Mobile ad fraud continues to drain billions from marketer budgets, with AppsFlyer's data showing that approximately 22% of non-organic app installs globally carry fraud signals in 2025-2026, up from roughly 17% in 2022. Financial and shopping apps remain the hardest hit categories, with fraud rates exceeding 35% in some regions.
Industry estimates place the total financial exposure to app install fraud in the billions annually worldwide, with click flooding and SDK spoofing consistently ranking as the dominant fraud vectors across measurement providers.
Post-ATT signal loss on iOS has paradoxically made iOS fraud harder to detect due to limited deterministic attribution, while simultaneously shifting fraud volume toward Android—meaning Apple's privacy changes may have worsened the overall fraud environment rather than improving it.
In our experience working across a broad range of mobile app accounts, we have observed that accounts without layered fraud protection consistently waste a significant share of their Android budgets on fraudulent installs that never generate downstream value.
Page Contents
What are the mobile ad fraud rates by app category in 2025-2026?
| App Category | Android Fraud Rate | iOS Fraud Rate | Most Common Fraud Type | YoY Trend |
|---|---|---|---|---|
| Finance / Fintech | 34-38% | 5-8% | SDK Spoofing | Up 6% |
| Shopping / E-commerce | 30-35% | 6-9% | Click Flooding | Up 4% |
| Gaming (Casual) | 18-22% | 3-5% | Device Farms | Stable |
| Gaming (Midcore/Hardcore) | 14-18% | 2-4% | Click Injection | Down 2% |
| Food & Delivery | 25-30% | 4-7% | Click Flooding | Up 5% |
| Travel & Hospitality | 20-25% | 3-6% | SDK Spoofing | Up 3% |
| Health & Fitness | 12-16% | 2-4% | Click Flooding | Stable |
| Entertainment / Streaming | 16-20% | 3-5% | Device Farms | Down 1% |
| Utilities / Productivity | 22-28% | 4-6% | SDK Spoofing | Up 3% |
| Social / Dating | 20-26% | 5-8% | Device Farms | Up 2% |
What are the mobile ad fraud rates by region?
| Region | Android Fraud Rate | iOS Fraud Rate | Primary Fraud Vector | Estimated Annual Exposure |
|---|---|---|---|---|
| Southeast Asia | 35-40% | 6-10% | Device Farms + Click Injection | $850M+ |
| India & South Asia | 30-38% | 5-8% | Click Flooding | $720M+ |
| Latin America | 25-32% | 5-7% | SDK Spoofing | $580M+ |
| Eastern Europe / CIS | 22-28% | 4-7% | Click Flooding + SDK Spoofing | $420M+ |
| Africa / Middle East | 28-35% | 5-9% | Device Farms | $340M+ |
| Western Europe | 10-14% | 2-4% | Click Flooding | $680M+ |
| North America | 8-12% | 2-3% | SDK Spoofing | $920M+ |
| Japan / South Korea | 6-10% | 1-3% | Click Injection | $380M+ |
| China (Domestic) | 18-25% | 3-6% | Device Farms | $510M+ |
Need help scaling your mobile app growth? Talk to RocketShip HQ about how we apply these strategies for apps spending $50K+/month on UA.
How much ad spend is wasted on each type of mobile ad fraud?
| Fraud Type | Share of All Detected Fraud | Avg CPI Inflation | Detection Difficulty (1-10) | Post-ATT Trend |
|---|---|---|---|---|
| Click Flooding / Click Spam | 32-36% | +40-60% CPI | 4/10 | Increasing on Android |
| SDK Spoofing | 24-28% | +80-120% CPI | 7/10 | Increasing across both OS |
| Device Farms / Emulators | 18-22% | +150-300% CPI | 5/10 | Stable, shifting to Android |
| Click Injection (Android) | 10-14% | +30-50% CPI | 6/10 | Stable |
| Install Hijacking | 4-6% | +20-35% CPI | 8/10 | Declining |
| Mixed / Hybrid Fraud | 3-5% | +60-100% CPI | 9/10 | Increasing rapidly |
| In-App Event Fraud | 2-4% | +200-500% CPA | 8/10 | Increasing post-ATT |
What fraud protection benchmarks should mobile marketers track?
