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What is Invalid Traffic and How Does It Impact Your Ad Campaign Performance?

Leave a Comment / Blog, Ad Fraud / mFilterIt Experts

Are you proactively analyzing the ad traffic of your campaigns? Is it really coming from genuine users or just being generated by bots? Yes, a significant portion of traffic that makes your ad campaigns seem successful could be invalid traffic. According to mFilterIt’s analysis featured in FICCI Report 2025, invalid traffic contributes to as much as 30–50% of activity across digital channels, directly distorting performance metrics and draining ad budgets. This means the performance you see on dashboards may not always reflect real user intent. Instead, it could be influenced by automated systems, proxies, or manipulated interactions that inflate impressions, clicks, and even conversions. In this blog, we break down what invalid traffic really is, why it’s increasing, differences between general invalid traffic and sophisticated invalid traffic, and how you can identify and mitigate its impact to ensure your campaigns deliver genuine results. What is Invalid Traffic? Why is it Increasing Rapidly? Invalid traffic simply means ad activity that doesn’t come from real users but still shows up as genuine impressions, clicks, or visits. This happens when bots or automated systems interact with ads, making it look like people are engaging when they actually aren’t. Over time, this traffic has become more advanced and harder to spot. Bots now easily mimic real user behaviour, such as browsing pages, scrolling, or clicking on ads. Moreover, developments in technology, AI usage, and advertising infrastructure also contribute to this. Here’s how: AI is making bots smarter Earlier, bots were easy to detect because they behaved like machines. Today, AI-powered bots can scroll, pause, click, and even mimic browsing patterns. Some can simulate entire user journeys, making fake engagement look real in analytics tools. The ad ecosystem has become more complex Modern advertising runs through multiple layers and channels, DSPs, SSPs, ad exchanges, networks, and resellers. This fragmentation creates blind spots, making it easier for low-quality or fraudulent traffic to enter without being noticed. Cheap infrastructure fuels large-scale ad fraud Server farms allow fraudsters to generate massive volumes of ad traffic at very low cost. What once required physical devices can now be scaled instantly using virtual environments. Limited transparency and visibility Limited transparency and visibility across the digital ecosystem make it harder for advertisers to verify traffic quality. With restricted access to detailed user-level data, identifying whether engagement is coming from real users or sophisticated invalid traffic becomes more challenging. As long as advertisers pay based on clicks or impressions, there’s always a chance for misuse. Fraudsters take advantage of this by generating invalid clicks or views to earn money, especially when proper checks are not in place. Therefore, detecting invalid traffic has become more important than ever. Invalid traffic is generally classified into two main types: General Invalid Traffic (GIVT) and Sophisticated Invalid Traffic (SIVT). Two Major Types of Invalid Traffic Invalid traffic is broadly classified into two categories based on how complex the fraud technique is. What is General Invalid Traffic (GIVT)? It refers to non‑human or automated interactions that inflate ad metrics, caused by easily identifiable bots, spiders, or crawlers. These bots typically do not attempt to mimic real human behavior. They’re not malicious in intent but can distort campaign reporting and waste ad spend due to their automated nature. Because the patterns are predictable, platforms and verification tools can often identify and block this traffic using ad fraud detection techniques. Here’s what we observed in one of the campaigns . VPN and proxy traffic contributed 12.4% of total activity, indicating that a significant portion of traffic was not genuinely coming from real users. Several visits appeared to come from genuine mobile users but traced back to VPN and proxy networks, that were being used to hide the real user’s location. A deeper analysis showed that these IP addresses were linked to data center hosting providers (DCH) instead of real user networks. What is Sophisticated Invalid Traffic (SIVT)? Sophisticated Invalid Traffic (SIVT) refers to advanced forms of fraudulent or non-genuine traffic that are designed to look like real user activity. Unlike basic invalid traffic, these methods use automation, scripts, or manipulated devices to mimic real behavior, making them harder to detect with simple filters. Here are some sophisticated invalid traffic techniques we have observed in various campaign analysis: Sample Observation 1: In this case, the sophisticated invalid traffic technique that is being used is pop-under activity. It occurs when a website opens in the background instead of the active browser tab, meaning the user does not actually see or interact with the page. This is what we observed: the page loaded behind the main window and showed no real user interaction, indicating artificially generated visits. This type of activity is used to inflate traffic numbers without genuine engagement, and here, pop-under traffic contributed about 6.1% of the total traffic. Sample Observation 2: This shows a case of device spoofing, where traffic pretends to come from a real mobile device. Normally, smartphones support touch actions like tapping, scrolling, or pinch-zooming. However, in the above data, some devices marked as mobile showed “Not Standard” touch support, meaning these normal mobile features were missing. This suggests that the devices were not real smartphones but simulated environments or automated systems pretending to be mobile users. In this analysis, device spoofing made up about 2.7% of the traffic, indicating automated activity trying to appear like real user interactions. Sample Observation 3: Server farm-driven activity contributed 3.2% of total traffic, highlighting the presence of sophisticated, non-human interactions. In this case, traffic appeared to come from mobile devices across different sources. We noticed very high hardware concurrency values (192 and 96) from devices shown as mobile. Hardware concurrency simply means how many tasks a device can handle at the same time. Normal mobile phones can only run a limited number of tasks, so numbers this high are unusual for real users. This suggests that the traffic is likely coming from multiple channels, like bot farms, proxy-based execution, and automated browsers instead of real mobile devices. These systems are

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How Fraudsters Bypass MMP Detection

