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CMOs Are Losing Millions To Ad Fraud. Here’s How mFilterIt Helps Solve It

Ad fraud is not just a basic bot traffic problem anymore. It’s an issue contaminating the entire marketing funnel silently. Here’s what the data says, and what leaders must do next. Imagine spending ₹100 on a digital campaign and losing ₹20–30 before a single real customer ever sees your ad. Not due to a bad creative. Not a flawed targeting strategy. But because the digital advertising ecosystem, the programmatic advertising pipelines, the affiliate networks, and the ad placements, are structurally misplaced. According to mFilterIt analysis of campaigns run in 2025, featured in the FICCI Media & Entertainment Report 2026, digital ad spend wastage is widespread, and leaders are only beginning to understand its scale. That is why the gap between awareness and action is a real crisis. And that’s what we are going to cover in this blog. Business costs of ad fraud that directly impact the business review Where invalid traffic is highest, broken down by channel and platform What full-funnel ad fraud detection and mitigation actually looks like in practice The Business Costs of Ad Fraud Impacting Business Performance When marketers think about bad ad campaign performance, they usually blame it on creative misses or wrong audiences. But media leakage operates invisibly, creating four distinct business harms: Wasted Media Spend: A significant portion of ad budgets is lost on invalid traffic, bot activity, and low-quality engagements that do not contribute to real business outcomes. Distorted Performance Analytics: Fraudulent and low-intent interactions contaminate campaign data, making optimization decisions unreliable and impacting overall marketing efficiency. Poor/Junk Lead Quality: Fake clicks and invalid conversions result in low-quality leads entering the funnel, increasing acquisition costs, and reducing sales effectiveness. Brand Trust Risk: Ads appearing alongside unsafe or fraudulent environments can damage brand reputation, weaken consumer trust, and negatively impact long-term brand perception. How Invalid Traffic Travels Across Platforms and Channels The data breaks this down in granular terms. Invalid traffic patterns vary significantly by platform, and some of the highest-risk channels are also among the most aggressively used by marketers to reach their target audiences. Affiliate Marketing Affiliates are paid based on results. But if 40–50% of conversions or leads are invalid, marketers end up paying for manipulated and fake results, instead of the real ones. Performance-linked pricing only works when the performance is real. Read more about how affiliates exploit lead gen campaigns here. Programmatic Advertising Global programmatic ad spend reached $642 billion in 2025 and is expected to grow to nearly $800 billion by 2028. (Source: Start.io) Considering the growth, fraud is also widespread. With invalid traffic at impression level ranging between 30–45% as per mFilterIt’s analysis of digital brand campaigns, nearly half of what you’re buying could be going to bots, not buyers. In-App or Mobile Advertising In-app fraud no longer stops at fake installs. It extends into fabricated post-install actions such as sign-ups, lead submissions, purchases, and in-app events, polluting attribution, and CRM data before it even reaches the sales team. With nearly 30–35% invalid activity patterns, mobile advertising ecosystems are increasingly vulnerable to synthetic engagement at scale. CTV and OTT Advertising Connected TV and OTT platforms are where the budgets are shifting rapidly. But 15–20% frequency cap violations mean your ads are repeatedly hitting the same device, not reaching new audiences, just burning spend. Walled Gardens Platforms like Google and Meta are often assumed to be clean. They’re cleaner, but 9–18% invalid click patterns show that even here, not every click is a real person. Moreover, ad fraud gets even more complicated and sophisticated to identify as it moves down the funnel. Platforms like Google and Meta are often perceived as relatively cleaner ecosystems. While they do have stronger fraud controls, 9–18% invalid click patterns still indicate that not every engagement originates from a genuine user. To understand the impact of click fraud in performance campaigns, read this blog. What makes ad fraud increasingly difficult to detect is its evolution deeper into the funnel. As fraudulent activity progresses from clicks to installs, leads/purchases, and post-conversion events, the signals become more sophisticated, making detection, attribution validation, and quality assessment significantly more complex. How Ad Fraud Leaks Budget Across Every Stage of the Marketing Funnel Fraud doesn’t enter at just one stage. It impacts the entire funnel, from impressions to conversions. Here’s how: Upper Funnel: Reach Isn’t Always Real Made-for-Ad Sites (10–12% leakage): Ads appear on websites created only to serve ads, not real users. Made-for-Kids Placements (7–9% leakage): Your ads appear beside content where your actual audience doesn’t exist. CTV & OTT Frequency Capping Violations (15–20% leakage): The same user keeps seeing your ad repeatedly, wasting budget instead of expanding reach. Lower Funnel: Ad Fraud Gets More Expensive Low-Intent Traffic (20% inflated visits): Traffic numbers look strong, but many users never intended to engage or convert. Low-Intent Events (14% leakage): Fake or low-quality interactions distort campaign optimization signals. Ad-Driven Conversion Inflation (43% leakage): Bots and fraudulent actions inflate conversion numbers, creating a false sense of performance. Organic Traffic Poaching (22% leakage): Users who would have converted organically are wrongly attributed to affiliates. Therefore, the C-suite has a direct stake here. As the digital marketing budget grows, so does accountability across both the CMO and the CFO. Campaigns are increasingly expected to move beyond impressions toward measurable outcomes: engaged visits, site conversions, and real revenue signals. That expectation becomes impossible to meet if the underlying data is compromised. Hence, the need for a full funnel strategy and an ad fraud solution that uses advanced technologies to identify and prevent ad fraud proactively. How mFilterIt’s Ad Fraud Solution Helps Using the Full Funnel Strategy mFilterIt’s ad fraud solution helps solve the problem not just by auditing campaigns after the damage is done, but by detecting and blocking invalid traffic in real time, across every stage of the customer journey. Here’s how it works across three critical areas: Branding campaigns: Where your reach is being stolen For every branding campaign, the foundational question isn’t just ‘did people see the ad?’

