Competitor ad fraud detection research: What recent studies reveal about invalid clicks

Competitor ad fraud detection has moved from a fringe concern to a core requirement for PPC advertisers operating in highly competitive auctions.

As automated bidding systems dominate Google Ads and Microsoft Ads, invalid clicks are increasingly being used to manipulate auction dynamics, drain budgets, and distort performance signals.

According to industry estimates, approximately 14 to 22% of paid search ad clicks are invalid or fraudulent; that is, about 1 out of 5 clicks is not made by a real user, particularly in competitive campaigns.

This article explores recent discoveries and shows how proactive defenses, including competitor IP blocking, fit into a contemporary fraud prevention plan.

The current landscape: Scale, growth, and sophistication of ad fraud

Recent research published between 2024 and 2025 highlights two consistent realities of the current ad fraud landscape.

First, invalid click activity remains a significant and growing financial burden for advertisers. Second, the techniques used to generate fraudulent clicks, particularly those tied to competitive sabotage, have become far more sophisticated and harder to detect with simple rules.

Across search, display, and app advertising, industry analyses show steady year-over-year growth in fraud exposure.

While estimates vary by methodology and region, the consensus is clear: without improved defenses, fraud-related losses will continue to rise.

Competitive verticals such as finance, legal services, insurance, SaaS, and e-commerce are especially exposed due to high CPCs and aggressive bidding environments.

A particularly concerning development documented in recent investigations is the rise of so-called “ghost click farms” and device hijacking schemes.

In these setups, real user devices often compromised via malicious apps or scripts, are unknowingly turned into fraud nodes.

The resulting clicks closely resemble legitimate human behavior, rendering traditional indicators like abnormal geolocation, rapid click repetition, or clearly bot-like user agents far less reliable than they once were.

Competitors as active attackers: Targeted and intentional fraud

Competitor ad fraud is often deliberate and strategic. Research and campaign audits show that rivals may click ads to exhaust daily budgets early, remove competitors from auctions, or feed misleading signals into automated bidding systems.

These attacks typically target high-intent, high-cost keywords, where even a small number of invalid clicks can have a measurable impact.

Indicators include repeated clicks from specific IP ranges or networks, activity clustered around competitor business hours, and clicks that never progress toward meaningful engagement or conversions.

What recent research says about effective detection methods

Recent academic and applied research has helped clarify which detection approaches work best against competitor-driven fraud.

Machine learning is still useful, but transparency matters

Studies show that models such as Random Forest, XGBoost, and neural networks can identify fraudulent patterns when trained on rich data sets.

However, advertisers increasingly need explainable results to justify blocking decisions and support platform disputes.

Cross-signal correlation

Effective detection combines multiple signals, network data (IP, ASN), device characteristics, behavioural patterns, and post-click activity.

Isolated anomalies may raise suspicion, but correlated signals significantly reduce false positives.

Real-time blocking vs. post-hoc refunds

Research consistently finds that blocking invalid clicks before they are billed protects budgets and preserves campaign learning.

Refunds may recover some spend, but they do not undo damaged performance data or corrupted bidding signals.

Why blocking competitor IPs is a practical and effective tactic

One consistent finding in applied research is that a meaningful portion of competitor fraud originates from repeatable and identifiable network sources.

While advanced attackers may use large residential proxy pools, many competitor-driven attacks rely on corporate networks, small ISPs, or reused proxy infrastructure.

Blocking these IPs or ASN ranges in real time delivers immediate value by preventing budget drain and protecting conversion data.

Although IP blocking is not a complete solution on its own, it is a proven foundational layer in a defense-in-depth strategy, particularly against low- to mid-effort competitor sabotage.

Why other defenses should complement IP blocking

Since competitors are constantly evolving, the most effective method to use IP blocking is to incorporate other elements:

1. Behavioral ML scoring: models that retrain on campaign-level variables and include explainability to audit decisions made by teams.
2. Device fingerprinting and cookie analysis: Link click activity to devices rather than individual clicks. This makes it harder for attackers who try to remain anonymous by changing IPs.
3. Platform cooperation and reporting: coordinate with ad platforms to report suspicious patterns of malicious actor accounts and publisher fraud; ad platforms can shut down problematic users and reimburse affected parties in certain instances.
4. Frequent forensic audits: Forensic audits of prominent campaigns and keywords often detect malicious attacks and refine filters with little to no effect on legitimate traffic.

Practical steps advertisers should take today

Based on recent research and marketing industry guidelines, advertisers can adopt this quick checklist:

1. Allow real-time filtering of suspicious IPs/ASNs: begin with persistent offenders identified across campaigns and top-spend keywords. This is the quickest method to eliminate obvious clicks from competitors or click farms.
2. Use instrument conversion events to create detection models: click events not followed by usual behavior are non-valuable clicks and higher risk.
3. Explainable machine learning models for the detection step: Ensure your team can explain why certain clicks were blocked.
4. Run regular forensics and share outcomes with platforms: identify malicious publisher domains, accounts, or vendor emails to help generate pressure for platforms to act.
5. Treat ad fraud like cybersecurity: there should be collaboration among the marketing, IT, and security departments because many types of fraud overlap with cybersecurity threats.

How ClickPatrol fits into modern competitor ad fraud detection

ClickPatrol addresses one of the most consistently validated defenses highlighted in research: preventing known competitors and fraudulent sources from seeing or clicking ads in the first place.

By blocking repeat offender IPs in real time, ClickPatrol protects budgets and preserves the integrity of conversion signals used by automated bidding systems.

Unlike reactive refund-based approaches, real-time blocking stops damage before it occurs, keeping campaign learning stable and performance metrics trustworthy.

Staying ahead of competitor ad fraud

Competitor ad fraud is growing more targeted and costly, but it’s also preventable. Research shows that proactive, real-time defenses outperform reactive refunds.

By blocking competitor IPs and combining this with layered detection, advertisers can protect budgets, preserve performance data, and ensure ads reach real, high-intent customers.

Don’t wait for refunds after the damage is done. Take control of your traffic today and keep your ads working only for real customers. Start protecting your campaigns with ClickPatrol now.

Abisola

Meet Abisola! As the content manager at ClickPatrol, she’s the go-to expert on all things fake traffic. From bot clicks to ad fraud, Abisola knows how to spot, stop, and educate others about the sneaky tactics that inflate numbers but don’t bring real results.

View all posts by Abisola (Abisola)

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