Blog, Ad Fraud / mFilterIt Experts

Mobile Measurement Partners (MMPs) have long been the industry’s first line of defence against mobile ad fraud. Through SDK integrations and last-click attribution, they have helped brands track installs and flag suspicious activity based on known patterns such as: Unusual install spikes Abnormal click-to-install times Repetitive device IDs While this works well for obvious fraud, the challenge today is far more sophisticated. Fraudsters now mimic normal user behaviour, making fraudulent traffic look genuine. In many cases, they have effectively reverse-engineered MMP detection logic and learned how to stay within acceptable thresholds. By carefully blending different traffic types in calculated proportions, bad actors are able to pass MMP checks and continue draining campaign budgets unnoticed. This is why the common concern today is clear: MMPs catch obvious fraud but often miss blended fraud. In this blog, we cover: Why MMPs struggle to catch blended traffic How mixed traffic gets a green signal in campaigns How brands can protect themselves beyond basic MMP checks Why MMPs Struggle to Catch Blended Traffic MMPs are designed to detect fraud using known red flags such as unusual click-to-install times, or repetitive user behavior. But today’s fraudsters have become smarter. Instead of sending clearly fake traffic, they mix fraudulent activity with genuine users so that nothing looks suspicious at first glance. This makes the traffic appear legitimate on the MMP dashboard, while budgets continue to get quietly drained in the background. Bot Traffic – Hiding Behind Volume Bots generate large volumes of clicks and fake installs, creating an illusion of strong campaign activity. When this fake traffic is mixed with real users, the overall data starts to look normal. Click and install ratios are high where one click is followed by one install hence time patterns seem balanced, device IDs appear varied, and nothing stands out as an obvious anomaly. Because MMPs are typically built to detect extreme outliers, this blended fraud often slips through unnoticed. Last-Click Attribution – Stealing Credit for Real Installs In fraud tactics like click spamming and click injection, fraudsters either flood the system with fake clicks or place a click just before a real user completes an install. This helps them hijack last-click attribution and steal credit for a conversion that should go to a genuine source. Since the install itself is real, the MMP often treats it as genuine. The fraud happens at the click stage, which many surface-level detection models fail to catch effectively. Incentivised Traffic – Real People, Misleading Results This is one of the hardest forms of fraud to detect because it involves real people. Users are paid or rewarded to install an app, so all the signals look human; real IP addresses, normal device behavior and natural session activity. To an MMP, this traffic appears completely clean. The problem usually becomes visible only later, when retention and engagement suddenly drop after the campaign budget has already been spent. Read in detail about device fraud How the Data Exposes the Evasion, MMPs Cannot Detect The data below highlights findings from a campaign ran between Sept–Oct 2025, where bot traffic was mixed with organic installs, making it difficult for MMPs to separate real activity from fraudulent traffic. Here’s what the data shows: The conversion rate gap is the clearest proof of hidden invalid traffic: The top source, publisher 1, shows conversion rate falling from 0.24% to 0.10% after bot traffic is removed, meaning nearly 58% of the apparent performance was artificial uplift. Massive click volume is creating a false sense of scale: Publisher 8 delivered 816M clicks, but its clean CVR drops to just 0.02%, huge activity on paper, but almost no genuine conversion value. Strong reported CVR can still hide severe bot contamination: Publisher 11 appears to be a top-performing source with 0.63% reported CVR, but once bots are removed it drops to 0.18%, with 72% bot share, indicating invalid traffic driving the most performance. Bot-heavy traffic is not an outlier – it is widespread: 7 out of 10 visible publishers show bot share above 60%, including sources like publisher 5 (68%), publisher 9 (70%), and publisher 10 (70%), despite all of them marked as clean by MMP. Even mid-volume sources show inflated performance: Publisher 7 drops from 0.20% to 0.08% CVR, while 61% of its traffic is bot, showing that inflation is not limited to only the largest traffic sources. The most dangerous fraud isn’t what MMPs catch, it’s what they don’t. Understanding the evasion tactics is the first step to building detection that actually keeps up. How Brands Can Protect Themselves from Mixed Traffic Beyond Basic MMP Checks MMPs should be treated as the first layer, not the only layer. An independent layer of validation through mobile ad fraud solution like mFilterIt’s empowers brands against mixed traffic, catching sophisticated tactics that MMPs miss – Recover stolen conversions through full click-path validation: Move beyond last-click attribution to validate the complete user journey from first touch to install so click hijacking and attribution theft are identified before they drain budgets. Pinpoint fraud sources with publisher-level transparency: Gain granular visibility down to publisher, sub-publisher, placement, and traffic source level to isolate hidden fraud pockets and eliminate waste at the source. Improve acquisition quality by validating real user intent: Go beyond app install counts and measure retention, session depth, registrations, and purchase signals to separate genuine users from low-intent or incentivised traffic. Surface blended fraud early with CVR and traffic anomaly detection: Monitor conversion gaps, abnormal click bursts, and sudden traffic mix shifts to detect bot-driven or manipulated traffic before it impacts performance metrics. Conclusion Blended fraud is changing the way brands need to think about campaign validation. What once appeared as a reliable defence layer is now being challenged by fraud tactics that are far more calculated and harder to spot. This makes it critical for brands to look beyond standard MMP signals and adopt deeper monitoring frameworks like mFilterIt’s Valid8 that can uncover hidden manipulation across clicks, installs, and post-install behaviour. In a high-investment digital ecosystem, protecting media spends is no longer just about catching obvious fraud, it is about identifying the traffic that is intentionally built to look real. FAQs How do fraudsters bypass MMP detection? They mix fake traffic with real user activity, making fraud look genuine and harder for MMPs to detect. Why do MMPs miss blended fraud? Because blended fraud mimics normal user behaviour and stays within acceptable detection thresholds. What are click injection and click spamming? These are fraud tactics that use fake clicks to steal credit for genuine app installs. How can brands detect

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$14.8B at Stake: Will You Let Cookie Hijacking Slip Through?

Blog, Ad Fraud / mFilterIt Experts

The U.S. affiliate marketing industry is entering a new phase of scale. It is crossing the $10 billion mark for the first time, up from $9.1 billion in 2023, and projected to reach $14.8 billion by 2028. With giants like Amazon, Walmart, and Target running large, complex affiliate programs, the stakes have never been higher. But as the channel grows, so does the race to claim commissions (sometimes wrongfully) which can also look like this-imagine a shopper comes directly to your website, ready to buy but yet somehow, a third-party partner ends up taking creadit for that sale. Many brands are already trying to tackle it, but the growing sophistication of the tactic makes it increasingly difficult to control. In this blog, we dive into a sophisticated tactic known as cookie hijacking where affiliate cookies are secretly inserted into a user’s browser to claim credit for organic traffic, ultimately stealing conversions that rightfully belong to the brand. Behind the Scenes of Cookie Hijacking: 3 Tactics You Might Be Missing Here are three common ways affiliates cause cookie stealing to hijack organic traffic: Extensions Injecting Cookie Affiliates driving sales by influencing real customers is ideal but them stealing is not. One common way an affiliate program experiences this issue is through cookie hijacking. While analyzing a leading global e-commerce platform, we found that many users were directly visiting the site to make purchases. However, some had browser extensions installed (like coupon or deal tools) that silently triggered affiliate links in the background, without any click or user consent. As a result, when the purchase was completed, the system attributed it to an affiliate. Since most tracking follows a “last click wins” model, the affiliate whose cookie was dropped last received the credit, despite having no real influence on the sale. Auto-redirect with Affiliate Tag Another way of cookie hijacking that we noticed in the same brand’s use case was, the page as when users were redirected to brand’s website. If a user is browsing normally and visits a random page (this could be a shady site, popup, or even hidden script). The page quickly redirected them to brand’s site and in a split-second redirect, an affiliate cookie is dropped silently. When that user makes purchase, the credit is given to the affiliate as system sees the cookie. Forced cookie from an external site A user visits a completely unrelated website, not your brand’s. In the background, that site quietly drops an affiliate cookie without the user clicking anything or showing any intent. Sometimes, the user is even redirected to your website, making it look like a normal visit. Later, when they make a purchase, the affiliate gets credit, simply because their cookie was already placed earlier. How Can You Safeguard Your Brand From Cookie Hijacking Cookie manipulation is a growing risk for U.S. brands, especially those running large-scale affiliate programs. As partner ecosystems expand, having clearer visibility becomes essential to avoid affiliate fraud and protect genuine performance. Legacy, surface-level tools can highlight obvious issues, but the real question is whether they can keep up with increasingly sophisticated fraud tactics. In most cases, they fall short. And for U.S. brands running high-stakes affiliate programs , uncertainty isn’t something they can afford. With a more advanced, third-party approach like mFilterIt’s, renowned brand are already bringing more transparency to their affiliate marketing programs. Here’s how it empowers brands- Launch instantly, stay in control – No integrations needed, just immediate visibility into your affiliate ecosystem Gain complete transparency – Always-on scanning ensures you see every leakage, not just the obvious ones Expand your risk coverage – Protect your brand from both known partners and unknown bad actors Make decisions with confidence – Accurate, low-noise insights you can actually act on Hold the right partners accountable – Clear attribution helps you take precise, effective action Understand your true customer journey – See exactly how users reach and convert on your platform Protect revenue in real time – Identify and stop fraud before it impacts your bottom line Conclusion The key to a smooth affiliate program is visibility to understand real user journeys and know where attribution is coming from. Brands that focus on transparency and proactive monitoring through holistic ad fraud solution can prevent revenue leakage and build stronger, more reliable affiliate programs. FAQs What is cookie theft? Cookie theft is when someone steals a user’s cookie or places their own cookie in the user’s browser to wrongly take credit for a purchase they didn’t influence at the first place. How to prevent cookie hijacking? Monitor affiliate traffic and user journeys closely Block suspicious extensions, redirects, and unknown sources Validate partners and enforce strict program rules Use advanced tracking/monitoring tools for better visibility Why is cookie hijacking difficult to identify? Cookie hijacking is difficult to identify because it often happens silently in the background. Since the user still completes a genuine purchase, the fraud appears legitimate in standard attribution systems, making it harder for legacy tools to flag. What are the common signs of cookie hijacking in affiliate programs? Common signs include sudden spikes in conversions, abnormal click patterns, high traffic from unknown sources, and mismatched user journeys that indicate unauthorized tracking activity What impact does cookie hijacking have on attribution and commissions? Cookie hijacking manipulates attribution by assigning credit to fraudulent affiliates, leading to incorrect commission payouts and reduced returns for genuine marketing efforts. What compliance measures help prevent affiliate fraud in the US? US advertisers can enforce strict affiliate policies, conduct regular audits, use fraud detection tools, and follow FTC guidelines to ensure transparency and prevent fraudulent activities.

