Digital advertising now accounts for 73% of total global media spend, with global digital ad spend projected to reach $740 billion in 2026 – up 11.4% compared to the previous year. On the one hand, the enormous, growing scale makes programmatic advertising an attractive approach for brands and media buyers. On the other hand, it is also why the landscape can be highly vulnerable against threats and ad fraud.
Ad fraud remains one of the most significant and persistent threats in the digital advertising industry. Global losses to ad fraud in 2026 are estimated at $100 billion or more, with roughly 20% or more of programmatic traffic considered invalid in some industry benchmarks
Addressing ad fraud requires a multi-layered approach across the full campaign lifecycle.
- Before a campaign goes live, pre-bid filtering and third-party verification tools block invalid traffic before it consumes budget.
- During the campaign flight, real-time monitoring flags anomalies early enough to act on them.
- After the campaign ends, conducting analyses leads to helpful findings that will benefit future campaign setup.
This article breaks down how ad fraud works in 2026 and what advertisers and media buyers can do at each stage of a campaign to detect, prevent, and recover from it.
Understanding ad fraud – 2026 insights
What is ad fraud?
Ad fraud refers to any deliberate activity that generates illegitimate ad impressions, clicks, or conversions with the intent of extracting advertising budget without delivering real audience value.
Global losses to ad fraud are estimated at $100 billion or more in 2026, with roughly 20% or more of programmatic traffic considered invalid in some industry benchmarks. For advertisers and media buyers, this means a significant portion of campaign budgets may be reaching bots, fake websites, or non-human traffic rather than real, engaged audiences.
Invalid traffic – Two categories
Industry standards distinguish between two types of invalid traffic (IVT). They differ mainly in how difficult it is to detect and pinpoint them.
- General invalid traffic (GIVT) refers to traffic that is relatively straightforward to identify and filter – known data centre traffic, bots declared in robots.txt files, and crawlers that follow predictable patterns
- Sophisticated invalid traffic (SIVT) is significantly harder to detect. It includes hijacked devices, falsified user behaviour, and bot activity specifically designed to mimic legitimate human engagement and evade detection tools
How does ad fraud enter the programmatic supply chain?
The speed and scale of programmatic advertising create structural entry points that bad actors continue to exploit. The most common ways ad fraud get into the supply chain are domain spoofing, ad and pixel stuffing, fraudulent clicks, and low-quality, made-for-advertising sites.
- Domain spoofing: Fraudulent inventory is misrepresented as premium publisher inventory, causing advertisers to pay premium prices for placements that never actually appear on the claimed site
- Ad stacking and pixel stuffing: Multiple ads are layered on top of each other or compressed into a single pixel, generating impression counts without any real visibility to a human viewer
- Click injection: Fraudulent clicks are inserted into the attribution path, falsely crediting ad fraud activity for conversions that happened organically
- Made for Advertising (MFA) sites: Low-quality websites built specifically to attract programmatic spend, generating high impression volumes with near-zero genuine audience engagement
How advertisers and media buyers are addressing ad fraud in 2026
Addressing ad fraud effectively requires action at every stage of a campaign: before the campaign is launched, during its course, and after it has concluded.
Phase 1: Pre-Campaign – Setting up protective barriers against ad fraud
The most cost-effective place to fight ad fraud is before an impression is served. By building verification engines into their pre-campaign setup, advertisers can significantly reduce IVT exposure.
There are several ways they can build preemptive measures against ad fraud.
- Enable ads.txt and app-ads.txt filtering: For many advertisers, these baselines are non-negotiable. These files allow publishers to publicly declare authorised sellers of their inventory, making domain spoofing significantly harder to execute at scale
- Deploy pre-bid verification: Through a trusted third-party partner such as IAS or DoubleVerify, pre-bid filtering evaluates each impression opportunity against known fraud signals before a bid is placed, which blocks invalid inventory before it consumes budget
- Build and maintain allow lists for high-priority or brand-sensitive campaigns: By restricting ad serving to a pre-approved set of publishers and placements, advertisers can reduce the risk of receiving low quality impressions and, consequently, wasting budget.