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Affiliate Lead Fraud Exposed: How Fake Leads Hijack Performance Marketing

Blog, Ad Fraud / mFilterIt Experts

Welcome to the world of Pay Per Lead! A more trustworthy and monetized model. When impressions were being faked, clicks were being hijacked, and brands were receiving barely any conversions. Hence marketers, especially in tier-1 markets like the United States, decided to do what any rational person would do. They stopped paying for clicks and started paying for outcomes. Fill a form, generate a lead, get paid. Simple, accountable, fraud-proof. The moment payment moved to the lead event; some affiliates simply moved their operation there too. Suddenly, forms were being filled by scripts, credentials were being recycled, and conversion metrics were spiking in ways that looked extraordinary on a dashboard and meant absolutely nothing in a sales pipeline. The model designed to eliminate fraud became the next frontier for it. In this blog, we will discover – What is lead fraud and how affiliates exploit PPL campaigns What our latest analysis revealed on lead fraud Why your current measures are not enough to tackle lead fraud What a holistic ad traffic validation solution solves in lead gen campaign What is Lead Fraud and How Affiliates Exploit Lead Gen Campaigns Imagine opening a lemonade stand and suddenly getting 500 “customers” who ask for lemonade, write down their names, and then disappear before buying anything. Sounds exciting at first until you realize nobody actually wanted lemonade. That’s exactly what lead generation fraud looks like in digital marketing. In lead generation fraud, fake demand is created by fraudsters by filling up lead forms with credentials without having any real intent of buying any product/service. This means your brand who has partnered with affiliates are exploiting your marketing campaigns by filling out multiple fake leads and very subtly shifting the burden of non-conversion on sales team. Lead fraud happens in two ways – Fake Leads Completely made-up entries with false details, often created by bots. They look like leads but have no real user behind them. Punched Leads Manually filled leads using random or reused information to hit targets. They seem real but don’t convert when contacted. What is the Mechanic Behind Lead Fraud? Lead fraud is not just another move to pollute your campaigns; it is a very strategic one that is noticeable only when the commission is attributed to partners. Here’s how affiliate lead generation fraud typically works: Fake lead generation Affiliates submit fabricated or bot-generated leads using fake names, emails, and phone numbers, often sourced from data dumps or auto-filled by scripts, to hit volume targets and earn commissions. Incentivized traffic manipulation Real users are paid or incentivized (cash, gift cards) to fill out forms with no genuine purchase intent, inflating lead counts while producing zero conversion value for the advertiser. Lead recycling Old or previously sold leads are repackaged and resubmitted, sometimes with slightly altered details, to collect duplicate commissions from advertisers who lack deduplication checks. Cookie stuffing / attribution hijacking Affiliates drop tracking cookies on users’ browsers without their knowledge, falsely claiming credit for leads or conversions that originated organically or through other channels. Device/IP farming Using emulators, VPNs, rotating proxies, or device farms, affiliates simulate multiple unique users from a single operation, bypassing basic device fraud filters and generating large volumes of fraudulent leads at scale. Affiliate Lead Fraud Exposed: 44 Leads Tracked to One Cookie Upon analysing the lead generation campaign for a major USA brand that had partnered with affiliates to bring leads, we found severe lead punching use case – The numbers looked great until they didn’t. 342 leads from just 656 visits. A conversion rate that most marketers would celebrate. On paper, this campaign was firing on all cylinders. In reality, it was being quietly gamed. The Cracks Beneath the Surface When traffic quality signals were layered over the raw data, the same fingerprints kept showing up — literally. Every suspicious lead traced back to the same affiliate source, the same device, the same desktop environment, the same location, and near-identical browser signatures. Not similar. The same. That is not how real consumer behaviour works. The Day the Mask Slipped The clearest evidence of manipulation surfaced on 06-12-2025. A single cookie ID was used to submit 44 leads in one day. One device. One session fingerprint. Dozens of “different” users. No genuine audience behaves this way. But an affiliate with a script, a quota, and a commission on the line? Absolutely. The Graph Doesn’t Lie Conversion rates don’t naturally leap from baseline to 13%, then 21%, then 33% in a matter of days. Organic growth curves they don’t spike like a heart monitor. When they do, it almost always points to the same culprits, automated submissions, recycled user pools, or incentivised form-filling dressed up as real demand. The Real Cost of Fake Leads This is where the damage moves from a data problem to a business problem. Behind every inflated metric sits a real consequence sales teams burning hours chasing contacts who never existed, budgets being doubled down on channels that are actively cheating, and acquisition cost calculations built on a foundation of fiction. The campaign looked like a success. The business was paying for failure. What This Should Change Affiliate marketing remains one of the most powerful growth levers available — but only when the leads coming through it are real. The moment you measure performance purely by volume and conversion rate, you hand fraudulent affiliates exactly the playbook they need. The brands winning this battle are looking deeper: behavioural patterns, device consistency, cookie-level tracking, and source-by-source forensics. Because in a world where lead generation fraud is this sophisticated, the only defence is an equally sophisticated offence. Why Surface Level Analysis is not Enough to Detect Lead Fraud Lead exploitation is a broader ecosystem with affiliates disrupting the campaigns through sophisticated tactics. Surface level solution only covers the basic obvious signals like duplicate signals and repeated IP addresses but not something advanced, here’s why they aren’t enough – Fraud has moved from pattern to behaviour: Basic filters catch duplicate emails and repeat IPs, but sophisticated affiliate fraud rotates identities, devices, and locations specifically to avoid these checks. Fraudsters map your rules before they operate: Conversion thresholds, IP blacklists, and volume caps are not deterrents, they are a blueprint. Fraud operations stay comfortably within every limit your detection layer has published. Surface tools measure outputs, not intent: They confirm a lead arrived. They cannot see the 400-millisecond

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Affiliate Traffic is Not Always High Intent. What is Affiliate Fraud and How It Impacts Campaigns