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What 756+ Million OTT Ad Requests Revealed About Where Media Budget Really Goes

Blog, Ad Fraud / Shiraz Noor

OTT advertising seems to be a safe bet right now. Brands are moving serious budgets here to reach a wider segment of audience at once. In 2025, 28% of total digital ad spend in India was heavily driven towards OTT platforms and video content. (Source: Exchange4Media) But what if we told you that the audience pool that you are reaching right now is limited? The numbers that you see on your dashboard are not always true. This is exactly what came to light during a recent campaign analysis we conducted for a large automotive brand running video ads across two of India’s leading OTT platforms. We went beyond what the platform reported and validated what was actually happening at the delivery level. Over 756 million ad requests were reviewed across three months. Here’s what the data revealed, and what it means for every marketer running branding campaigns on OTT today. The Scale of the OTT Advertising Campaign & Why It Matters The campaign ran across two major OTT platforms simultaneously, covering both CTV and mobile inventory. It covered multiple brand lines, from regional language campaigns tied to popular content properties, to national-market brand pushes. In total, over 756 million ad requests were reviewed during the assessment period. Across Platform A, 1.18% of ad requests were blocked before delivery. However, the figure was significantly higher at 7.41% for Platform B. This gap between the two platforms is not incidental. It reflects differences in inventory quality, frequency capping behavior, and bot traffic patterns. Finding 1: Frequency Capping Violations – The Reach Problem Hiding in Plain Sight A frequency cap exists for two reasons: To protect the viewer from ad fatigue To protect the advertiser from burning budget on an audience that has already been saturated. When it is not enforced at the delivery level, both goals fail simultaneously. In this campaign, frequency overshoot was the single largest driver of blocked impressions, particularly on one of the two platforms, where it ran as high as 8.32% in a single month. At the device level, the problem was even more stark. A single CTV device was found to have accumulated 711 ad requests over a span of just 10 days, against a defined frequency cap threshold of 3 impressions per device. Multiple other devices on the same campaign showed repetition counts ranging from 245 to 510 requests across the same period. Action Taken to Prevent Frequency Capping Breaches Every ad request was evaluated in real time against the predefined frequency capping before the impression was served. When a device had already crossed its exposure limit, the ad request was blocked automatically. Impact Impression delivery shifted from repeatedly exposed devices to a new audience base. Budget was redirected toward incremental reach. Reach distribution became more balanced across devices. Every counted impression met the defined frequency and traffic quality thresholds Finding 2: Brand Safety – What Content Were the Ads Actually Running Against? Brand safety on OTT is not a binary condition; it depends on what specific content a particular ad placement is running against, and whether anyone is actually checking. During this campaign, content-level placement analysis was conducted using Video ID signals available from the platforms. It revealed that a portion of ad impressions were being served alongside content that no automotive brand would knowingly approve. Specific placements were identified and blocked that fell into the brand unsafe content categories: 1. Obscenity & Profanity: Adult content classified under the GARM video safety framework 2. Crime & Harmful Acts: Films with depictions of violence and criminal activity 3. Arms & Ammunition: Content featuring weapons as a central theme 4. Illegal Drugs: Content involving drug-related imagery These were not obscure placements on low-quality inventory. They were identifiable content URLs on mainstream OTT platforms, surfaced through systematic placement-level analysis. Action Taken to Prevent Ad Placements Besides Unsafe Content Each placement was analyzed based on text, frame-by-frame classification, and GARM-aligned video-level analysis. Once categorized as brand-unsafe, impressions associated with those placements were blocked from delivery. This ensured that the Impact Brand ads appeared only against content that met its defined safety standards. No brand-unsafe impressions were counted as delivered. Brand’s media team received verifiable assurance, not just a platform-level declaration. Brand integrity was protected at the most granular level possible Finding 3: Invalid Traffic – The Bots That Looked Like Genuine Viewers Invalid traffic on OTT does not look like a flood of suspicious clicks. In this campaign, it showed up in three distinct forms. 1. Outdated OS signals: Devices running Android versions 5.0, 5.1, and 6.0 were generating ad requests in December 2025. These are operating system versions that are years past their support lifecycle. 2. Outdated browser signals: Smart TV devices were detected running browser versions from nearly a decade ago, like Chrome 53 and Chrome 68. In-use CTV devices do not carry browser fingerprints this outdated. These signals point clearly to spoofed or manipulated device identities. 3. Data Center IP activity: A subset of traffic was traced to IP addresses belonging to data centers and VPN infrastructure providers. These IPs were routing traffic to mimic genuine viewer behavior, appearing to originate from real residential locations while actually passing through commercial data center networks. Action Taken to Reduce Bot Traffic Each signal was evaluated in real time as part of the VAST-level ad traffic validation process. Requests carrying bot traffic indicators were flagged and blocked before an impression was served. Impact On Platform A, invalid traffic stayed between 0.55% and 0.67% across the quarter. On Platform B, despite higher inventory variability, IVT was actively contained through continuous real-time filtering. Zero IVT-affected impressions were passed through as billable delivery across either platform, every impression that was counted was a genuine one. As a result, once all three layers of validation were in place, the campaign delivered exactly what it was planned to. Viewability held above 92% throughout the quarter. Geographic delivery aligned closely with targeting intent; regional campaigns delivered impressions in their intended language markets. Moreover, CTV advertising accounted for nearly 99.9% of delivery across both platforms, confirming the campaign was genuinely reaching the living room screen it was built for. Why is Ad Traffic Validation Non-Negotiable for OTT & CTV Campaigns? Frequency violations, unsafe placements, sophisticated invalid traffic – these patterns exist across OTT campaigns broadly. They go undetected simply because advertisers often don’t look at the right layer of data. Here’s what mFilterIt’s proactive ad traffic validation solution – Valid8 makes possible for brands: Ensures your frequency cap is actually working, not just set Enforces frequency cap thresholds at the device level, so before the impression is served, overexposure is stopped before it costs you, not

