Blog - mfilterit

AI Slop: The New Brand Safety Crisis

Blog, Brand Safety & Suitability / mFilterIt Experts

Brands spend millions daily on programmatic advertisements with the belief that the ads are shown to genuine users on authentic websites. The truth is that an increasing number of this inventory does not cater to any readership, rather to bids. The advent of artificial intelligence has led to the creation of content farms that produce articles purely based on the objective of getting advertised. The term used for such content is referred to as AI slop and poses one of the most significant challenges to media quality and brand safety in digital advertisement today. AI has made it possible to write articles, generate graphics, and create landing pages much faster than even the most professional editorial teams ever could. Such high speeds allowed for incredible scalability, but the downside was that it led to something worse – scalability without substance. Content produced by artificial intelligence in large quantities often does not inform anything; rather, the content is geared towards gaining traffic and earning from ad views. That’s what AI slop is all about. AI Slop is not only a problem in terms of content generation in digital marketing today; it has also become a problem of media quality The numbers back this up: Top-performing advertisers converted 54% of their programmatic spend into qualified impressions, while lower-performing advertisers converted only 32.1% — a 21.9 percentage point gap, the largest the benchmark has ever recorded. (Source) MFA (Made-for-Advertising) exposure climbed to 1.1%, with the same benchmark flagging AI-generated low-value content, AI slop, as an emerging subtype that now demands ongoing monitoring and active mitigation. During peak season, this number is expected to rise exorbitantly. In this blog, you will discover – Why AI Slop is the next evolution of MFA websites What is the mechanism behind AI slops? How AI slops actually cost a brand? How can brands identify and mitigate AI slop? Why AI Slop is the Next Evolution of MFA Websites Made for Advertising (MFA) sites have always been one of the easiest and most prominent ways for fraudsters to exhaust brand’s budgets. Traditional MFA sites relied on outsourced content, clickbait headlines, and SEO manipulation. AI has dramatically reduced the cost of creating such environments. Today, a single operator can launch hundreds of AI-generated websites, each capable of attracting hefty programmatic advertising budgets despite offering little value to consumers. It is not just text either. AI slop now includes AI-generated images, videos, and even full social media accounts, completed with fake followers and engagement, designed to appear like legitimate, active communities. The key thing to understand is these are not passion projects gone wrong. They are built deliberately to attract viewability through advertising money and irrelevant ad placements. None of it is meant for a human reader. It’s meant for an ad auction. What is the Mechanism Behind AI Slops? AI slop sites are built on one principle – creating content in bulk but the mechanism that goes from creating to exhausting budgets is what makes it critical for brands. Following is the mechanism behind AI slops – AI slop thrives because every layer of the digital ecosystem relies on automated signals instead of human judgment. Artificial intelligence has made it very easy to create content so publishers can make thousands of pages every day. Search engines do not really look at how good the content’s they just look at things like keywords and links and how new the content is. The content made by intelligence is usually made to do well with these things even if it is not really worth reading. When these pages get ranked high or shared, they get a lot of visitors. This can be used to make money from ads. The companies that handle ads mainly check if the page is working properly and not if the content is actually good. Long as the page is safe and follows the rules and is about the right topic it can usually have ads on it. People who buy ads usually buy them in amounts through computer programs, so humans do not often see where the ads actually show up. There are tools to catch content, but they do not catch content that is just not very good. This creates a circle: the money from ads pays for content made by artificial intelligence, which makes more space for ads. The system is not really broken. It is just based on things that are supposed to show quality and artificial intelligence content has gotten very good at pretending to be good without being worth anything. Artificial intelligence content is very good at this. It keeps getting better at making content that is not really good. How AI Slops actually Cost a Brand? AI slops cost the brand more than money. The loss can also be reputational, and once lost, a reputation might be even harder to fix than gained. Here is what the effects of AI slops actually do to brands: Measurement Distortion: AI Slop environments frequently generate inflated engagement metrics, creating a false perception of campaign success while delivering limited business outcomes. Audience Quality Degradation: Even when impressions are technically viewable, audiences interacting with AI-generated content environments often demonstrate significantly lower engagement and conversion rates. Optimization Risks: Media buying algorithms may unintentionally optimize toward cheaper AI-generated inventory, directing larger portions of budget toward low-value placements over time. Lack of Trust: Consumers believe that brands appear in trustworthy environments. Failing to do that can undermine their trust and brand reputation, which is more costly than gaining it. How Brands Can Identify and Mitigate AI Slop AI slop is becoming an increasingly serious challenge for brands. But identifying and avoiding these environments doesn’t have to be a guessing game. With mFilterIt’s comprehensive and always on brand safety solution, brands can separate quality inventory from low-value placements through three layers of detection. Each layer is designed to catch signals that the others might miss, ensuring ad placements meet stricter standards before they are considered safe. Repetitive Content Detection With our manual checks keeping hawk’s eye at your ad placements, we ensure your ads are not placed in slopped environments that do not bring any value to your content. Each pattern is manually validated, so brands can trust the accuracy of what’s flagged and what’s not. Ad Visibility & Ad Block Analysis An ad served isn’t the same as an ad seen. This layer confirms that ads were actually rendered, visible, and placed in genuinely viewable environments, not hidden, buried below the fold, or blocked, and flags pages so cluttered with ad units that they were clearly built for impression volume rather than user experience. Ad stacking is one