Blog, Ad Fraud / mFilterIt Experts

Affiliates only get paid when a user takes a defined action, a lead, an install, or a purchase. So, the traffic must be intent-driven? But that’s not the case everytime. Why? Because affiliate fraud exists. And it’s more common and sophisticated than marketers realize. Fraudsters manipulate the payment models by generating fake traffic, fake leads, bot installs, duplicate accounts, organic hijacking, etc. The catch is they make all this look legitimate that makes ad fraud even more difficult to detect. Therefore, in this blog, we are going to break the myth about affiliate marketing most advertisers still believe in. Affiliate Traffic is Always High Intent. The answer is not always. Affiliate traffic you’re paying for may not be as genuine as it appears. Continue reading further to know how affiliate traffic fraud impacts campaign performance. What is Affiliate Fraud? Affiliate fraud is when fraudulent affiliate partners manipulate the system to generate fake actions like leads, installs, or conversions that appear genuine. The purpose is to earn commissions without delivering value. Effective affiliate fraud detection helps marketers identify and prevent such fraudulent activities before they impact campaign performance Many affiliates prioritise volume over quality. Hence, the vulnerability to ad fraud and manipulation of results. Here are some of the common affiliate fraud tactics they use Lead punching Fake or low-quality leads are submitted deliberately to trigger payouts. This is usually done using bots or fabricated data to fill in lead forms in bulk. Cookie stuffing Affiliates drop tracking cookies on a user’s browser through extension downloads, redirects, pop-ups, or hidden scripts. Users then get tagged with that cookie even though they never interact with affiliate’s content. Know more about cookie hijacking in detail here. Incentivized installs Users are paid to install an app, with zero genuine interest in it. This happens when fraudulent affiliates use reward-based platforms or unapproved promotions on various platforms to drive installs, leading to high uninstall rates and lower LTV. Referral and coupon fraud Fake or duplicate accounts are created just to claim referral rewards. Affiliates exploit loopholes in referral or promo systems using multiple identities, devices, or disposable emails to generate repeated payouts. Validation spoofing Fraudulent signals are engineered to pass quality checks. This happens when attackers manipulate device data, IPs, or behavioral patterns to make fake leads appear legitimate during verification. Bot-generated form fills Automated bots fill out forms at scale to manufacture leads. Bots mimic human behavior to submit large volumes of fake entries, inflating lead counts without real user intent. Organic traffic misattribution Affiliates manipulate last-click hijacking attribution to hijack organic traffic and conversions. They inject tracking links at the final stage of a user journey, overriding the original source and falsely claiming credit for the conversion. What Real Campaign Data Analysis by mFilterIt Reveals About Affiliate Fraud Across audited campaigns, up to 35% of affiliate traffic shows signs of bot involvement, inorganic behaviour, or misattributed organic actions. Case Overview 1: Lead punching by an automobile brand’s affiliate partner A major global automobile brand was running affiliate campaigns to drive specific conversion events. In this case, customers completing a “cash thank you” or “lease thank you” action after a vehicle transaction. The numbers looked fine from the outside. But when the campaign was audited, the findings were alarming. 70% of all invalid traffic traced back to a single affiliate partner. That one partner had a 74% invalid visit rate, and an 86% invalid event rate. In plain terms: nearly 9 out of every 10 conversion events attributed to that affiliate were fraudulent. The company had been paying for results that didn’t exist. Case Overview 2: Referral coupon fraud under the name of a global petroleum brand A global petroleum brand was running customer acquisition campaigns and spending well on them. But lead quality was still poor, and referral coupons were being flagged for suspicious activity. When the mFilterIt SDK was deployed to analyse install-level data, the truth came out. Of all the app installs that appeared to be clean and legitimate, 21% were actually referral coupon fraud. Automated bots or fake users were simply creating fake and duplicate accounts to claim referral incentives, with no intention of becoming actual customers. One geography alone accounted for 76% of that coupon fraud. The Impact: How Affiliate Fraud Damages Your Business Outcomes? When affiliate fraud goes undetected, the impact ripples across your entire marketing operation: Your sales pipeline fills with unqualified and fake leads that waste your team’s time. Your CPA and CPI benchmarks look artificially efficient, so you keep spending on the wrong sources. Your budget gravitates toward the channels “performing” best, which are often the most fraudulent. Channels that are actually working get defunded because they can’t compete with inflated affiliate numbers. How to Protect Marketing Budget from Affiliate Fraud with mFilterIt’s Affiliate Fraud Detection Solution? mFilterIt provides a full-funnel ad fraud detection solution that gives marketers visibility at every stage of the affiliate journey, not just at the click level, but all the way through installs, events, and conversions. It helps you: See where your traffic is actually coming from, identify underperforming or suspicious affiliate partners before they do more damage. Catch attribution manipulation, detect when genuine conversions are being falsely claimed by affiliates who had no real role in driving them. Spot incentivized users early, flag users who only took action to claim a reward, with zero intention of sticking around. Monitor referral and coupon activity in real time, identify patterns of abuse before they inflate your acquisition numbers. Validate traffic before it enters your funnel, filter out bots, fake devices, and spoofed signals at the pre-install stage itself. The result? You stop paying for performance that was never real and start making budget decisions based on data you can actually trust. For a deeper look at how affiliate fraud shows up across different campaign types and what to watch for at each stage, read our complete Affiliate Fraud Guide for Marketers. Conclusion Affiliate marketing isn’t the problem. Blind trust in it is. When you assume every action is genuine, fraudsters win. When you start auditing affiliate data correctly, you take control back and start seeing genuine results. Your affiliates should be working for your growth. Not against it. Find out what your affiliate partners are driving for you and how much of your affiliate spend is delivering real results. Connect with mFilterIt experts now. Frequently Asked Questions What is affiliate fraud and how does it work? Affiliate fraud is when fraudulent affiliates manipulate the payout system to earn commissions without delivering real users. They do this by generating fake leads, bot installs, duplicate accounts, or stealing credit for conversions

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What Is Frequency Capping? Why It Matters in Digital Advertising Campaigns?