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Are You Competing Against the Market or Against Your Own Affiliates?

Blog, Ad Fraud / mFilterIt Experts

Affiliate programs are a powerful revenue driver and bring undeniable scale and performance to the table. It’s no surprise that brands continue to increase their investment in the channel. Global affiliate marketing spend is expected to reach $17B in 2025 (up from $15.7B in 2024) and is projected to surge to $38.35B by 2030. (Source) But as investments rise, one question remains: how deeply is this performance really being evaluated? Nearly 22–30% of digital ad spend (Source) is lost to invalid traffic or fraudulent activity and affiliate campaigns are one of the easiest places for it to hide. The affiliate ecosystem is revenue-driven but complex with multiple partners involved and that makes it more vulnerable to performance leakages. When some partners take credit for users you already acquired organically, you unknowingly start competing with your own growth. You know your external competitors. What you don’t see is the partner within your own ecosystem quietly draining your ad budget. These bad partners not only impact you but also steal the credit of genuine partners, impeding their growth. Sounds like a big claim? Let’s uncover it. Steady Growth or Midnight Spikes? What Affiliate Data Is Telling You Your genuine affiliate partners will show a steady and explainable growth pattern. The installs and traffic driven by them will not be restricted to a specific time window or sudden spikes. Instead, you will see natural variations; some days higher, some lower based on seasonality, campaign activity, and normal user behaviour, making the performance look realistic and trustworthy. Whereas, in case of fraudulent affiliates, you will notice a sudden spike in the number of installs. The user journey will not be mapped, and apps can get installed on always-on basis especially during the times when no normal person will install your app (3-4 am). From a marketer’s perspective, sudden out performance without clear explanation often signals inflated or manipulated metrics, not real user acquisition. The graph below shows the exact odd-hours spike happening at peak night where y-axis highlights the install rate and x-axis, the time in hours. How is Wrong Affiliate Intervention Rewriting your Growth Story? You built a strong affiliate network but what if it is rewriting your growth story? Affiliates that do not bring valid traffic and yet win the attribution race are actually not contributing to your ROI. Here’s what the wrong affiliate intervention looks like – This data of 7 days indicates campaign performance of various affiliates. In just seven days of campaign data, the gap between clicks and installs shows major discrepancies. One partner alone generated 29.03 million clicks but delivered only 45,501 installs, an extremely low 0.16% click-to-install rate while others also failed to cross even the 1% install rate mark. On the surface, the program appears to be scaling through massive traffic, but in reality, the growth narrative is being shaped by inflated clicks rather than real users, distorting performance, budgets, and optimization decisions. From Attributed Performance to Real Incrementality: The Shift You Need This time, you are not required to increase the budget of affiliate programs, instead what you require is a comprehensive approach that provides right attribution to deserving partners, cutting noise of fraudulent affiliates. Here’s how mFilterit’s holistic ad fraud solution Valid8, empowers your brands with an added layer of attribution integrity – Eliminate odd-hour install spikes by closely monitoring the full user journey and identifying suspicious patterns at the source level before they drain your budget. Demand true source-level transparency to shift budgets toward partners delivering genuine installs and cut spend on hidden, low-quality traffic sources. Detect traffic quality issues and behavioural anomalies early to optimise campaigns toward high-intent users instead of inflated performance numbers. Automate blocking, protect payouts, and optimise partner performance to reduce wasted spend, safeguard ROI, and scale confidently with partners that truly drive results. How We Tracked Down IVT: Saved $1.3 Million in Just 3 Months ? For a major travel portal running performance campaigns to acquire new customers, the problem wasn’t the budget, it was the lack of visibility into where the traffic was actually coming from. Despite healthy spending, the brand could not clearly distinguish between genuine and low-quality affiliate sources. We stepped in and closely monitored affiliate performance across the program. By identifying the partners driving fraudulent and non-incremental activity and stopping payouts to them, the brand ensured that only genuine contributions were rewarded. As a result, it was able to save up to $1.3 million in just three months while bringing back control over its performance spend. Conclusion The last thing you must worry about while running an affiliate program is to fight against your own affiliates. Affiliate marketing program are not the problem; the real opportunity lies in making them work the way they are meant to. To unlock their true incremental value and eliminate dishonest contributions, brands need to evaluate the entire affiliate journey, not just the final attribution. Only then they can fight affiliate marketing fraud and reward genuine partners, stop performance leakages, and turn the channel into a reliable, growth-driving engine. Want to know how? Schedule a call! FAQs How Can You Tell If An Affiliate Is Driving Real Growth? Real affiliates show consistent, natural performance trends. Sudden install spikes, odd-hour conversions, or a big gap between clicks and installs are signs of non-incremental or low-quality traffic. Why Do Affiliate Programs Sometimes Waste Ad Budget? Because last-click attribution can reward partners who didn’t create real user intent, brands end up paying for users they would have acquired organically, leading to inflated metrics and lower ROI. How Can Brands Stop Affiliate Fraud And Protect Roi? By analysing the full user journey, identifying traffic sources, and rewarding only genuine incremental conversions while blocking invalid partners and payouts. What are the signs of affiliate fraud in campaign data? Common signs include sudden traffic spikes, odd-hour conversions, unusually high clicks with very low installs, poor click-to-install rates, and conversions from suspicious referral sources. These patterns often indicate invalid traffic or attribution of manipulation. How can brands audit their affiliate partners effectively? Brands can evaluate affiliate partners by reviewing traffic quality, conversion behaviour, referral sources, and adherence to program guidelines. Ongoing

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How to Know If Your Campaign is Affected by Ad Fraud: 5 Signs Marketers Often Miss

