Globally, digital advertising spend is becoming more accessible, and a recent industry study estimates ad spend crossed $700 billion in 2025 and is expected to cross $780 billion in 2026. With ad spend becoming more automated, advertisers are focusing their budgets on measurable engagements (i.e, clicks, leads, and purchases) that are located within a more automated, multi-layered, programmatic, and data-driven environment. This act increases exposure for advertisers and publishers to invalid traffic and click manipulation, and distortion of proper click attribution.
Performance marketing has become a systemic fraud and financial liability risk, and high-volume advertisers need to think of fraud as a systemic financial liability. High-volume advertisers need to incorporate fraud within the acquisition cost models, attribution models, partner payouts, and their optimization models. Fraud, especially digital fraud, is self-reinforcing and compounds over time.
As performance marketing systems become more sophisticated, so does fraud. More and more automated bidding users are found within EU and US borders, and automated bidding engines are creating arbitrage, cross-border bidding, fraud, and optimization gaps. New fraud techniques are sbeing spottedand placing gaps that disrupt the normal user engagement patterns to mimic legitimate user interaction.
For media buyers, affiliate networks, and performance-driven brands in super competitive verticals (Finance, Nutra, and Gambling), the implications are clear and concrete. Fraud kills effective return on adspends (ROAS), skyrockets cost per acquisition (CPA), and wrecks your conversion baselines. In verticals where the margins are tight and the customer acquisition costs are high, even a 5–10% fraud exposure can deteriorate your profitability.
Ad fraud stats should be a given at this stage, and predictive stats should be the foundation of your financial models, risk assessments, and operational controls. This report attempts to provide a structured overview of ad fraud and click spam in 2026, combining fraud estimates from the industry, aggregated cybersecurity research, and the benchmarks of attribution studies.
Key Ad Fraud & Click Spam Statistics (Executive Summary)
- Estimated global ad fraud losses in 2026: $100–120 billion annually
- 8–15% of global digital ad spend is exposed to invalid traffic (IVT)
- 12–25% invalid traffic rate in open programmatic display environments
- 5–12% click spam exposure in mobile app install campaigns (aggregated attribution studies)
- Up to 30% of affiliate traffic in high-risk verticals shows anomaly indicators
- 40–50% of total web traffic estimated to be bots; 25–35% classified as malicious bots
- 10–20% CPA inflation in campaigns with undetected click flooding
- 3–8% average ROI erosion in moderately exposed enterprise campaigns
- 15–30% ROI erosion in poorly controlled reseller-heavy ecosystems
- 60%+ of mobile click spam events occur within seconds before install (attribution hijacking pattern)
- 20–35% of ad fraud activity is routed through residential proxy networks
- 2–5% of paid search clicks flagged as invalid in mature accounts; higher in emerging markets
- 18–30% fraudulent traffic exposure in Gambling campaigns in unregulated markets
- 12–22% fraud exposure in Finance lead-generation funnels (varies by GEO)
- 5–15% duplicate or low-quality lead rate in Nutra and high-CPA lead funnels
- 50–70% pre-click fraud filtering is achievable with infrastructure-level controls
- 30–50% post-attribution detection rate, depending on analytics sophistication
- Fraud growth rate estimated at 8–12% CAGR, tracking closely with digital ad spend growth
- Cross-border arbitrage accounts for 20–40% of high-risk affiliate fraud cases
Global Financial Impact of Ad Fraud
Estimated Annual Losses and Growth Trends
For 2026, total losses are projected to be in the $100 to $120 billion range, depending on the method used and what is included (display-only vs. cross-channel). The estimated fraud losses CAGR for the last five years is between 8% and 12%, closely following the growth of digital ad spend overall.
Regardless of the ad spend growth, fraud growth will most likely always be proportional, and budgets will continue to grow as more and more automation is added and new channels are opened. As new fraud detection measures are introduced, fraud operators will quickly adapt to take advantage of those new measures.
The most evident examples of increased ad fraud are in rapidly growing economies. As new markets become digitized, new performance-oriented budgets are adopted. The lag in the implementation of fraud detection measures leads to the manifestation of high levels of invalid traffic during the ad spend growth.
Fraud as a Portion of Total Ad Spend
Aggregated industry analysis indicates field of research exposure to some form of invalid traffic fraud globally,y regarding digital ad spends of 8-15% to some form of fraud. Exposure changes widely due to Open programmatic display environments, ts which tend to report IVT rates often between 12-25% as opposed to more tightly controlled ecosystems of search,rch which report invalid click rates of 2-5% unless there are volatile geographies.
Brand-awareness campaigns are faced with fewer fraud exposure targets than CPA or revenue-share model optimized campaigns, which tend to have more fraud exposure due to the measurable payout event targets. This sort of event typically results in a greater volume of invalid traffic than a brand awareness upper-funnel placement of ads.