| Metric / Benchmark | Healthy Range | Warning Threshold | Critical Threshold | Measurement Frequency |
|---|---|---|---|---|
| Click-to-Install Time (CTIT) | 10s – 24hrs median | > 40% under 10s | > 60% under 10s | Daily |
| New Device Rate | 5-25% | > 40% | > 60% | Daily |
| Multi-Touch Anomaly Rate | < 5% | 5-15% | > 15% | Weekly |
| Session-per-User (Day 1) | 1.5 – 4.0 | < 1.0 or > 8.0 | < 0.5 or > 15.0 | Daily |
| Day 1 Retention (vs. organic) | Within 20% of organic | 30-50% below organic | > 50% below organic | Daily |
| Revenue-per-Install (Day 7) | Within 25% of organic | 40-60% below organic | > 60% below organic | Weekly |
| Geographic Distribution Anomaly | Matches targeting ±10% | > 20% deviation | > 35% deviation | Weekly |
| Duplicate IP Cluster Rate | < 3% | 3-8% | > 8% | Daily |
| Conversion Rate (Click-to-Install) | 1-15% (varies by channel) | > 25% | > 40% | Daily |
How do self-attributing networks compare to non-SANs for fraud rates?
| Channel Type | Avg Android Fraud Rate | Avg iOS Fraud Rate | Primary Risk | Recommended Protection Layer |
|---|---|---|---|---|
| Meta (Facebook/Instagram) | 2-5% | 1-2% | Low, mostly click-spam edges | MMP baseline + post-install validation |
| Google Ads (UAC/ACe) | 3-6% | 1-3% | Low-moderate click flooding | MMP baseline + conversion validation |
| TikTok Ads | 4-8% | 2-4% | Moderate SDK spoofing risk | MMP fraud suite + CTIT analysis |
| Apple Search Ads | < 1% | < 1% | Negligible | MMP baseline sufficient |
| Snap Ads | 3-7% | 2-4% | Moderate click flooding | MMP fraud suite |
| Programmatic / DSPs (Tier 1) | 12-20% | 5-10% | High, device farms + spoofing | Full MMP fraud suite + manual audits |
| Programmatic / DSPs (Tier 2-3) | 25-45% | 8-18% | Very high, all vectors | Full suite + independent verification |
| Ad Networks (Incentivized) | 30-50% | 10-20% | Very high, device farms | Full suite + post-install event gating |
| Influencer / CPI Networks | 15-30% | 5-12% | High, mixed fraud | Full suite + cohort analysis |
Analysis
The 2025-2026 fraud landscape tells a clear story: the post-ATT world has not reduced fraud. It has merely redirected and reshaped it.
Industry data consistently shows that financial exposure to app install fraud has grown year over year, driven primarily by the explosion of Android fraud in markets where digital advertising spend is growing fastest: Southeast Asia, India, and Latin America. This growth mirrors the broader trend where global app install ad spend growth, a 12% year-over-year increase that has attracted proportionally more fraud.
The Android-to-iOS fraud ratio has widened substantially since ATT, with industry patterns suggesting Android fraud now runs materially higher than pre-ATT levels relative to iOS. This is not because iOS has gotten more secure (though Apple's ecosystem does provide some baseline protection).
It is because the deprecation of IDFA under App Tracking Transparency has made iOS attribution murkier, pushing both legitimate and fraudulent volumes toward Android where deterministic attribution via GAID still functions (at least until Google's Privacy Sandbox for Android matures).
SDK spoofing has emerged as the fastest-growing fraud vector, with industry observers tracking consistent year-over-year increases. This technique, where fraudsters generate fake install signals without any real device interaction, is particularly insidious because it bypasses traditional device-level detection.
The rise of hybrid fraud (combinations of multiple techniques in a single campaign) is especially concerning. Fraudsters are increasingly sophisticated, layering click flooding with low-level SDK spoofing to create patterns that look more organic to basic detection systems.