Blog, Ad Fraud / mFilterIt Experts

You see the same ad once. Fine. Twice? Still okay. But by the increasing number of times in a day, it stops being memorable and starts becoming annoying. Now flip the perspective. As an advertiser, you’re paying for each of those impressions, assuming you’re reaching new users. But what if you’re not? What if your campaign is just circling around the same audience again and again? This is exactly what happens when frequency capping fails. To understand this in detail, let’s dive deep and know what frequency capping is and how to prevent breaches effectively. What is Frequency Capping? Frequency capping simply controls how often the same person should see your ad within a given time period. The idea is straightforward, instead of showing the same ad to one user ten times, the system distributes those impressions across multiple users. This ensures that campaigns expand their reach, avoid overexposure, and maintain efficiency. When implemented correctly, frequency capping helps maintain a balance between visibility and user experience. It prevents fatigue, protects brand perception, and ensures that budgets are used to reach more potential customers, not just the same ones repeatedly. However, this balance only exists when the cap is actually followed during ad delivery which is where things often start to break down. Campaigns today run across multiple exchanges, devices, and tracking systems. A single user may interact with ads through different browsers, apps, or devices, each generating separate identifiers. What appears to be “one user” in reality becomes multiple fragmented identities within the ecosystem. What Frequency Capping Violations Look Like in Real Campaigns Frequency capping breaches may not always stand out in summary reports, but they become very clear when you look closely at delivery data. In a campaign analysis, a frequency cap of 3 impressions per device was clearly defined. The expectation was simple once a device reached this limit, further ad delivery should stop. However, the actual delivery pattern showed a clear breach. A single device recorded 2,112 impressions during the campaign period. This is far beyond the defined cap and highlights a direct failure in enforcement. What makes this more concerning is not just the number, but the pattern. The same device continued to receive ads repeatedly, indicating that the system was not stopping delivery even after the cap was exceeded. Instead of controlling exposure, the campaign allowed unrestricted ad repetition at the device level. This clearly shows that when we expand beyond a single device, the pattern becomes more widespread. Multiple device IDs showed unusually high impression counts: Several devices crossed 1,000+ impressions. Others also stayed between 800 and 1,600 impressions. This shows that the issue was not isolated; it was happening across multiple devices. At this point, the campaign stops behaving like a reach-driven campaign. Instead of distributing impressions across a larger audience, it begins to concentrate delivery on a smaller group of users. According to the analysis, the first device ad request of 2,112 times was shown at 2:00 pm in the afternoon. Likewise in other devices, the ad request showed multiple times that were distributed in different time periods. This analysis highlights three key signs of frequency capping breaches here: A small number of devices generating a disproportionately high share of impressions Repeated delivery far exceeding the defined frequency cap Growing impressions without a meaningful increase in reach Why This Matters More Than It Seems At first glance, frequency capping violations may not appear critical. Campaigns continue to deliver impressions, and performance metrics may seem stable. However, the real impact becomes clear when you look at how those impressions are distributed. When the same users are repeatedly exposed to ads, it starts affecting the campaign in multiple ways: Reduced effective reach – instead of reaching new users, the campaign stays limited to a smaller audience Budget inefficiency – spend is wasted on repeated impressions that add little incremental value Lower engagement rates – users become less responsive when they see the same ad too often Over time, these effects build up and quietly reduce overall ad campaign performance, even when the campaign appears active on the surface. How mFilterIt Helps Control Frequency Capping Violations Once frequency capping violations are identified, the next step is not just detection but control. Identifying frequency capping violations is only half the job. The real value lies in controlling them at the moment of delivery. mFilterIt goes beyond just reporting the issue it actively ensures that frequency caps are followed, so campaigns don’t fall into repetitive delivery patterns. In the above campaign, once excessive ad repetition was detected at the device level, mFilterIt stepped in to restrict impressions beyond the defined cap in real time. This immediately reduced overexposure and allowed impressions to be redistributed more effectively across users. As a result, the campaign shifted from repeated targeting of a few devices to a more balanced and reach-driven delivery model. Controlled ad exposure with no frequency overshoot Campaigns stay aligned with defined frequency limits, ensuring that users are not exposed to ads beyond the intended threshold Minimized repetition and reduced impression wastage By limiting repeated delivery to the same devices, campaigns avoid spending on impressions that do not add incremental value Stronger reach through better distribution Impressions are spread across a broader audience, helping campaigns move beyond a limited user pool and improve overall reach Improved user engagement with balanced exposure When users are not overexposed to the same ad, they are more likely to stay responsive, leading to better interaction and brand recall More efficient and performance-driven campaigns With better control over ad frequency and delivery patterns, advertisers can optimize campaigns more effectively and drive stronger campaign performance By combining real-time control with continuous monitoring, mFilterIt ensures that frequency capping is not just a campaign setting but a mechanism that actually works. Conclusion Frequency capping is not just about setting limits it’s about making sure those limits are actually followed. When enforcement fails, campaigns lose reach, waste budget, and see a drop in overall ad campaign performance. To avoid this, advertisers need more than just setup they need continuous monitoring and control over ad delivery. With mFilterIt’s ad fraud solution, you can ensure clean delivery, controlled ad frequency, and better reach quality by filtering out invalid traffic and enforcing caps in real time. Get in touch with mFilterIt’s experts to take control of your campaign delivery and drive better performance. Frequently Asked Questions What is frequency capping in digital advertising? Frequency capping is a setting that limits how many times the same user sees an ad within a

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Traffic Quality vs Invalid Traffic Volume: What Really Drives Campaign Performance?