Blog, Ad Fraud / mFilterIt Experts

Bot traffic is taking up more than half of the internet traffic. Out of which, 37% of the traffic is driven by bad bots. (Source: Imperva) And this bot traffic is beyond just inflated activity. Sophisticated ad fraud techniques penetrate the funnel, impacting not just analytics but end goals like sign-up, purchase, etc. They can bypass basic ad fraud detection methods easily, mimicking human-like behaviour. It skews the data, further impacting decision-making, conversion rates, and retention across mobile and web campaigns. That is why it is important for advertisers to know about not just surface-level signs of ad fraud but also the sophisticated indicators. In this article, we’ll break down some of the signs of ad fraud that we have observed in the campaign analyzed. Let’s dig in. What Differentiates Sophisticated Ad Fraud Techniques from Basic Bot Traffic? Basic bot traffic is easier to detect. It often creates visible spikes like traffic coming from locations outside the targeted region, same devices, unrealistic click volumes, or abnormal engagement patterns. On the other hand, sophisticated ad fraud is different. Instead of obvious anomalies, it mimics human-like behaviour. The manipulation happens inside patterns that are harder to identify and detect: OS distributions, CTIT inconsistencies, imperceptible ad placements, IP clustering, or traffic coming from incent fraud. Basic bots inflate numbers. Sophisticated fraud impacts performance intelligence. That is what makes it more dangerous. It does not just waste budget. It influences optimization decisions, attribution models, and scaling strategies, without triggering immediate suspicion. Therefore, understanding this difference is the first step toward detecting it. Now, let’s have a look at some of the sophisticated ad fraud signals. Sign 1: Heavy install coming from older Android OS versions Fraudulent affiliates using bots and emulators running on older Android OS versions to generate fake app installs. After comparing OS version install distribution across different traffic sources: Google installs were spread across multiple OS versions (10-16), reflecting a healthy and natural user base. However, two affiliate partner sources revealed a very different pattern. Partner A and Partner B showed a heavy concentration of installs on OS 12, 13, and 14 While the benchmark (Google) traffic was distributed more broadly across OS 10–16 The mismatch clearly indicated emulator-based or bot-driven installs. Sign 2: Google Play installs happened before the user clicked on an ad Click-to-install time (CTIT) measures how long it took a user to install an app after clicking on an ad. Naturally, an app install takes up to minimum 20-30 seconds. However, in one of the campaigns we noticed app installs taking place even before the users clicked on an ad, resulting in negative CTIT. This is a clear indicator of mobile ad fraud. Therefore, extremely short or negative click-to-install time indicates click injection. If your CTIT distribution doesn’t resemble a natural curve, it’s worth investigating further. Know how. Sign 3: Inflated Installs Coming From Incent App In one of the campaigns, we observed that a telecom provider was unknowingly running ads on an incent app. Users were redirected through a shared link, asked to install the app, and complete specific steps to earn rewards. This resulted in a high number of installs, but the actual engagement remained low. The majority of users completed the required action only to earn coins and did not return. This clearly indicated incentive-driven traffic rather than genuine user acquisition. Read this to know about incent apps and low-quality traffic in detail and how advertisers can protect their mobile app campaigns. Sign 4: Invalid Traffic Coming From Imperceptible Window In one of the web campaigns, 99% of traffic was coming from an imperceptible window (also known as pixel stuffing ) through a specific publisher source. This means the ad was technically loaded in 0x0 iFrames, but not actually visible to users. Although impressions and traffic volumes appeared normal, user engagement metrics clearly indicated non-human behavior. Analysis revealed: Repetitive browser agent across sessions Over 70% of data originating from a single IP cluster Zero scroll activity and no sales generated This means advertisers must check not just if the ad was delivered but also if the ad was actually viewable. We have broken down how to move beyond the viewability myth. Check it out here. Sign 5: Repeated Ip Traffic From The Same Subnet (Invalid Traffic Pattern) In genuine campaigns, IP addresses are typically distributed across diverse networks. But what we observed was different. At first, the traffic appeared strong. But on deeper evaluation of IP-level data, we found that a large portion of clicks and visits were traced back to a single IP subnet. Each IP was generating more than 70+ clicks, consistently inflating traffic. The concentration of activity within a contiguous subnet suggested coordinated or automated behavior rather than random user traffic. If a significant share of your traffic is coming from closely grouped IP ranges. especially those flagged under VPN or proxy networks, it requires immediate audit. Volume alone does not indicate performance. Source diversity does. How Can Advertisers Identify Sophisticated Bot Traffic? Detecting sophisticated ad fraud requires moving beyond surface-level indicators. Here are key actions advertisers should take: Analyze Deeper Behavioral Patterns Validating only surface-level signals like clicks and installs is not enough. You need to monitor click-to-install timing distributions, engagement depth beyond first interaction, repeat device and IP behaviour, etc. These patterns uncover anomalies that standard filters miss. Benchmark Across Trusted Sources Compare partner traffic against known clean channels, ecosystem adoption trends, and natural engagement ranges. Discrepancies from benchmarks often reveal non-genuine or invalid traffic behaviour. Validate Before Scaling Budgets Campaign scaling should never happen without ad traffic validation. High volume doesn’t mean high value. Invest in tools that provide real-time ad fraud detection, cross-source transparency and analysis, alerts for sophisticated patterns with proofs and in-depth understanding of new emerging patterns as well. At mFilterIt, our ad fraud detection tool – Valid8, helps detect ad fraud signals that ad platforms and MMPs often overlook. They also allow you to: Understand true user intent Exclude invalid traffic before optimization

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15% of Ad Impressions were Exceeding Frequency Capping: Here’s How We Fixed It in a CTV Campaign