- Prioritise private marketplace inventory: For campaigns where audience quality is the top priority, many brands resort to this approach: PMP deals limit access to verified, pre-approved publishers, reducing the structural vulnerabilities that open exchange buying introduces
Phase 2: During the Campaign – Monitoring and acting early when ad fraud is detected
Pre-bid filtering reduces fraud exposure but does not eliminate it entirely. Active monitoring throughout the campaign flight allows media buyers to identify and respond to anomalies before they compound into larger budget losses.
- Set automated alerts for red flag signals, including abrupt spikes in CTR or cost-per-view, sub-second session durations, near-zero engagement metrics, and mismatches between traffic source and conversion data
- Review traffic quality metrics continuously rather than waiting for end-of-campaign reporting. Platforms that surface viewability, IVT rates, and engagement data in real time give buyers the visibility needed to act while the campaign is still live
- Conduct mid-campaign partner audits when anomalies appear. If a specific placement or publisher is generating suspicious traffic patterns, pause and investigate before allocating further budget
- Adjust targeting and placement strategy based on what the data is showing. A sudden drop in viewability across a cluster of placements is a signal worth acting on immediately, not flagging for the next campaign review
Phase 3: Post-Campaign – Analyzing outcomes and drawing conclusions regarding ad fraud
After the campaign ends, media buyers and campaign managers can proceed to analyzing outputs and draw insights that will facilitate better decision making in future campaigns.
- Re-run attribution models excluding flagged invalid traffic: This step helps recover a more accurate picture of which channels, placements, and creatives actually drove performance. Attribution built on IVT-contaminated data consistently overstates the contribution of fraudulent sources
- Audit similar inventory sources: After any fraud incident, a thorough audit report helps campaign managers understand the full scope of exposure. Fraud rarely comes from a single isolated source – if one placement is flagged, adjacent placements from the same supply chain are worth reviewing
- Update pre-bid blocklists: By documenting findings in the post-campaign audit, campaign managers can ensure fraudulent sources identified in one campaign cannot re-enter the supply chain in the next
- Enforce transparency requirements with SSP and publisher partners: Brands can request these providers to ensure seller.json compliance and supply path data to verify that inventory is being sourced from declared, legitimate sellers
- Document findings and share them across the buying team: These reports are important to ensure institutional knowledge about fraud patterns is retained and applied consistently.
What good campaigns’ performance looks like – Benchmarks to track
The three-phase anti-fraud approach outlined above needs a reference point to be actionable. Without clear benchmarks, it is difficult to know when a metric signals a genuine problem versus normal campaign variation.
The figures below reflect 2026 industry standards and give media buyers a concrete basis for evaluating traffic quality at every stage.
Invalid traffic rate
IVT rates in open programmatic display environments range from 12 to 25%. That range represents the baseline risk of buying on the open exchange without verification tools in place. Good DSPs with pre-bid filtering in place can even reduce IVT rates to 2 to 3%.
Any campaign consistently sitting above 5% may require immediate investigation of placements and supply sources.
Viewability rate
A viewability rate below 60% on a specific placement is a red flag worth acting on. It may indicate ad stacking, pixel stuffing, or low-quality inventory that is generating impressions without real exposure.
Click-through rate
The global average CTR for display ads is often cited at 0.05% to 0.1%. An abrupt spike well above this range, without a corresponding change in campaign setup, targeting, or creative, is one of the most common early indicators of click fraud or bot activity. CTR should be read alongside session duration and engagement data, not in isolation.
Video completion rate
The benchmark for video completion rate in programmatic in-stream placements is 70 to 80%. CTV averages above 95% completion and viewability above 92%. Completion rates significantly below these ranges on in-stream placements may point to non-human traffic or placement environments where ads are technically served but not genuinely viewed.
Conclusion
Ad fraud in 2026 is not a problem that can be solved once and set aside. It evolves alongside the programmatic ecosystem that enables it, and the methods bad actors use are becoming harder to detect as automated systems grow more sophisticated on both sides.
For advertisers and media buyers, the most important shift in mindset is treating ad fraud prevention as an ongoing operational discipline. Ad fraud will not disappear. But with the right processes, tools, and benchmarks in place, its impact on campaign performance and budget efficiency can be managed, measured, and meaningfully reduced.
