Ad Fraud & Click Spam Statistics 2026

As digital advertising becomes more automated and performance-focused, advertisers need to account for invalid traffic and attribution errors alongside other acquisition risks. Clicks, leads and purchases are useful signals, but they do not always show whether a source caused the outcome or whether the activity was genuine.

For media buyers, affiliate networks and performance-driven brands, the practical concern is how unreliable traffic or attribution can affect spend, partner payouts and optimization decisions. The impact depends on the campaign, channel, traffic sources and the way outcomes are measured.

This report examines ad fraud and click spam in 2026, focusing on how teams can interpret traffic signals, investigate attribution discrepancies and improve operational controls. The figures below use different sources and measurement scopes, so they should not be treated as directly comparable.

Key statistics at a glance

Advertisers were estimated to lose $84.2 billion to ad fraud in 2023, according to Juniper Research’s study produced with Fraud Blocker (2023).
That estimate represented about 22% of the $382 billion in global digital ad spend in 2023, according to Juniper Research (2023).
Juniper Research projected ad fraud losses of $172.3 billion by 2028, or about 23% of projected global digital ad spend of $747 billion, in its 2023 study.
Juniper Research estimated that about 30% of mobile ad spend was lost to fraud in 2023, in its study produced with Fraud Blocker.
Juniper Research estimated that about 17% of desktop clickthroughs were illegitimate in 2023, in its study produced with Fraud Blocker.
Pixalate’s Q2 2026 benchmarks reported global invalid traffic rates of 20% on desktop and mobile web, 41% in mobile apps and 26% on CTV, based on its Q2 2026 benchmarks.
Pixalate’s Q2 2026 United States benchmarks reported invalid traffic rates of 25% on web, 39% in mobile apps and 25% on CTV, in its North America report.
Pixalate’s Q2 2026 EMEA benchmarks reported web and mobile app invalid traffic rates of 18% and 36% in the United Kingdom, and 16% and 37% in Germany, in its EMEA report.
Automated traffic made up 51% of all web traffic in 2024, surpassing human traffic for the first time in a decade, according to the Imperva 2025 Bad Bot Report (Thales).
Bad bots accounted for 37% of internet traffic in 2024, up from 32% in 2023, according to the Imperva 2025 Bad Bot Report (Thales).

Key Ad Fraud & Click Spam Statistics (Executive Summary)

Published loss estimates and invalid traffic rates depend on the channels, traffic types and measurement methods covered.
Click flooding can claim attribution for a genuine install or conversion without having caused it.
Click injection and unusually concentrated click-to-install timing can warrant investigation, but timing alone does not prove fraud.
Affiliate and reseller campaigns require source-level visibility because traffic can pass through multiple intermediaries.
Bot activity, duplicate leads, invalid clicks and misattributed conversions are distinct issues and should be measured separately.
Prevention and investigation depend on consistent event definitions, reliable source data and feedback from downstream outcomes.
Teams should compare campaign performance using validated outcomes, not clicks or attributed conversions alone.

Global Financial Impact of Ad Fraud

Estimating Losses and Interpreting Trends

Juniper Research, in a study produced with Fraud Blocker, estimated that advertisers lost $84.2 billion to ad fraud in 2023, about 22% of the $382 billion in global digital ad spend. The study projected losses of $172.3 billion by 2028, or about 23% of projected spend of $747 billion. Juniper did not publish a detailed methodology, so operators should treat these as estimates rather than campaign-level benchmarks. The figures provide context for the scale of the issue, not a forecast of any individual buyer’s exposure. Source: Juniper Research (2023).

Fraud risk does not necessarily rise in proportion to advertising spend. Exposure can change with traffic mix, campaign design, market conditions, supply-chain transparency and the controls used by advertisers and platforms. Fraud tactics can also adapt as measurement and enforcement practices change, so teams should use their own traffic and outcome data when making decisions.

When entering a new market or channel, assess the available evidence before scaling. Review source-level performance, conversion quality and downstream acceptance, and document how suspicious activity is identified and handled.

Fraud as a Portion of Total Ad Spend

Invalid traffic rates vary by source, geography, inventory and measurement method. Juniper Research estimated that about 30% of mobile ad spend was lost to fraud in 2023 and that about 17% of desktop clickthroughs were illegitimate. These estimates use different measures, so operators should compare them cautiously and validate their own channel-level traffic before adjusting spend. Source: Juniper Research (2023).