Blog, Ad Fraud / mFilterIt Experts

Every brand’s marketing program runs on one principle – more visits amount to more leads. While Google and META highly influence users’ journey but not all that stands true. The journey is simple but equally prone to the complexities of digital advertising ecosystem. This shifts the real question from how many visits your campaigns generate to where those visits are coming from or if they are leading to any conversions? We saw this firsthand while working with one of the USA’s leading aggregator players. For them, deeper validation of their campaign traffic was the turning point. When they looked closer at where their visits were actually coming from, the picture changed entirely. Irrelevant, low-quality sources were quietly eating into their budget and polluting their campaign data, making it nearly impossible to measure what was truly working. So, they made a call: blacklist the bad sources. Clean the data. And rebuild on a foundation they could actually trust. In this blog, we break down exactly how that played out: Sources that pollute Google and Meta campaigns and their impact How blacklisting changed the game The measurable impact of defending against fraudulent traffic Key takeaways for marketers Conclusion Source-Level Fraud in Google and META Campaigns We did a thorough analysis of the brand’s campaigns running on Google and META and here’s what we found – From META, brand received the highest Invalid Traffic (IVT) of 28.51%. From Google, brand got 8.15% IVT from various fraudulent sources. The difference in invalid traffic across platforms clearly shows that not every traffic source delivers the same quality of users. A campaign may generate high traffic numbers, but that does not always mean the visits are genuine or valuable. In some cases, a large portion of traffic can come from fraudulent or low-quality sources that never convert into real customers. For Google campaigns, the IVT percentage may appear lower compared to other platforms, but the advertising costs on these walled gardens are significantly higher. This means that even a small percentage of invalid traffic can result in substantial budget wastage and reduced campaign efficiency. To understand this better, let’s look at the major sources contributing to IVT and the direct impact they have on marketing campaigns – VPN/ Proxy Fraud: Traffic routed through VPNs or proxy networks to disguise real user identity and location. Impact: Bypasses geo-targeting and fraud filters, making fake traffic appear legitimate. Geo Fraud: Traffic coming from the geographies that were never a target at the first place. Impact: Creates a false sense of campaign success in priority markets. Behavior Fraud: Bots or automated scripts designed to mimic real user actions like fake clicks, scrolling, session duration. Impact: Inflates engagement metrics while delivering zero real intent. Device Repetition: Repeated interactions from the same device or a controlled pool of devices also called device farms. Impact: Indicates click farms or emulator-driven traffic, skewing user-level data. Pop-Under Traffic: Ads triggered in hidden or background windows without active user intent. Impact: Generates low-quality visits that look like traffic but don’t convert meaningfully. mFilterIt’s Solution: How Blacklisting Changed the Game for Leading Aggregator mFilterIt transformed campaign performance by shifting the focus from traffic volume to traffic authenticity. Through our ad fraud detection tool, brands attained real-time traffic validation and source-level analysis, identifying and blocking fraudulent or low-quality sources before they impact campaign outcomes. This enables brands to take precise actions like blacklisting, ensuring that only genuine users move through the funnel. Here’s how it changed the game – IVT Dropped by 42% in META Campaigns What began at 38% dropped down to 22% in just three months, a major 42% reduction in IVT. This was not a one-time correction; it indicates a consistent, ongoing improvement driven by focused campaign optimization. As deeper traffic validation was done and low-quality sources were identified, the system was able to filter out fraudulent sources such as geo-masking and repeated device activity. Over time, this led to cleaner inputs, better targeting decisions, and more reliable performance signals. The continuous decline also indicates that optimization efforts didn’t just remove existing fraud but actively prevented its recurrence. IVT Dropped by 8.4% in Google Performance Max Campaign Below graph highlights reduction of IVT in Google performance max campaigns by 8.4%. Invalid traffic dropped from 15.84% in September to 14.51% in November—a noticeable improvement over a short period. In Performance Max campaigns, even a single percentage point reduction matters because these campaigns operate at scale and involve higher media spends. So, while the IVT reduction is 8.4%, the real impact goes beyond that number. Less wasted spend on invalid traffic means more budget is directed toward. Campaign Performance Over Time: The Impact of Traffic Quality Optimization Once the blacklisting began, the campaign showed progress in terms of traffic quality in both Google and META campaigns. Let’s see what each denotes – Performance Improvement in META Campaigns This table highlights how campaign performance evolved over a five-month period, Initially, when all traffic sources were allowed to run freely during August, the campaign delivered 1,753 clicks and 101 conversions, resulting in a conversion rate of 5.76%. While costs were moderate, performance was held back by poor traffic quality. As shown in the image below, the conversion rate significantly improves post blacklisting. Moving into September 2025, there’s an interesting shift. Although costs increased by 23.5%, the conversion rate improved to 6.31%. This suggests that cleaning up low-quality traffic sources (likely via blacklisting or filtering) began to pay off. Even with higher spend, the campaign became more efficient because the traffic quality improved. By October 2025, performance stabilizes. Costs remain nearly flat (+0.5%), but the conversion rate climbs further to 6.64%. This indicates that earlier optimizations are holding strong, and the campaign is now reaching a more relevant audience consistently. In November, the conversion rate jumped to 7.36%. The upward trend in conversion rate from 5.76% to 7.36%; is significant. It reflects a clear improvement in traffic quality and campaign efficiency, not just increased spend or scale. Performance Improvement in Google Campaigns The data highlights a clear turning point in campaign performance before and after blacklisting was implemented. In August, the campaign struggled with high cost per conversion (519) and a low conversion rate (0.48%), indicating inefficient spend driven by poor traffic quality. The campaign improved once blacklisting was brought in action as reflected in conversion rates. Post-implementation, starting September, performance improved significantly. Cost per conversion dropped sharply from 137 in September to as low as 67 by early November while conversion rates increased from 0.48% to 2.07%. This reflects the direct impact of filtering out low-quality and fraudulent traffic, allowing the campaign to focus on more relevant users. Overall, the trend demonstrates how traffic