Blog, Ad Fraud / Shiraz Noor

Your ads are getting delivered, but to a limited audience pool. This is what we recently saw in one of the campaigns running on CTV platforms. While impression delivery appeared strong for this Indonesian brand, engagement metrics did not align proportionately with campaign spend. On deeper analysis, it was observed that ad impressions were being served repeatedly to a limited set of devices instead of expanding to new viewers. This indicated a potential frequency capping breach, where ads were being delivered beyond the defined exposure threshold. Impact? Poor campaign efficiency and ad fatigue. So, if you are an advertiser running OTT & CTV advertising campaigns, or your audience is experiencing something similar, this is must-read. Deep-Down to Identify the Problem Throughout the campaign, 6.02 million ad requests were evaluated through VAST-level validation signals. What the Data Revealed About Frequency Capping Breach The evaluation uncovered that: 15.86% of impressions were exceeding the defined frequency cap A total of 16.47% delivery was prevented, combining frequency capping breach and invalid traffic filtration Over 950,000 impressions were blocked due to frequency violations alone. Certain Smart TV device IDs generated thousands of repeated ad requests within short time windows In one instance, a single device triggered over 7,600 ad requests in a single day, clearly indicating abnormal repetition behavior. Additionally, a small portion of traffic (0.61%) was linked to data center and VPN-based IP activity, pointing toward advanced traffic manipulation patterns. The Action Taken To address this, real-time frequency validation was implemented at the VAST integration level. Every ad request was evaluated against the predefined frequency threshold before delivery. If a device had already crossed the limit, a no-ad response was triggered, preventing further exposure. Repeated device patterns and abnormal request spikes were filtered out without impacting legitimate delivery. This ensured that exposure remained controlled and aligned with the campaign’s intended frequency settings. The Measurable Impact After filtration and enforcement: 691,691 impressions were validated and served cleanly. Video engagement remained strong. These results demonstrated that once frequency capping was enforced and invalid traffic was removed, genuine viewer engagement remained stable and healthy. More importantly, reach distribution improved, budget wastage was reduced, and exposure became more balanced across devices. What This Means for OTT & CTV Advertisers To prevent frequency capping breaches, simply setting up a frequency cap is not enough. What matters is whether that cap is actively enforced at the moment of ad delivery or not. mFilterIt ensures that ad exposure remains controlled, balanced, and performance-driven through real-time frequency governance. Here’s how advertisers can benefit: Ensures Proactive Enforcement Of Frequency Caps By validating ad requests before they are served, exposure thresholds are actively monitored and enforced. This prevents ad impressions from exceeding defined limits and ensures campaigns remain compliant throughout their lifecycle. Prevents Impression Wastage On Limited Devices Device identities are analyzed to monitor how frequently a specific device has been exposed to an ad. By tracking repetitions at the device level, advertisers can clearly identify when impressions are being served within a limited audience pool and take corrective actions accordingly. Maintains Clean Reach By Combining Frequency & Traffic Quality Ad frequency overshoot can sometimes overlap with invalid traffic signals. By evaluating both exposure limits and traffic quality together, mFilterIt ensures campaigns maintain clean reach without inflating ad impressions through excessive or abnormal delivery. Protects Viewer Experience With Balanced Exposure By maintaining balanced exposure levels, advertisers can ensure that audiences are not overwhelmed by repeated messaging. This helps create a more relevant and engaging viewing experience while preserving brand perception across OTT and CTV environments. Enables Smarter Campaign Optimization Insights from frequency analysis allow advertisers to refine targeting strategies, adjust exposure thresholds where necessary, and improve distribution efficiency. This ultimately supports stronger reach expansion and better use of media investment. Conclusion OTT and CTV advertising is built to deliver premium, high-impact brand moments. But without proactive validation, campaigns generate ad impressions within a limited device pool, restricting reach and draining budget on repetitive exposure. Advanced frequency capping using ad traffic validation solution is a performance safeguard. With this approach, brands can protect reach, maintain engagement quality, and ensure that budget is directed toward incremental audience expansion, not overserving the same devices. If you want your branding campaigns to deliver genuine impressions, balanced reach, and measurable ad viewability across OTT and CTV advertising, it’s time to move beyond settings and into enforcement. Connect with our experts to know more! FAQs What Is Frequency Capping? Frequency capping refers to the maximum number of times an advertisement should be shown to a user or device within a defined time frame. For example, a brand may decide that a viewer should not see the same ad more than three times per day to maintain optimal exposure without causing ad fatigue. What Is Frequency Capping Breach? A frequency capping breach occurs when an ad is shown to the same user beyond the predefined limit. Moreover, this can happen even when you’ve set a frequency cap in your ad manager, due to platform-level inconsistencies, device-level repetition, or synchronization gaps across systems. How Are Frequency Caps Configured? Frequency caps are typically configured based on: Campaign Publisher Geography Time duration (daily, weekly, monthly) Why Does Frequency Capping Matter? Effective frequency capping ensures that users aren’t served the same ad too many times within a short period. This enhances ad campaign performance, effectiveness, prevents irritation, and maintains a positive viewing experience. However, finding the right balance requires data-driven decision-making, continuous testing, and collaboration with advertising platforms.

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How to Identify Affiliate Fraud: Key Signs, Impact & Prevention Strategies