Pixalate’s Q2 2026 benchmarks put global invalid traffic at 20% on desktop and mobile web, 41% in mobile apps and 26% on CTV. Its benchmarks cover more than 80 billion open programmatic impressions, mostly buy-side open auction. For affiliate and lead-gen teams, the spread across environments is a reason to assess inventory and outcomes by channel rather than apply one rate to every campaign. Source: Pixalate (Q2 2026).

Campaign objectives also affect what teams need to monitor. Brand-awareness campaigns may focus on delivery and engagement quality, while CPA or revenue-share campaigns need to validate the events that trigger payment. A click or conversion should not be treated as proof of genuine intent without considering its source and downstream outcome.

Longer and less transparent supply paths can make it harder to trace traffic to its origin. That complicates investigation, but does not by itself establish that a source is fraudulent. Record the source and intermediary data available, and compare it with campaign outcomes and quality signals.

Understanding Enterprise and Affiliate Ecosystem Exposure

Working directly with a major platform does not eliminate invalid traffic. Risk can still vary by channel, market, campaign setup and the way activity is measured. Teams should apply consistent validation when they add new regions, sources or campaign types.

Affiliate campaigns can involve multiple publishers and resellers, making it important to preserve source-level information throughout the traffic path. When that information is missing, it can be difficult to determine where a quality issue began or which partner should receive credit. Not every low-quality or suspicious lead is intentional fraud; it may also reflect poor targeting, duplicate submissions or differences in validation rules.

Clear partner terms, shared event definitions and regular reviews of accepted, rejected and converted outcomes help teams investigate discrepancies. For additional guidance, see this overview of affiliate protection.

Click Spam and Attribution Discrepancy Statistics

Click Flooding and Install Hijacking

Click flooding, also called click spamming, is a mobile attribution fraud pattern in which a source reports large volumes of clicks in the hope that one will be recorded as the last touch before an install. AppsFlyer defines click flooding as a mobile ad fraud method where networks report a large number of fraudulent clicks in the hope of taking credit for the last click before an app install. The user and install may be genuine even when the attribution is not.

This can distort campaign reporting and partner payouts. A source may receive credit for an organic install or for demand created through another channel. If teams optimize against that attribution without investigating it, they may reward the wrong source and make scaling decisions using unreliable signals.

Click injection follows a related pattern: a fake click is triggered shortly before an install or first app open to claim attribution. Adjust describes click injection as a form of mobile app fraud where malicious apps trigger fake clicks just before an install, leading to stolen attribution and wasted ad spend. Click-to-install timing can help identify activity for review, but timing alone is not conclusive evidence. Interpret it alongside other campaign and source-level signals.

Misattributed conversions can also affect automated optimization. If a system treats an unearned conversion as a reliable signal, it may shift budget toward a source that did not create the demand. Compare attributed events with validated downstream outcomes and investigate discrepancies before making material decisions.

CPA and Misattributed Conversions

Click spam can make reported CPA unreliable when clicks receive credit for conversions they did not cause. The resulting distortion depends on the volume of affected traffic, the attribution rules and how the campaign records conversions. A universal CPA inflation figure cannot be inferred without campaign-specific evidence.

When organic or direct conversions are assigned to a paid source, performance dashboards can overstate that source’s contribution. This may lead to poor budget allocation, inaccurate partner payments and misleading comparisons between campaigns.

Attribution errors can also affect longer-term analysis. If a team uses misattributed conversions to assess customer value or compare sources, its conclusions may be unreliable. Reconcile platform reports with CRM and buyer outcomes, and document how conversions are deduplicated and validated.

Time-to-Install Anomalies

Time-to-install (TTI) analysis compares the interval between a click and an install. A concentrated cluster of installs shortly after clicks may warrant investigation, particularly when it differs from the campaign’s normal pattern. However, legitimate user behavior can also produce short intervals, so TTI should not be used as a standalone fraud verdict.

Review timing patterns alongside source, geography, device, campaign and downstream outcome data. A sudden change can reflect a traffic-quality issue, a campaign change or a measurement problem; confirm the cause before blocking traffic or changing payouts.