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Merchant Risk Monitoring in the Evolving Digital Payments Scenario

Blog, Brand Protection / mFilterIt Experts

Digital payments in India, led by Unified Payments Interface, are growing at an exceptional pace. Transaction values continue to rise year after year, reflecting how deeply digital payments are now part of everyday life. The graph below highlights this momentum, with transaction volumes growing YoY and an expected 300% increase in both transaction volume and rupee value by FY30 vs FY25. Emerging Trends: UPI payments are the key driver of this growth – with its ecosystem of participating Banks (~700), merchant ecosystem (~65 million) and serving nearly half a billion customers. UPI in terms of Volumes is expected to grow contribute ~85% in terms of rupee value of all digital payments by FY30. Add to this the 40 authorised TPAPs, the ecosystem is bound to flourish and is also insulated from external threats (eg. GPay, visa, mastercard shutting down in Russia); given that this is completely Made in India tech stack. A key component of UPI Payments is the Merchant Eco System – currently contributing ~30% of current value to ~63% in terms of Rupee value. Debit and Credit Cards comprising POS and Digital spends, expected to grow at a CAGR of 25%, with spends primarily coming from credit cards and increase penetration of UPI linked credit cards. The spends growth will be primarily driven by digital spends which is expected to be around 72-75% (FY30) from the current 63%. – this represents a a perceptible shift away from the traditional Brick and Mortar Merchant. Prepaid / Fastag & SI transactions is expected to see a steady growth with primary spends being digital in nature. Emergence of Fintechs, TPAPs, Payment Banks, Telcos once dominated primarily by banks and technology firms, the sector now attracts players from diverse fields such as retail, telecommunications, FinTech, and e-commerce, enabling cross-sector innovation and new market entrants. The above trends is a paradigm shift vis a vis what we have seen till a few years ago and brings with it, its own perils such as cybercrime and fraud risks. Cybercrime has evolved to such and extent that FY25 has seen a 24% spike in cybercrimes reported on the NCRP portal, with the rupee value of reported fraud loss being INR 22,495 crores. This is the reported numbers and the overall non reported instances could be approximated to be 1x in terms of rupee value. Given that this is majorly digital, the merchant eco-system plays a key role in the same; payment instruments like VPAs, underlying merchant accounts etc are used extensively as collections accounts to receive the proceeds of such crime or act as an intermediary in the laundering of such proceeds. Hence it is that much more imperative that there is a lot more focus on the kind of merchant who are being on boarded; given the digital nature of merchant spends and the ease at which the merchant can evolve post onboarding, a Life Cycle based Risk Management Approach is the need of the hour. Every merchant you onboard is a vote of confidence in your platform. But every transaction they process is also a new surface for risk. Hence, what starts at onboarding shouldn’t stop there; it needs continuous attention. In this blog, we will discover- How one-time checks are not enough in merchant onboarding How rapid growth in digital payments is increasing risk And how end-to-end merchant evaluation can make a real difference How One Time Risk Checks are not Enough in Merchant Onboarding? There is a traditional belief that all merchant risks can be tackled before onboarding and once the onboarding is completed, no merchant risks can travel, making the following, the key checkpoints of merchant onboarding – Verify business details Review website Approve and go live But as the digital payment ecosystem is growing more complex, merchant risk monitoring does not stop at onboarding. Assuming that merchants will remain compliant after initial checks is often unreliable. In reality, risks can emerge at any stage post-onboarding, such as – Change in website content Sale of restricted / banned / high-risk products Manipulate redirects or hidden flows Drift away from declared business categories All of this happens after onboarding, when visibility is often limited. How Scaling Digital Payments Elevates Risk in Merchant Onboarding While digital adoption is speeding up, it also brings in new risks that don’t just appear at the start, they continue throughout the merchant journey. More merchants mean: More variation in quality and intent More edge cases that manual checks miss Higher probability of fraudulent or non-compliant entities slipping through However, stopping the merchant ecosystem from evolving is not the solution- “Continuous Monitoring”, is. Why Onboarding Checks Alone aren’t Enough & How Continuous Monitoring Fills the Gap? Here’s how comprehensive and continuous merchant risk monitoring strengthens payment getaways, enabling them to fill the gaps created by just focusing on merchant onboarding – (also include business outcome) Onboarding-only monitoring limitations How holistic monitoring helps One-time snapshot of the merchant Continuous tracking of merchant behavior Misses post-onboarding risks Detects emerging risks in real time Relies on static documents Validates ongoing digital presence and activity No visibility into changes in offerings Flags deviations in products/services Cannot track compliance drift Monitors policy compliance risk continuously Misses malware or suspicious activity later Scans regularly for threats and anomalies Reactive issue resolution Enables proactive risk prevention Higher risk of regulatory/reputation impact Strengthens against compliance risk and brand protection End-to-End Merchant Evaluation: A New Outlook to The Merchant Life Cycle. Merchant onboarding now demands confidence that cannot be achieved without holistic evaluation of merchant lifecycle, one that goes beyond onboarding and keeps validating risk at every stage. That’s where mFilterIt’s approach and product offerings steps I; not only focussing on onboarding but also monitors and evaluates risk which may evolve at a later stage. Business & Operational Analysis We help you truly understand who you’re onboarding by looking beyond basic details-analyzing the business, verifying promoters, and checking location authenticity. Outcome: You onboard genuine, trustworthy merchants from the start. Pre-screening & Identity Verification We validate merchants using multiple signals like contact details, device, IP, and KYC documents to catch inconsistencies early. Outcome: Lower chances of fraud and stronger defence against compliance risk from day one. Website Risk Analysis (and beyond) We continuously monitor the merchant’s digital presence-checking website content, offerings, compliance, and any suspicious activity. Outcome: Early detection of risks and better control even after onboarding. Overall impact: You move from one-time checks to continuous visibility—helping you build a safer