Fraudsters are increasingly sophisticated, layering click flooding with low-level SDK spoofing to create patterns that look more organic to basic detection systems. North America and Western Europe have the lowest fraud rates not because fraudsters ignore these markets, but because higher CPIs justify more sophisticated protection investments.
In high-CPI markets like the US, even a modest fraud rate represents meaningful dollar losses, so marketers deploy full MMP fraud suites.
In lower-CPI markets like Southeast Asia, the same dollar loss threshold can allow fraud to persist at much higher percentage rates before the pain becomes acute enough to trigger intervention.
The financial and shopping categories are hardest hit because their high LTV and correspondingly high CPI payouts attract the most sophisticated fraud operations. Fraudsters follow the money, and high-value fintech installs are far more lucrative targets than low-CPI casual game installs. Understanding how to reduce CPI for mobile apps through creative testing is critical, since active creative testing can lower CPIs and simultaneously make fraud economics less attractive.
What This Means For You
What This Means For You: Start by auditing your current fraud exposure before optimizing anything else. If you are spending more than $5,000 per day on Android campaigns across non-SAN channels, you almost certainly have a fraud problem, whether you see it in your dashboard or not.
The first concrete step is enabling your MMP's full fraud protection suite. AppsFlyer's Protect360 and Adjust's fraud prevention toolkit both offer real-time blocking and post-attribution fraud detection.
Real-time blocking catches a meaningful share of fraudulent installs, but you also need the post-attribution layer to catch sophisticated attacks that slip through initial screening. Second, implement the Click-to-Install Time (CTIT) distribution analysis as your primary diagnostic.
Legitimate installs follow a predictable curve: most occur 30 seconds to 2 hours after the last click, with a long tail extending to 24 hours. If more than 40% of your installs from any single sub-publisher show CTIT under 10 seconds, that is almost certainly click injection.
If the distribution is unnaturally flat (equal installs across all time buckets), that signals click flooding. Third, validate using downstream metrics.
At a high level, comparing Day 1 retention and Day 7 ROAS of each source against the organic baseline, weighted by spend volume, can help flag potential fraud signals.
A source with Day 1 retention far below organic baseline is a higher-priority investigation when it is also spending at high daily volume, because the absolute budget at risk is greater.
This approach, weighting by business impact using abs(% change) x sqrt(spend), eliminates the noise of small, volatile sources and focuses your fraud team on the sources burning real budget. Fourth, concentrate your budgets.
As we have discussed regarding why early-stage apps should not diversify ad spend, spreading thin across many networks at low daily budgets creates two problems simultaneously: you do not get enough conversion data for algorithms to optimize, and you expose yourself to more fraud vectors.
Self-attributing networks like Meta, Google, Apple Search Ads, and TikTok have dramatically lower fraud rates (1-8% versus 12-50% for programmatic and ad networks), as confirmed by AppsFlyer’s 2025 Performance Index. If you are running on non-SAN channels, gate access to payouts behind meaningful in-app events.
Require registration completion, a first purchase, or a Day 3 retention event before crediting the network. This makes the economics of fraud unprofitable for all but the most sophisticated operations. Fifth, revisit your privacy-first attribution and measurement setup.
Post-ATT, the temptation is to rely more heavily on probabilistic attribution and modeled conversions, but these methods are more susceptible to fraud manipulation.
Use SKAdNetwork (now SKAN 4.0) conversion values as a fraud validation signal: if a source shows high install volumes in your MMP but minimal or zero SKAN postbacks, that is a red flag worth investigating. Understanding how SKAdNetwork works and underreporting helps you set proper validation thresholds.
Frequently Asked Questions
How much money is lost to mobile ad fraud each year?
Global financial exposure to mobile app install fraud exceeds $5.4 billion annually as of 2025-2026, according to AppsFlyer's data. This represents roughly 22% of all non-organic app installs carrying some form of fraud signal. The actual realized loss depends on your fraud protection stack.
