Find Fraud
- Document everything: Screenshots, data exports, timestamps
- Pause the traffic source: Stop the bleeding immediately
- Notify your MMP: They may have additional intelligence
- Contact the network: Share your findings and request investigation
- Request refund/credit: Most networks will credit clear fraud
- Adjust your data: Remove fraudulent installs from your analytics
- Update your defenses: Learn from the attack pattern
The Refund Conversation
When requesting refunds, be specific and data-driven. "We believe we have fraud" gets ignored. "We identified 12,847 installs with click-to-install times under 2 seconds, which is statistically impossible for real users, and are requesting a credit of $38,541" gets action.
Advanced Fraud Prevention Tactics
Honeypot Campaigns
Run small campaigns on suspicious networks with impossible targeting (e.g., a niche app targeting users in a language your app doesn't support). If you get installs, they're almost certainly fake.
Post-Install Event Validation
Pay only for validated events, not installs. If you pay CPA for first purchase instead of CPI for install, fraud becomes much less profitable.
Machine Learning Models
Build or buy ML models that identify fraud patterns specific to your app. Generic solutions miss app-specific fraud; custom models catch more.
Supply Path Optimization
Understand where your traffic actually comes from. Many "premium" networks are reselling traffic from unknown sources. Demand transparency.
The Economics of Fraud Prevention
How much should you invest in fraud prevention? Here's a simple framework:
If you're spending $100K/month on UA and industry fraud rates are 15-30%, you're losing $15K-$30K monthly to fraud. Even aggressive fraud prevention that recovers half of that is worth significant investment.
The gaming company I mentioned at the start? After implementing proper fraud prevention, they recovered approximately $200K/month in wasted spend. Their ROAS improved by 35% overnight—not because they got better at marketing, but because they stopped paying for fake users.
The Future of Fraud Prevention
Fraud evolves constantly. Tomorrow's fraud won't look like today's. Stay ahead by:
- Following industry research and fraud reports
- Participating in industry working groups
- Maintaining close relationships with your MMP's fraud team
- Regularly auditing your traffic sources
- Never assuming "we've solved fraud"
Privacy changes are creating new fraud vectors. As attribution becomes more probabilistic (thanks to iOS privacy), fraudsters exploit the uncertainty. The battle continues.
Protect Your Investment
ClicksFlyer integrates advanced fraud detection signals with your attribution data, helping you identify suspicious patterns across campaigns and networks. See the quality behind the quantity and stop paying for fake users.