A decrease in layered models traffic of impressions or clicks in which multiple intermediaries are involved equals a decrease in transparency. Less visibility in regard to the origin of the traffic means greater exposure to statistically invalid activities in combination with the more structurally complex control Layers. In result, the percentage of spending lost is not uniform and depends on the overall transparency of the control.
Understanding Enterprise and Affiliate Ecosystem Exposure
Enterprise brands that directly collaborate with large platforms have relatively low baseline fraud exposure due to the filtering done at the platform level. However, even the enterprise level can still capture some invalid traffic, especially in the display, video, and app install campaigns when new regions or networks are opened.
The model used in cancellation Affiliate ecosystems, especially those with several layers of resales, becomes even more vulnerable. Each additional layer of resale models increases obscurity and decreases responsibility. Composite analyses show that layered traffic models have anomaly rates from 1.5 to 3 times higher than those observed in direct publisher relationships.
Models using many resellers increase risk due to the dilution of fraud signals across traffic routing. The fraud in such ecosystems isn’t always intentional, and the fraud simply circulates in layers where the upstream traffic quality has not been sufficiently validated. With additional distribution layers, the statistical exposure increases.
Click Spam and Attribution Discrepancy Statistics
Rates of Click Flooding and Install Hijacking
Click flooding, a typical phenomenon in mobile performance campaigns, involves artificially creating a large number of background clicks in order to maximize the chances of receiving attribution. Aggregated studies in mobile attribution show that click spam accounts for 5-12% of exposed installs in some verticals, especially in gambling and casual gaming, and the rates are even higher.
Patterns of install hijacking tend to repeat over time and exhibit the same structure and timing signature as pattern-based fraud. An example of this is the attribution of click spam, which causes fraud in the first range and accounts for approximately 60% of the click spam fraud within seconds before the recorded install event. This is an example of pattern-based attribution hijacking.
In statistically driven models, there is often a presence of click-to-install distribution anomalies, and in some cases, the distribution anomalies occur at a greater frequency than what would be expected for an organic user flow.
In high competition CPA campaigns, click flooding creates confusion in campaign optimization logic, as there is no clear signal for the campaign to optimize toward. The machine learning systems automate bidding at a set CPA, and as a result, the system pseudogenrates a successful event for the underlying mechanisms of the system to function and spurs fraud. The system often creates a distortion that becomes self-sustaining when the fraud detection systems are working poorly.
SAP and Misattributed Conversions CPA
By entering clicks that do not result in a conversion, or are misattributed, click spam and fraud lead to an increase in CPA, which is a CPFR violation. Because of the CPFR violation, it is estimated that the average inflation of CPA is approximately 10-20% across most of the industry in campaigns that are moderately exposed. In cases of extreme exposure, the rate of click spamming can lead to an inflation greater than 30%.
In an environment of misplaced Conversions ROI aggravation, when non-fraudulent direct traffic conversions or organic installs are misattributed to the sources of the fraudulent clicks, performance dashboards overstate the partner’s contribution. This results in poorly informed scaling decisions and poor budget allocation.
Considering the modeling approach, every small percentage error from attribution impacts the return on investment (ROI) from a long-term perspective. If, say, 8-10% of conversions are incorrectly attributed, the lifetime value (LTV) of the attributed conversions is grossly miscalculated. Media buyers using automated rule-based programs may reinforce margins across channels without knowing.
Time-to-Install Anomalies
The statistical approach to click spam detection may center around time-to-install (TTI) analysis. Valid user behaviors demonstrate a reasonably wide range of TTI values, which can span several minutes, hours, or even days, depending on the size of the app and the intention of the user. Counterfeit installs, on the other hand, exhibit tight clusters of time around the last click.
Anomalies are shown by a sudden spike within near-zero TTI. If a large proportion of installs is triggered within a click, this increases the possibility of click hijacking. Most analytic models identify these patterns by using distributional deviation (i.e., click > install > time difference > fraud detection).
The fraud operators may have to make a more random TTI interval to decrease detection. However, even after attempts to randomize TTI, patterns of distribution irregularities and IP rotations do not go undetected in a large aggregation analysis.
Ad Fraud by Traffic Channel
Ad Fraud in Programmatic Display
Programmatic display advertising is also the most vulnerable advertising method when it comes to invalid traffic. Multiple open exchange environments display an IVT rate of 12–25% on average, varying by geography and inventory tier. Private marketplaces andsupply pathss that are selectively curated have consistently lower rates, usually under 10%.
Lower-tier inventory pools are still dealing with ad stacking, pixel stuffing, and domain spoofing. Fraud detection vendors have estimated that a significant percentage of suspicious display traffic is caused by bot-driven impressions. In spite of verification tools addressing large-scale domain spoofing to a greater degree than in the past, small arbitrage networks still exploit under-regulated supply chains.