Sources may produce varied timing patterns, and no single distribution proves that activity is genuine. Use consistent measurement windows and compare findings with other available evidence.

Ad Fraud by Traffic Channel

Ad Fraud in Programmatic Display

Programmatic display campaigns can involve many inventory sources and intermediaries, which may make it harder to assess traffic quality and trace its origin. Invalid activity can take different forms, including automated impressions or clicks and discrepancies between reported delivery and observed engagement. Pixalate’s Q2 2026 benchmarks reported invalid traffic rates of 20% on desktop and mobile web, 41% in mobile apps and 26% on CTV. For media buyers, those differences make channel-specific review more useful than a single programmatic benchmark. Source: Pixalate (Q2 2026).

Review supply-path information, placement data, delivery and engagement metrics together. Changes in viewability or engagement can prompt further investigation, but do not establish that traffic is invalid on their own. Keep records of the evidence and definitions used when evaluating a source.

Exposure in Paid Social and Search Advertising

Invalid activity can occur in search and social campaigns, but the signals and measurement practices differ by platform and objective. Click anomalies may merit investigation when they appear alongside other inconsistencies, such as changes in source quality or a mismatch between reported conversions and validated outcomes.

For social campaigns, distinguish between awareness and conversion objectives and evaluate engagement against the goal of the campaign. Platform filtering may affect what is reported, but advertisers should still reconcile campaign data with their own analytics and downstream records where available.

Post-click analysis can help teams understand whether reported engagement leads to accepted leads, sales or other intended outcomes. Treat unusual activity as a prompt to review the evidence, not as a definitive fraud classification.

Risks in Affiliate and Reseller Traffic

Affiliate and reseller traffic can be difficult to assess when source details are incomplete or traffic passes through several intermediaries. Preserve partner and sub-source identifiers where possible, and compare activity with validated lead and conversion outcomes. Duplicate leads, low-quality submissions and invalid clicks should be tracked separately because they may require different responses.

Cross-border campaigns can combine different traffic sources, market conditions and validation requirements. Review results by geography and source, and investigate changes in traffic quality rather than assuming that a particular origin or intermediary is responsible.

Traffic operations platforms can route each click or lead using configured signals and campaign rules. Hyperone routes in real time using factors such as geography, source, device, time, caps, destination availability, traffic quality and fraud signals, and can fall back when a destination is capped or unavailable. CRM and buyer callback outcomes, including accepted, rejected and converted events, can feed subsequent routing decisions. This can help teams align distribution with observed outcomes; it does not replace source validation or establish that a traffic source is fraudulent.

Ad Fraud Risk by Vertical

Fraud Risk in Finance Campaigns

Finance campaigns for credit, loans, and investment products can offer substantial payouts, making lead quality and validation important. Risks include duplicate submissions, fabricated contact details, and incentivized form fills that do not reflect genuine interest.

A lead may pass an initial form check and fail later qualification by the advertiser or buyer. That delay can make source-level performance difficult to assess. Compare downstream acceptance and conversion outcomes with the original traffic source rather than treating every submitted lead as equally valuable.

Fraud Exposure in Gambling Campaigns

Gambling campaigns can attract fraud attempts because operators may pay for registrations, deposits, or other qualified-player events. Risks vary by market, campaign terms, and the quality of partner controls. Inflated registrations can make a source appear productive before deposit, retention, and other later outcomes are known.

Potential warning signs include sudden registration spikes, repeated or incomplete information, unusually short click-to-registration times, inconsistent location signals, and a sharp gap between registrations and later outcomes. Treat these as reasons to investigate, not as proof of fraud. The IAB Europe guide to ad fraud describes how invalid traffic can affect impressions, clicks, conversions, and other data events.

Gambling campaigns also need attribution controls. Click flooding, for example, can make a source appear to deserve credit for a conversion it did not cause. Review the full path from click to verified outcome, and check whether reporting, partner terms, and payout rules are consistent.

Compliance matters alongside traffic quality. The UK Gambling Commission’s guidance on gambling advertising and marketing regulations covers the requirements for socially responsible and compliant promotions. Performance teams should assess whether traffic and partner practices meet the operator’s compliance standards as well as its acquisition goals.