Blog, Ad Fraud / mFilterIt Experts

Consider a fast-growing ecommerce brand with strong organic traffic and a well-run affiliate program. Revenue looks solid every month, but one odd trend appears: a mid-tier affiliate suddenly becomes the highest contributor, while trusted, high-quality partners stay flat. At first, it feels like a performance win. However, a closer look reveals the truth. Most of those “affiliate-driven” traffic was from users who were already interested to buy from the brand. At the last moment, the credit shifts to the affiliate — even though they didn’t bring in a new customer. To burst this bubble, focus on what really adds value. In this blog, you will discover – The real-world signs of affiliate fraud How to detect it using actionable data signals And how to prevent it without hurting scale or genuine partners Signs to Identify Affiliate Fraud in Programs Brands running affiliate marketing programs can spot key warning signs triggered by fraudulent activity, understand the mechanisms behind them, and uncover what these indicators truly reveal – Unusually high clicks with low engagement or conversions What it is: Campaigns receive a high number of clicks but very few real actions like sign-ups, purchases, or engagement. How it happens: This is usually caused by click spamming, bot traffic, or forced redirects that create fake or unintentional clicks. What it indicates: Artificial traffic inflation aimed at organic hijacking, manipulating attribution and making performance appear better than it actually is. Inflated installs with distorted click-to-install ratios What it is: High install volumes paired with unusually short click-to-install times or irregular conversion paths. How it happens: Driven by click injection techniques that hijack organic or paid traffic at the last moment. What it indicates: Attribution manipulation and conversion theft from legitimate marketing channels. Abnormal growth from a small group of affiliates What it is: A few affiliates show sudden, disproportionate growth while overall program performance remains flat. How it happens: Often due to last-click hijacking of organic and paid installs What it indicates: Skewed performance reporting and possible conversion stealing rather than incremental growth. Sudden spikes in installs from limited device models, OS versions, or IP ranges What it is: High volumes of activity originating from a narrow set of technical identifiers. How it happens: Generated using device farms, emulators, or automated traffic systems. What it indicates: Non-human traffic rather than genuine user acquisition. Installs originating from unauthorized or unverified sources What it is: App installs coming from unofficial app stores, third-party APKs, or unknown publishers. Why it happens: APK tampering or manipulated distribution channels. What it indicates: High risk of fraud, poor user quality, security vulnerabilities, and low lifetime value. Sharp spikes followed by rapid drops in activity and retention What it is: Sudden bursts in installs or sign-ups that collapse shortly after. Why it happens: Incent-based campaigns that attract reward-seeking, low-intent users. What it indicates: Artificial scale that fails to generate long-term engagement, retention, or revenue. High volume of users completing only minimal actions What it is: Users perform just enough actions to trigger payouts and then disengage. Why it happens: Incent fraud, forced actions, or scripted behavioral flows. What it indicates: Low-quality acquisition that inflates metrics but delivers no sustainable business impact. Traffic spikes during odd hours or irrelevant geographies What it is: Large traffic volumes (including clicks and impressions) coming in at unnatural times or from low-relevance regions. Why it happens: Bot networks, proxy servers, or geo-masking fraud operations. What it indicates: Automated or manipulated traffic designed to bypass detection. High uninstalls or drop-off rates within the first 24–48 hours What it is: Users churn almost immediately after installation or signup. Why it happens: Forced installs, incentive-driven behavior, or misleading creatives. What it indicates: Poor user intent, weak onboarding quality, and wasted acquisition spend. Unusually High Retargeting Conversions What it is: A sudden or consistent surge in conversions attributed to retargeting campaigns. Why it happens: Fraudulent sources manipulate attribution using techniques like click spamming, cookie stuffing, or last-click hijacking. What it indicates: Conversion hijacking rather than genuine retargeting impact. How to detect and prevent Affiliate Fraud? Your legacy tools might be validating traffic at initial stages but is it going deeper to analyse compliance as well? Once the signs are identified, the next approach for brands must be to opt for a comprehensive AI-driven solution that keeps their affiliate programs intact by also extracting the metrics that is not inflated by wrongful conversions. One such solution is Valid8 by mFilterIt that strengthens brands against affiliate fraud while maintaining affiliate integrity – Build Source-Level Transparency Monitor every click and conversion comes from. When you see the true source of performance, you can reward real partners, eliminate hidden leakages, and invest with confidence not assumptions. Enable Holistic Coverage Detect and block traffic from incent walls, curb unauthorized coupon usage, and ensure your program rewards only genuine, high-intent users — not incentive-driven or commission-leaking conversions. Protect Retargeting from Fake Audiences Retargeting only works when the original data is clean. Filter invalid traffic early so your budget reaches high-intent users not bots or recycled audiences. Turn Insights into Smarter Investments Real-time, advanced analytics show what’s truly driving ROI. Double down on winners, cut risky sources fast, and optimize with speed. Combine Machine Speed with Human Intelligence Automation detects anomalies instantly; expert analysis adds context and action. Together, they resolve threats faster and keep performance on track. Conclusion Brands running affiliate campaigns must first ensure the quality and authenticity of the traffic generated by their partners. This not only protects brand investments but also safeguards genuine affiliates from being impacted by fraudulent practices. To effectively break these patterns, a robust ad fraud detection solution is essential and mFilterIt’s Valid8 validates full-funnel ad activity in the most comprehensive way. Want to know how? Schedule a call now! FAQs What is affiliate fraud in digital marketing? Affiliate fraud refers to deceptive practices used by fraudulent partners to generate fake clicks, installs, leads, or conversions in order to earn illegitimate commissions, causing financial loss and inaccurate performance data for brands. How can brands detect affiliate fraud early? Brands can detect affiliate fraud early by monitoring traffic quality, analyzing engagement metrics, tracking source-level data, validating full-funnel performance, and using AI-driven fraud detection solutions for real-time monitoring. What is organic hijacking in affiliate fraud? Organic hijacking occurs when fraudsters intercept organic user journeys and falsely attribute conversions to affiliate channels using last-click manipulation or forced redirects.

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What is Ad Monitoring? Why Does it Matter During IPL Advertising?

Blog, Ad Fraud / Durgesh Rathore

The Indian Premier League (IPL) is not just one of the biggest sporting events in the country; it’s also one of the most powerful advertising platforms for brands. Every season, millions of viewers tune in across TV and digital platforms, making IPL a high-impact opportunity to drive brand visibility at scale. Naturally, brands go all in. From live broadcast placements and digital ads on OTT platforms to in-play overlays during matches, IPL advertising budgets are spread across formats with one common expectation: maximum visibility. But in a multi-feed broadcast environment like IPL and T20, heavy spending does not automatically guarantee that audiences actually saw what you aimed for until you see the final reports from broadcasters post matches. This brings us to a critical challenge for advertisers – ad monitoring. During IPL advertising, it’s no longer enough to buy ad slots and assume the ads will be delivered diligently. Brands need to understand when ads appeared, in what context, on which screens, at what time, and for how long they were actually visible. How Ads Are Actually Delivered During IPL Matches? IPL advertising is not a single media buy. Ads are delivered on multiple placements, platforms, and locations. Live match branding on jerseys, boundary boards, pitch mats, and other on-ground assets Ads during commercial breaks on TV and OTT platforms Digital display and video ads on streaming apps In-stream overlays like Aston ads and L-band ads that appear during the live play Such fragmented broadcast ecosystems make tracking difficult and inconsistent, creating a visibility gap. The Visibility Gap: What Brands Don’t See During T20 and IPL Broadcasts Many advertisers investing in IPL advertising focus on placements purchased — number of ad slots, jersey branding, etc. However, here’s what brands might not see. Brand Exposure During Live Match Coverage On-ground brand assets such as jerseys, boundary LED boards, pitch mats, stumps, and skirting depend entirely on broadcast framing for visibility. Whether a logo appears on screen, and for how long, it is decided by the flow of the match. Close-ups, replays, or action-focused shots can easily sideline on-ground branding. As a result, a brand may be present throughout the stadium but appear on screen only for a shorter span of time. FCT Ads During Breaks and Digital Insertions Commercial break advertising is often assumed to be the most predictable part of IPL campaigns. However, ad delivery and performance during breaks can differ significantly. Their effectiveness depends on cut percentages, placement, and visual clutter at the moment they appear. Short exposure windows can significantly reduce impact, yet these formats are rarely measured beyond confirmation of placement. However, without regular monitoring, advertisers are left relying on aggregated post-campaign reports rather than verified ad delivery. Non-FCT Ads: In-Stream Overlays During Live Play In-stream formats like L-band ads (horizontal strips across the bottom of the screen) and Aston ads (lower-third graphics that appear without disrupting the match feed) appear during live gameplay, positioning them as high-impact placements. However, their actual visibility depends on how long they remain on display during active play and whether it was delivered or not. Without in-stream ad monitoring, brands have limited clarity on whether these overlays were prominently visible or quickly overshadowed by live action and graphics. What Most Advertisers Investing in IPL Advertising Miss Without Advanced Ad Monitoring? Broadcaster reports focus on delivery — spots aired or planned reach. What they often miss is: Frame-level confirmation of brand visibility Feed-by-feed ad delivery and visibility differences Differences across regional and OTT feeds Duration, cut percentages, and prominence of on-screen ad exposure Competition mapping while the matches are happening Continuous ad tracking and verification Incomplete tracking leaves advertisers with incomplete verification. More importantly, these reports rarely provide evidence to question under-delivery. Without visibility data such as early cuts, missed peak moments, or feed-level gaps, advertisers have limited grounds to seek clarifications on ad delivery. Hence, the need for ad monitoring. So, What is Ad Monitoring in IPL Advertising? In simple terms, ad monitoring is the process of independently verifying what actually appears on screen during IPL broadcasts across live play, commercial breaks, OTT platforms, and in-stream overlays. Without active ad monitoring, advertisers risk losing both visibility and impact. Performance dips, under-delivery, missed geographies, or incorrect creatives damage campaign effectiveness, leading to lost opportunities in a media moment that’s all about timing and precision. An ad monitoring solution verifies what is actually shown on screen across all formats, platforms, and feeds. It tracks: If live match branding was visible or not How long a brand stayed on screen Whether each ad in commercial breaks played as planned Which feeds and regions saw the ad Presence of overlay ads and their duration Any discrepancies between planned and delivered visibility Did the ad run at the scheduled time How many times did each creative appear Was the brand visible during peak match moments Instead of relying just on broadcaster reports alone, it uses frame-by-frame detection and automated AI parsing to independently verify visibility and brand presence throughout the entire broadcast – live or recorded. Benefits of Ad Monitoring During IPL Advertising Ad monitoring helps advertisers move from assumptions to evidence, offering clear, actionable insights across every advertising format. Here’s how: Any-Asset, Frame-by-Frame Detection Tracks every brand element, logos, visuals, taglines, and products, frame by frame, ensuring no on-screen exposure is missed during live matches, replays, or commercial breaks. Platform & Format Agnostic Visibility Monitors ad visibility across TV, OTT, mobile, and CTV platforms, giving advertisers a consistent view of exposure regardless of where audiences are watching. Multilingual & Regional Precision Breaks down visibility by language feeds and regions, helping brands understand how exposure varies across geographies and ensuring regional campaigns deliver as planned. Contextual Advertising & Performance Benchmarking Supports stronger contextual advertising decisions by comparing brand visibility, measuring placement quality, engagement potential, and screen presence to assess true performance. Competitor Visibility & Insights Ad monitoring enables brands to see how competitors are advertising during the same