Performance campaigns on wide-ranging display networks that have not been tightly controlled in terms of supply path optimization tend to have the highest levels of irregularities. There are definite statistically significant correlations in viewability and engagement metrics with the quantity of invalid traffic that is exposed.
Rates of Exposure for Paid Social and Search Advertising
As a rule, search advertising has a lower rate of invalid clicks than other advertising methods, and rates of 2–5% are usually seen in mature accounts. Yet for localized campaigns in developing markets, an unusually high rate of anomalous clicks is commonplace. Search fraud is most typically the result of automated click inflation or click spam coming from a competitive adversary and not from bot-driven impression fraud.
Social media advertisements expose different campaign objectives. Brand awareness objectives have fewer false engagements than conversion-focused campaigns. Research indicates 5-10% anomalous engagements in social campaigns. Despite this, social media sites have filters to reduce bot traffic.
Due to this change in social media sites, less focus is put on mere impression fraud. This leads to more focus on false clicks and inflated engagements. At this point, post-click analytics and attribution modeling are most important.
Metrics of Risks Associated with Affiliates and Resellers
Affiliate and reseller traffic models show increased chances of fraud. Based on location and industry, the anomaly range appears to vary between 10-30%. This multi-layer approach has increased complexity when trying to identify the original fraud source.
Border-crossing reseller traffic combines different traffic types. If there is no central point of verification, the chances of using bot traffic or recycled leads increase. Inadequate monitoring may lead to inflated clicks of more than 10%.
In order to deal with these problems, some teams build control layers at the level of infrastructure that filter and redistribute traffic before it reaches endpoints of monetization. These systems execute some logic of routing, analysis of IPs, and detection of anomalies in real-time. Hyperone is a reference platform in these cases, as it sstressestraffic automation infrastructure to manage risk and reduce exposure before payout attribution. The objective of these types of infrastructure is a statistically measurable reduction in cases of invalid traffic.
Fraud Ad by Vertical
Fraud Rates by Finance Vertical
In the Finance campaigns, there are credit, loans, and investment products that operate under higher CPA conditions. Industry average estimates are under fraud risk exposure of 12-22%, with higher rates in developing markets. Lead duplication and incentivized form submission are ways to achieve this.
Since finance funnels are usually designed to have a multi-step qualification, a fraudulent lead may, inn fact, pass the initial validation. However, downstream validation may result in closure of that lead. This creates a delayed detection cycle. In fact, a 5-8% rate of low-quality leads can cause a considerable increase in the cost ofthe acquisition model.
Exposure of Gambling Campaigns
Gambling campaigns are also some of the highest-risk verticals. In the case of less regulated markets, fraud exposure of 18-30% is reported. Bot registrations, bonus abuse, and click flooding are the attacks of choice.
High payouts per conversion encourage fraud schemes on a sophisticated level. Because of this, statistical anomaly detection becomes more difficult. From the perspective of analytics, patterns of conversion timing, open irregularities across devices, device fingerprints, and cross-account overlaps of the same IP address stand out as significant.
Fraud Risk for Nutra & Lead Generation
Nutra and high-CPA lead-generation funnels are subject to moderate but persistent fraud exposure, typically 10–20%. Concerns include duplicate leads, recycled contact information, and falling into the trap of low-intent incentivized traffic.
Due to the aggressive scaling models and global reach of Nutra campaigns, fraud via cross-border traffic arbitrage becomes a prominent risk. The statistical variance of the lead-to-sale conversion ratio often exposes the fraud.
Statistics Regarding Bot Traffic and Automation
Human vs Non-Human Traffic Split
NuData Security conducted a study in 2020 estimating that, out of a total web traffic volume of 40-50%, 20-30% of traffic is considered to be automated in a non-misleading sense (such as search engine indexing), and about 20% of traffic is malicious in nature.
In the realm of advertising, automated traffic (bots) is designed to mimic the appearance of genuine impressions, clicks, and even conversions, if the objective of the advertisement is to generate revenue based on these actions. Behavioral analysis, IP intelligence, and device fingerprinting are the three most common methods used to determine whether a user is a bot or a human.
The Growth Patterns of Malicious Bots
Malicious bots have uninterrupted growth; year-over-year growth of 5–10% is cited for each of the previous 10 years. Newer residential proxy networks have become 20–35% of the detected bot networks. Geographic filtering of bot traffic has increased complexity for IP-based filtering.
Fraud manipulators, as of llatee have distributed networks for the purposes of posing as natural users. When Bot traffic is heavily distributed, more central, and less visible to unmitigated large-scale anomalies.