Fraud Risk in Nutra and Lead Generation

Nutra and other high-payout lead-generation campaigns can face duplicate submissions, recycled contact information, and incentivized traffic with little intent to buy. These risks are not uniform across campaigns, so teams should assess them using source-level data and buyer feedback rather than applying a single assumed fraud rate.

Cross-border traffic can also create discrepancies between a campaign’s intended geography and the location or characteristics of incoming leads. Compare targeting settings with available traffic and downstream outcome data, and investigate material mismatches before changing partner payouts or campaign rules.

Bot Traffic and Automation

Human and Non-Human Traffic

The Imperva 2025 Bad Bot Report from Thales found that automated traffic made up 51% of all web traffic in 2024, surpassing human traffic for the first time in a decade. For acquisition teams, that is a reminder to assess whether measured activity represents the users and outcomes a campaign is intended to reach. Source: Imperva 2025 Bad Bot Report (Thales).

The report also found that bad bots accounted for 37% of internet traffic in 2024, up from 32% in 2023. That broad traffic measure is not an ad-fraud rate, but it gives operators reason to review suspicious activity against source and downstream outcome data rather than treating every event as human engagement. Source: Imperva 2025 Bad Bot Report (Thales).

Automated traffic includes legitimate activity, such as search engine crawlers, as well as activity that can distort advertising measurement or spend. A global split between human, benign automated, and malicious traffic is not useful without a defined measurement method, traffic population, and reporting period.

In advertising, invalid automation can generate impressions, clicks, or conversion events that do not represent genuine interest. Teams can investigate patterns in event timing, source performance, and conversion quality. No single signal is enough to establish that traffic is fraudulent; findings should be checked against campaign context and partner data.

How Malicious Bot Activity Can Change

Bot activity and the methods used to disguise it can change over time. Distributed traffic, proxies, and varied request patterns may make simple IP-based rules less reliable on their own. That does not make every unusual location or traffic pattern evidence of fraud.

Review multiple signals together, including source, geography, timing, and downstream outcomes. Keep investigation rules specific to the campaign and revisit them as traffic patterns change.

Changes in Fraud Tactics

Automation can make some forms of invalid activity harder to distinguish from legitimate behavior. Large, synchronized spikes may be straightforward to spot, while low-volume or distributed activity can require closer review.

For that reason, teams should avoid relying on a single threshold or summary rate. Compare traffic patterns with accepted, rejected, and converted outcomes, and document how suspected invalid traffic is identified. For a broader overview of tools used in this area, see AI tools for traffic quality.

Detection and Prevention

Assessing Detection Performance

Detection rates are not comparable unless the measurement period, traffic sources, definition of invalid traffic, and detection stage are clear. Without those details, a percentage can create a misleading impression of how much fraud a system identifies or prevents.

Pre-attribution checks can help teams review traffic before it affects reporting or payouts. Post-click and downstream reviews remain important because a click that appears valid may still produce an unqualified or rejected lead. Combine both stages with clear documentation of the evidence and decision rules.

Real-Time Review and Post-Attribution Analysis

Pre-attribution review: assess source and traffic signals before relying on clicks or leads for optimization.
Post-click review: compare engagement and lead quality with buyer or CRM outcomes.
Outcome monitoring: track accepted, rejected, and converted events, and investigate changes by source.
Campaign adjustments: update routing or partner rules based on documented evidence, not an assumed universal fraud rate.

Real-time review can help teams act on relevant signals sooner, while post-attribution analysis can reveal quality problems that are not apparent at the click stage. Neither replaces clear definitions, reliable tracking, and review of downstream outcomes.

Geographic Differences in Ad Fraud Risk

Traffic quality can vary by campaign, source, and market. Pixalate’s Q2 2026 EMEA benchmarks reported web and mobile app invalid traffic rates of 18% and 36% in the United Kingdom, and 16% and 37% in Germany. For operators buying across these markets, the differences by country and environment support reviewing results at that level instead of applying a regional average. Source: Pixalate (Q2 2026).

Pixalate’s Q2 2026 United States benchmarks reported invalid traffic rates of 25% on web, 39% in mobile apps and 25% on CTV. These figures provide market context, but campaign decisions should still be based on the buyer’s own inventory and validated outcomes. Source: Pixalate (Q2 2026).