In our experience, marketers using full MMP fraud suites meaningfully reduce their fraud exposure compared to those running without protection, who can see substantial portions of Android budgets consumed by fraudulent activity.
Is mobile ad fraud worse on Android or iOS?
Android fraud rates are approximately 4.5x higher than iOS. Android averages 18-25% fraud across all categories globally, while iOS sits at 2-6%. Post-ATT, the gap has widened because the deprecation of IDFA reduced the volume and value of iOS attribution, making Android a more attractive target for fraudsters who rely on deterministic device-level attribution to claim credit. This shift occurred as iOS opt-in rates stabilized globally, leaving 70-75% of users invisible to deterministic attribution.
What is the most common type of mobile ad fraud?
Click flooding (also called click spam) accounts for 32-36% of all detected mobile ad fraud, making it the most prevalent type. SDK spoofing is the second most common at 24-28% and is growing fastest at roughly 18% year over year.
Click flooding works by sending massive volumes of fake clicks so the fraudster gets attribution credit when a user organically installs, while SDK spoofing fabricates entire install signals without any real user interaction.
How do I know if my mobile ad campaigns have a fraud problem?
The strongest diagnostic signals are abnormal Click-to-Install Time (CTIT) distributions, Day 1 retention rates more than 30% below your organic baseline, and new device rates exceeding 40% from any single source. If a sub-publisher shows conversion rates above 25% (click-to-install), that almost always indicates click injection or attribution manipulation.
Compare these metrics source by source against your organic cohorts to isolate problematic networks.
Do self-attributing networks like Meta and Google have fraud?
Yes, but at dramatically lower rates. Meta's fraud rate runs 2-5% on Android and 1-2% on iOS. Google Ads shows 3-6% on Android. Apple Search Ads is below 1%. These networks invest heavily in their own fraud detection because fraudulent installs degrade their algorithm performance.
By contrast, programmatic DSPs and CPI ad networks can show fraud rates of 12-50%, which is why concentrating spend on SANs, especially at lower budgets, significantly reduces fraud exposure.
Has mobile ad fraud gotten worse since Apple's ATT/iOS 14.5?
The overall fraud volume has shifted rather than simply increased. iOS fraud has become harder to detect (due to less deterministic data) but has not grown in volume. Industry patterns suggest Android fraud has grown since ATT launched, as fraudsters redirected efforts toward the platform with more exploitable attribution signals.
The rise of probabilistic and modeled attribution on iOS has also created new vectors for attribution manipulation that did not exist pre-ATT. Since Meta’s Conversions API improves downstream CPAs when properly implemented, ensuring CAPI is configured correctly becomes both a performance and fraud prevention measure.
What tools should I use to detect and prevent mobile ad fraud?
Your mobile measurement partner's fraud protection suite is the baseline: AppsFlyer Protect360, Adjust Fraud Prevention, or Singular's fraud tools. Enable both real-time blocking and post-attribution detection. Layer on top of this with your own CTIT distribution analysis, new device rate monitoring, and downstream conversion validation.
At scale (above $50K/month), consider independent verification tools like Interceptd or TrafficGuard as a second opinion on your MMP's findings.
What fraud rate should I consider acceptable for my mobile UA campaigns?
On self-attributing networks, anything under 5% is within normal range and difficult to eliminate entirely. For programmatic and ad network traffic, target under 10% after fraud filtering is applied. If post-filtered fraud rates on any single source exceed 15%, pause that source and investigate before resuming.
Zero fraud is not a realistic target because some false positives exist in fraud detection, and the marginal cost of eliminating the last 2-3% of fraud often exceeds the cost of the fraud itself.
Looking to scale your mobile app growth with performance creative that delivers results? Get in touch with RocketShip HQ to learn how our frameworks can work for your app.
Not ready yet? Get strategies and tips from the leading edge of mobile growth in a generative AI world: subscribe to our newsletter.
Related Reading
- Privacy-first attribution and measurement for mobile apps (comprehensive guide)
- Lookalike audiences for mobile app UA
- Privacy-first attribution and measurement for mobile apps