The Advancement of Fraud Technology
The last year has shown measurable growth in the complexity of fraud schemes due to the ease of access to technology. Automated script detection, as one example, has become more sophisticated in targeting specific users to generate and capture internet traffic. Although large simultaneous movements of users are detectable.
The net impact of technology-based fraud schemes will not correlate with the increase in total instances of fraud; the impact will be felt more in increasing the complexity of determining whether fraud has occurred due to the increasingly sophisticated measures fraud practitioners will employ to either obfuscate their activities or create the illusion of legitimate activities. Thus, conventional statistical measures will need to be more comprehensive and multi-dimensional to adequately capture the behavioral patterns being generated.
Detection & Prevention Benchmarking
Industry Average Detection Rates
Detection systems before clicks, if set up correctly, can catch between 50-70% of invalid traffic before reaching the attribution pipelines. For post-click detection systems, about 30-50% of the leftover fraud traffic after behavioral analytics and conversion modeling is fraud funnels.
Time is of the essence. Campaigns where filtering of invalid traffic is done in real-time do a better job of preventing the inflation of CPAs than those where auditing is done on a set schedule. In real-time, the signals that have been distorted will most likely be captured and sent into the system to be optimized.
Real-Time Filtering vs. Post-Attribution Detection
- Pre-click detection: 50-70% of invalid traffic is filtered before reaching attribution
- Post-click detection: 30-50% of the fraud traffic is captured through behavioral analytics
- Automated systems: < 24-hour
- ROI recovery: 5-15% positive change in Effective ROAS in about 5-15% of likely exposed campaigns
- Fraud filtering: 5-20% chances on reducing CPA
Real-time filtering mostly stops fraud from infecting the systems that set the optimized loops. Clear attribution, although useful, mostly treats the problem and not the systems that are teaching the systems to bid.
Geographic Distribution of Ad Fraud
Fraud is concentrated by region. Emerging digital markets have rapid digital growth and higher IVT levels, and in open programmatic channels,s this rate can exceed 20%. In the regions that have good regulations and a developed ad ecosystem, 20% IVT exposures are not a concern.
Campaigns across different regions show vast differences between stated and actual traffic origins. When conducting an IP analysis for a campaign, there are traffic patterns that do not align with the stated GEO targeting. Fraudulent traffic sellers take advantage of areas with weaker traffic enforcement and illegally resell traffic across borders.Cross-Borderr Arbitrage Example.
Cross-border arbitrage fraud is responsible for approximately 20-40% of high-risk affiliate fraud. Fraud traffic is bought cheaply in one region and illegally sold to higher-paying GEO campaigns, usually through the use of proxy masking.
Many of these fraud patterns are revealed with a disproportionate number of clicks and a poor conversion rate. Common signs of this activity include high bounce rates, inconsistent language and device use, and short session times.
Ad Fraud Trends 2026-2028
As a result of projected increases in digital advertising, fraud losses are also anticipated to rise to an estimated \$130-150 billion by 2028. Current estimates of compound annual growth rate (CAGR) are in the high to low single digits.
AI fraud technology is anticipated to become more sophisticated, which may make detecting fraud more difficult without significantly increasing the rate of fraud. Fraud activity is expected to be more dispersed and less concentrated. It is also anticipated that proxy devices and residential devices will be used to circumvent fraud detection.
The infrastructure-level mitigation is projected to grow in parallel. Systems such as automated traffic routing, anomaly detection, and pre-attribution filtering become common in performance stacks. The statistical aim will move from reactive reporting to proactive contamination.
Therefore, growing traffic automation is likely, especially in high-risk verticals. The priority will be on measurable control of invalid traffic and achievement of ROI.
Conclusion — Data Signals to Performance Teams
Evidence shows that ad fraud is a digital advertising ecosystem component that is structurally persistent. Fraud costs the world around $100 billion a year, and exposed invalid traffic fraud is between 8 and 15% of total spend.
The range of fraud in exposed channels is wide. The programmatic display and affiliate ecosystems have higher fraud anomaly rates than controlled search. Cyber fraud is more pronounced in the Gaming and Finance sectors as the incentive payment is higher.
Click spam and CPA attribution manipulation distort the CPA. The acquisition costs can be increased by 10% and more when some of the costs are hidden and go unaccounted. Roughly half of the total traffic on the internet is bot traffic, and it still continues to evolve along with residential proxies as well as AI-based behavior simulation.
Detection benchmarks show that filtering in real-time and controls at the infrastructural level reduce exposure. While analytics after attribution are still useful, preventing fraud at the entry point of the traffic provides a more reliable ROI.
For performance teams in competitive verticals, the signal from the data is unambiguous: fraud should be considered an operational variable and an integral part of infrastructure folds. For 2026 and the years to come, the integration of statistical oversight, automated filtering, and structural clarity in the performance system will be vital for the preservation of sustainable performance economics.
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