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How a D2C Beverage Brand Drove 82% Orders Growth on BlinkIt in 30 Days Using Unified Ad Manager

Blog, Digital Shelf Analytics / mFilterIt Experts

This blog is a look at what changed when structured campaign management replaced manual processes, and what the data showed one month later. Here’s some background for you. A fast-growing beverage brand was running keyword and product listing campaigns on Blinkit across multiple cities in India. While the brand had an established product catalogue and a reasonable ad budget, their campaign performance was inconsistent. To overcome this, the brand adopted a performance marketing management platform to bring structure and intelligence to their campaign and optimization processes. The result was a 60% increase in revenue within a month. Continue reading further to find out how Unified Ad Manager (UAM) worked. Three Gaps That Were Holding The Performance Campaign Results Back Most brands running campaigns manually hit at least one of these problems. This D2C beverage brand was dealing with all three simultaneously. Campaign inconsistency Between 6th to 21st November 2025, campaigns were near-inactive for 15+ days, which means effectively zero visibility on the platform during that period. A day without active spend is a day where competitors fill the shelf space. Limited keyword coverage Keyword gaps mean entire search queries go uncontested. Only 77 keywords were active, leaving large portions of search demand for tonic water, mixers, and sodas uncaptured. High cost per order An ACoS of 69.2% and ₹189 per order made scaling the campaigns economically difficult to justify. Without time-aware budget pacing, money gets spent during off-peak hours when purchase intent is low. What Automated Campaign Management Using Unified Ad Manager Changed The brand adopted mFilterIt’s Unified Ad Manager (UAM), a performance marketing management platform that provides ecommerce analytics in a single view to address the structural gaps across three areas: campaign scheduling, keyword discovery, and bid optimization, with one additional feature that turned out to be the most impactful of all. Here’s how: Continuous campaign activation Automated scheduling, dayparting, and budget pacing ensured campaigns ran without gaps every day. The extended blackout periods that had cost the brand significant impression share in November were eliminated entirely. Daily spend remained active and performance compounded week over week as optimizations had time to take effect. Broader keyword coverage The keyword discovery capabilities of Unified Ad Manager (UAM) expanded active targeting from 77 to 130 keywords (a 60% increase). The expanded keywords were kept tightly aligned to the brand’s product categories (tonic water, soda water, ginger ale, cocktail mixers), so reach grew without ignoring relevance. Dynamic bid and budget optimization The Unified Ad Manager (UAM) also helped the brand adjust bids continuously based on real-time performance signals, rather than keeping all bids static across all keywords. Budget was also systematically reallocated toward product listing campaigns, which delivered stronger ROAS, received a larger share, while product recommendation spend was scaled to a more appropriate level. The Day-Parting Insight That Showed Major Impact Of all the changes made between November and December, automated day parting showed significant and measurable results. In November, spend was distributed without any time-based structure. This meant ad budget was regularly consumed during periods of low traffic, like early mornings when conversion rates were lowest. The ROAS across those campaigns was 0.92x: spend was effectively generating less than ₹1 of revenue per ₹1 spent. In December, with unified ad management platform, the brand identified evening hours as the peak demand window for quick commerce beverage purchases and shifted 99% of active spend there. The outcome was a ROAS of 1.58x on those campaigns (a 72% improvement), driven not by increasing the budget but by changing when it was used. This means the ads were showing at exactly the moment purchase intent was highest, improving both conversion rates and the return on every rupee spent. The Impact D2C Brand Saw After Leveraging Campaign Management & Insights from Unified Ad Manager Metric Nov 2025 Dec 2025 Change Orders 1,211 2,204 +82% Revenue ₹3.3L ₹5.3L +60% ROAS 0.92x 1.58x +72% ACoS 69.2% 63.6% −8.1% Cost per Order ₹189 ₹153 −19% Active Keywords 77 130 +60% Ad spend grew by 47% month-on-month. Revenue grew by 60%. This gap where revenue outpaces spend is the signature of genuine efficiency improvement, not simply higher investment producing higher output. In simple terms, each additional rupee invested in December generated more revenue than it did in November, even as order volumes nearly doubled. This wasn’t just seasonal demand; it points to smarter, more optimized campaign performance. The same trend is visible in cost efficiency. Cost per order dropped from ₹189 to ₹153 (a 19% reduction) despite operating at twice the scale. The result: higher volumes, lower acquisition costs, and stronger returns, all at the same time. Key Takeaways: What Ecommerce & Quick Commerce Brands Actually Get When They Move to Unified Ad Management The results above are specific to one brand and one platform, but the underlying problems they solved are not. Fragmented campaign visibility, reactive bid management, unstructured budget spend, and keyword gaps are patterns that show up across categories and platforms whenever campaigns are managed manually. Here’s what changes when marketers start leveraging Unified Ad Manager by mFilterIt. You stop managing campaigns across platforms in silos A unified interface brings everything together in one place, making it easier to create, update, and monitor campaigns. This reduces operational effort and improves consistency in execution. Bids adjust in real time, not once a week when someone reviews the dashboard With dynamic bid management and optimizations, bids are continuously updated based on performance signals like ROAS and traffic trends. This ensures campaigns stay aligned with performance at all times, even beyond working hours. Budgets start moving towards what’s actually working A rule-based budget reallocation engine shuffles spend across campaigns in real time, shifting budgets towards high-performing sources and pulling back from lower-ROAS ad types. AI and ML-based campaign rules replace guesswork or manual decision-making A custom rule engine lets brands define the conditions under which bids should increase, budgets should be reallocated, or campaigns should pause. It executes those decisions automatically when the conditions are met. This removes the human bottleneck from time-sensitive optimisation and ensures the campaign strategy is actually reflected in live spend, not just in a planning document. Performance is tracked at the granular level Keyword-level, campaign-level, and platform-level logs tell you why and where to fix it. Detailed activity tracking across campaign overview, ad group, keyword, and rule engine logs helps in accuratedecision-making to improve campaign efficiency at scale. Therefore, by bringing structure, automation, and real-time decision-making into campaign management, the brands can grow faster while becoming more efficient at the same time. Ready