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What is Ad Fraud? Answering The Most Asked Questions About Ad Fraud

Ad Fraud, Blog / mFilterIt Experts

Ad fraud is an evolving threat and no longer linear. It is becoming more advanced everyday with AI and automation also contributing towards speed and scale. What once looked like normal bot activity has now become far more sophisticated, subtle, and harder to distinguish from genuine user behavior. This sophistication of ad fraud raises a lot of questions in the minds of advertisers, marketers, publishers, brand owners, or anyone involved in the digital advertising ecosystem for that matter. Hence, the purpose of this blog. To ensure you get answers to the most asked questions about ad fraud in one place. We will talk about everything from what is ad fraud to knowing how to respond to it with clarity and confidence. Let’s get started. What is ad fraud? Ad fraud is an attempt to generate fake, invalid traffic, or low-quality interactions on digital ads to manipulate campaign results. These interactions often appear real on the surface, such as impressions, clicks, leads, and installs, but actually come from bots, emulators, or click farms. By using various ad fraud techniques, fraudsters exploit payment models like CPM, CPI, or affiliate commissions. As a result, advertisers lose their ad budget on fake trafficand end up optimizing campaigns based on misleading metrics, leading to campaign inefficiency. What are the different types of ad fraud? Ad fraud shows up in different forms depending on the campaign objective, platform, pricing model, and even targeting. In case of web campaigns, it commonly appears as fake impressions, invalid clicks, or invalid traffic to exhaust budgets and inflate engagement metrics. In case of mobile app campaigns, ad fraud is more deeply tied to attribution and installs. Fraudsters exploit CPI and CPA models by generating fake installs, click injections, or install hijacking tactics that claim credit for users who would have installed organically. In case of affiliate campaigns, it takes the form of fake leads, fake installs, incentivized traffic, cookie stuffing, or unauthorized brand bidding, etc. The intent is to claim payouts without delivering genuine results. This results in poor partner performance, reduced ROI, and loss of trust in affiliate ecosystems. Get your hands on our ad fraud guide to learn more about different types of ad fraud techniques in detail. Who is affected by ad fraud? Everyone in the digital ecosystem is affected by ad fraud. Marketers and advertisers suffer direct budget losses and are left explaining poor performance and low-quality leads. Legitimate publishers face unfair competition from fraudulent inventory, revenue loss, reputational risk, and even potential network penalties. Agencies struggle with compromised data that weakens optimization and client trust. Ad networks and platforms risk credibility, higher operational costs, and compliance challenges. Affiliate managers deal with incentive-driven, low-intent users that inflate numbers while damaging long-term brand perception. How do I know if my campaigns are being affected by ad fraud? Ad fraud has moved beyond obvious bot techniques that were easier to identify. It has now evolved to mimic real user behaviour. However, to identify if your campaigns are being affected by ad fraud, you must notice the following signals: Sudden spikes in traffic or clicks without a proportional increase in conversions or meaningful engagement High engagement metrics but low downstream actions such as purchases, sign-ups, or app usage Repeated interactions from similar device types, locations, or behavioral patterns that appear “too consistent” Abnormally short or uniform session durations that don’t reflect natural browsing behavior Leads or installs that fail validation checks, show no post-conversion activity, or quickly drop off Campaign performance improving on dashboards while business outcomes continue to decline Individually, these signals may seem harmless, but they clearly indicate fraudulent or low-quality traffic is manipulating campaign performance. What is click fraud? Click fraud is a type of ad fraud technique where bots are used to generate fake or automated traffic clicks on ads without any real interest in the product or service being promoted. These clicks are created to look like genuine user interactions, making them difficult to identify at first glance. These fraudulent clicks also trigger actions like app installs, conversions, or website visits, further masking their true nature. In pay-per-click (PPC) advertising, publishers earn revenue every time an ad is clicked. Fraudsters exploit this model by creating fake websites or placements and artificially inflating click volumes using bots. As a result, advertisers end up paying for invalid clicks that deliver no real value, while fraudulent publishers profit from traffic that was never genuine in the first place. I often see high clicks but low conversions on my campaigns. Is this ad fraud or just poor performance? High clicks with low conversions do not always mean ad fraud. In many cases, poor performance can be caused by factors such as ineffective creatives, incorrect targeting, slow or confusing landing pages, or a mismatch between the ad message and the offer. However, ad fraud becomes a strong possibility when certain patterns start to appear. Sudden increase in clicks without any changes in targeting, creatives, or budgets. Clicks with little to no intent-driven actions such as form fills, purchases, or meaningful engagement. Clicks coming from repeated IP addresses or devices. The key is to look at behavioural signals to identify click fraud. Single metrics can be misleading, but consistent patterns of activity without business outcomes often signal something deeper than normal performance issues. Do ad platforms like Google and Meta already block ad fraud? How to prevent invalid traffic from Google? Yes, ad platforms like Google and Meta do have built-in systems to detect and block ad fraud. They do filter out a significant amount of invalid activity. However, these platforms operate in a closed ecosystem as walled gardens, hence posing limitations. This means advertisers have limited visibility into how traffic is generated, how users behave beyond surface metrics, and how fraud decisions are made. This lack of transparency creates blind spots. Fraudsters exploit these gaps using bots, click farms, and automated scripts that mimic real user behavior closely enough to bypass platform-level checks. As a result, some fraudulent

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