Compare the campaign’s intended geography with available location signals and downstream performance. A mismatch may result from targeting settings, reporting limitations, or traffic quality issues, so investigate the cause before taking action. Cross-border traffic can also involve reselling or misrepresentation of a source’s origin; document the discrepancy and review partner terms and tracking data.

Possible indicators include unusual geography patterns, inconsistent language or device information, and a difference between click volume and qualified outcomes. These indicators warrant investigation but do not establish fraud on their own.

Ad Fraud Trends for 2026–2028

Juniper Research projected ad fraud losses of $172.3 billion by 2028, about 23% of projected global digital ad spend of $747 billion. The study did not publish a detailed methodology, so teams should treat the projection as context rather than a precise planning benchmark. For affiliate and lead-gen operators, campaign-level source and outcome data remain more useful for allocation decisions. Source: Juniper Research (2023).

Over the 2026–2028 period, performance teams can plan for continued changes in automation, traffic sources, and methods used to obscure activity. The practical response is to maintain clear measurement rules, review source-level quality, and update checks as campaign conditions change—not to assume that every new technology signals a particular increase in fraud.

Traffic routing and anomaly review can help teams respond to changing conditions. Hyperone routes clicks and leads in real time using configured rules such as geography, source, device, time, caps, destination availability, traffic quality, and fraud signals. Its CRM and buyer callbacks can return accepted, rejected, and converted outcomes to inform subsequent routing decisions.

Conclusion: Data Signals for Performance Teams

Ad fraud and invalid traffic can distort campaign reporting, attribution, and payout decisions, but their scale varies by channel, source, market, and measurement method. Published estimates and benchmarks provide context, not a substitute for a campaign-level assessment.

Performance teams should define invalid traffic clearly, review source-level patterns, and compare clicks and leads with downstream outcomes. Use multiple indicators to guide investigations, and avoid treating any single anomaly as proof of fraud. Where a destination is capped or unavailable, routing rules with a fallback can help direct traffic elsewhere rather than sending it to a failed destination.

For teams that need to act on traffic quality as it changes, Hyperone combines real-time routing with configured quality and fraud signals. Downstream outcomes from CRM and buyer callbacks can feed later routing decisions, giving teams a way to connect traffic allocation with observed performance.

FAQ

What is ad fraud?

Ad fraud is activity that generates or manipulates advertising impressions, clicks, conversions, or other events in ways that misrepresent genuine user interest or campaign performance.

What is invalid traffic?

Invalid traffic is traffic that does not represent genuine user interest or activity under the applicable measurement rules. It can include automated activity, repeated or manipulated events, and other traffic that should not be counted as valid engagement.

What is click spam?

Click spam is the generation of clicks that may falsely claim attribution for an install, lead, or other conversion. The effect depends on the campaign’s attribution method and validation rules.

How can ad fraud affect CPA?

Invalid or misleading traffic can increase measured acquisition costs if spend is directed toward clicks or leads that do not produce valid outcomes. Attribution manipulation can also make it harder to determine which source contributed to a conversion.

How can ad fraud affect ROAS?

Invalid traffic or manipulated attribution can make reported revenue and advertising costs less representative of actual business results. Compare campaign reporting with verified revenue and other relevant downstream outcomes.

Which channels are exposed to ad fraud?

Exposure depends on the traffic source, campaign setup, partner controls, and measurement method. Teams should assess each channel and source using consistent definitions and downstream quality data instead of relying on a universal ranking.

Which verticals can face elevated fraud risk?

Any vertical can face invalid traffic. Campaigns with high payouts or easily triggered conversion events may warrant closer review, but risk should be assessed using the campaign’s own evidence and controls.

What are possible signs of fraudulent traffic?

Possible indicators include unusual click or lead spikes, repeated submissions, mismatched geography signals, abnormal event timing, and a gap between initial activity and verified outcomes. These patterns should prompt investigation rather than be treated as conclusive proof.

Why review traffic in real time?

Real-time review can help teams respond to changing traffic conditions before relying on them for later decisions. It should be paired with post-click checks and downstream outcome analysis.

How can performance teams reduce ad fraud exposure?

Teams can use clear traffic definitions, source-level monitoring, consistent tracking, partner controls, and reviews of accepted, rejected, and converted outcomes. Routing rules can also direct traffic according to campaign conditions and configured quality signals.