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

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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5,000+ LPG Booking Scam Accounts Detected: How Fraudsters Exploits Essential Services During Crisis

Blog, Brand Protection / mFilterIt Experts

Consumer behaviour shifts rapidly during times of uncertainty. People seek immediacy, convenience, and assurance, especially when it comes to essential services. Unfortunately, this urgency is exactly what fraudsters capitalize on. Our fraud detection experts identify various types of scams day in and day out. And what we have witnessed recently is not just another wave of financial fraud, but a shift in how fraud embeds into everyday customer journeys. We have identified scams related to LPG gas bookings due to the shortage, considering the critical situation outside. Fraudsters are leveraging a sophisticated mix of fake websites, phishing scams, social media posts, and messaging platforms to appear legitimate. This is a critical inflection point. Because, unlike traditional investment scams that rely on greed or high-return promises, these scams exploit need and necessity. They target consumers in moments of urgency, when trust is assumed without verification. How Fraudsters Operate Through LPG Booking Scams? Even scams during national events and crises like war situations, pandemic waves, and natural disasters, etc. follow a structured model designed to guide users from discovery to payments. Here’s how LPG booking scam works: Fake messages creating urgency and panic Fraudsters begin by creating panic-driven messaging such as “gas connection will be disconnected” or “limited stock available.” These messages are often linked to ongoing situations like shortages or supply concerns. The objective is to push users into taking quick action without verification. Creation of digital assets like fake websites, social media handles, etc. Once attention is captured, users are directed to fake platforms. These include lookalike booking websites, misleading landing pages, or sponsored links that closely resemble legitimate services. Their purpose is to create a convincing first impression and reduce suspicion. Bulk messaging-based attacks Fraudsters also rely heavily on messaging-based platforms like WhatsApp, Telegram, SMS, etc. to circulate fake booking links, payment links, and even KYC update requests, often framed as urgent actions that need immediate attention. This coordinated, multi-channel presence creates repeated exposure, making the scam appear more legitimate and increasing the likelihood of user interaction without proper verification. Customer care impersonation and call-based fraud In many cases, fraudsters directly interact with users by posing as customer support representatives or gas agency officials. They guide users step-by-step, using scripted conversations to build trust and create a sense of legitimacy. UPI fraud and unauthorized transactions Once trust is established, users are asked to make payments through UPI or bank transfers. These payment details are not linked to official entities, and funds are often routed through multiple mule accounts, making tracking and recovery difficult. Disappearance after payment and no service delivery After the transaction is completed, fraudsters cut off communication. The websites, phone numbers, or links used during the interaction become inactive, leaving users without any confirmation, service, or refund. Why The Rise in Digital Scams Demands a Broader Industry Response? Digital scams or investment scams are not just platform problems, nor solely regulatory challenges. But an ecosystem issue. Brands, digital platforms, regulators, and brand protection technology providers need to collectively rethink how trust is built and protected online. Because the question is no longer “Is this ad or website safe? It is now “Is this interaction authentic?” And that requires a fundamental shift from reactive moderation to proactive intelligence and continuous monitoring across the entire digital landscape. How mFilterIt Helps Identify Digital Threats Using OSINT Technology To tackle such evolving digital scams, the approach needs to go beyond surface-level fraud detection. Here’s how mFilterIt’s brand protection solution helps: Continuous monitoring across digital ecosystems including websites, search engines, social media platforms, and app environments, etc. to detect fake booking websites, phishing links, brand impersonation fraud, and malicious APK distributions. Identifies patterns and anomalies such as sudden spikes in crisis-related keywords (e.g., “urgent booking”), coordinated campaigns, and emerging fraud narratives linked to real-world events. Tracks misuse of brand names, government schemes, and crisis-driven messaging, helping uncover fake ads, sponsored scam campaigns, and misleading promotions designed to build trust. Analyzes social media and messaging platforms to detect fake posts, viral scam creatives, and coordinated disinformation campaigns, including how such content is amplified across networks. Combines AI-led intelligence with human validation to verify critical elements such as payment instruments, UPI IDs, bank accounts, and other suspicious activity signals. Maps complete fraud infrastructure, including linked domains, payment handles, phone numbers, and email IDs, to connect multiple fraudulent assets to a single organized network. Moreover, once fraud is detected. The brand protection solution also helps in: Reporting fraudulent assets to platforms, hosting providers, and domain registrars for takedown. Shares structured intelligence with law enforcement and relevant authorities, including fraud URLs, payment details, and evidence to support investigation. Enables faster identification and blocking of mule accounts, fraudulent payment channels, reducing financial loss and limiting further spread. Issues early warnings and alerts to help prevent large-scale victimization before such scams escalate. Conclusion: Staying Ahead of Evolving Digital and Investment Scams The rise of LPG booking scams highlights a larger shift in how fraud operates today, moving beyond traditional formats and embedding itself into everyday consumer journeys. As fraud becomes more contextual, fast-moving, and harder to detect, relying on reactive measures is no longer enough. Addressing this requires continuous monitoring, deeper intelligence, and faster action to identify and disrupt fraud networks before they scale. To stay ahead of evolving scams, start monitoring and taking action before fraud reaches your consumers. Get in touch with our brand protection experts today. FAQs What is an LPG booking scam? An LPG booking scam is a type of digital fraud where scammers impersonate gas service providers using fake websites, phishing links, or messages to trick users into making payments or sharing sensitive information. These scams often appear during high-demand situations or supply concerns. How does an LPG booking scam work? Fraudsters create urgency through fake messages, redirect users to lookalike booking websites, build trust via calls or messages, and then collect payments through UPI or bank transfers. Once the payment is made, they disappear without delivering any service. What is brand

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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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