AI Slop: The New Brand Safety Crisis Read More »

Amazon Prime Day 2026: 6 Digital Shelf Gaps Brands Must Fix to Maximize Sales

Blog, Digital Shelf Analytics / mFilterIt Experts

Amazon Prime Day has a way of creating big expectations. Last year, U.S. online sales crossed $24 billion during the event, and every brand understandably wants a share of that demand. Teams spend weeks planning promotions, increasing media budgets, forecasting inventory, and preparing for a spike in traffic. Then the sale ends. Some brands exceed their targets. Others are left reviewing dashboards and asking the same question: “We had the discounts. We had the inventory. We invested in advertising. So why didn’t the results follow?” The answer is rarely found in the sale itself because Prime Day does not create performance. It magnifies the opportunity for products that already exist. A product page that converts poorly in June will struggle even more when millions of shoppers arrive in July. A problem in visibility that is costing you a few hundred impressions on a normal day can translate into lost sales during Prime Day period. These gaps seem small but they cost brands a lot when traffic peaks. The brands that win Prime Day are usually not reacting during the event. They are the ones that outperform competitors have already addressed the issues that can cost them visibility, trust, and conversions. Here’s what you will discover: The critical digital shelf gaps that can hurt Prime Day performance The business impact of these gaps on traffic, conversions, and sales How to proactively fix them and maximize Prime Day results Gap #1: Your Product Pages Aren’t Converting Traffic Efficiently The volume of the traffic barely leads to anything when the quality of the traffic is weak. Brands get enough traffic on their product pages, but it is not ideal if the traffic is not converting into outcomes. During Prime Day, customers have multiple options available for the same product which they compare and make decisions within seconds. If your product listings do not clearly define relevant information like product quantity, nutritional value, age suitability, etc., shoppers move on. Hence, key factors like product titles, weak images, outdated A+ content, and inconsistent descriptions all contribute to a reduced conversation rate. Brands who do not focus on this aspect, end up spending putting more budgets in acquiring new traffic while simultaneously struggling to maximize revenue from the visitors they already have. One of the most successful strategies for Prime Day is – brands must treat PDP optimization as a conversion strategy, not a content exercise. Gap #2: Your Brand is Visible, But Not Visible Enough Being listed on Amazon does not guarantee discoverability. Prime Day significantly increases search activity across categories. Brands often discover that competitors dominate valuable search terms while their own products remain buried deeper in search results. This visibility gap affects both organic and sponsored placements. When competitor’s own category keywords, they capture demand before shoppers even reach your listing. The challenge isn’t simply knowing where you rank. The challenge is understanding: Which keywords competitors are winning. Where your Share of Shelf is declining. Which categories are driving visibility losses. How search trends are shifting ahead of Prime Day. Without this intelligence, brands often increase advertising spend without addressing the underlying discoverability problem. Gap #3: Pricing Decisions Are Based on Assumptions Instead of Intelligence Prime Day is often viewed as a discounting event and somewhere back, the pricing of the product remains ignored until it shows the impact post-performance. Many brands enter the sale with pricing intelligence strategies that are fixed while competitors adjust pricing dynamically throughout the day. Even small pricing shifts can influence customer’s decision through visibility, Buy Box ownership, conversion rates, and category rankings. The issue isn’t offering the deepest discount. The issue is understanding where pricing influences buying behavior and where it unnecessarily erodes margins. Gap #4: Buy Box Losses Are Happening Without Your Knowledge For brands selling through multiple sellers, Buy Box ownership can determine the difference between winning and losing Prime Day. A product may rank well, generate traffic, and even have strong reviews. But if the Buy Box shifts to another seller, most purchase intent follows it. What makes this particularly dangerous is how quickly Buy Box ownership can change especially when there are inventory fluctuations, pricing changes, and seller performance issues. Many brands discover these losses after sales performance declines. By then, recovery becomes significantly harder. Prime Day rewards brands that monitor Buy Box health proactively rather than reactively. Gap #5: Stockouts Create Damage Beyond Lost Sales Running out of inventory during Prime Day is usually viewed as a temporary sales problem but Amazon treats it differently. When a product becomes unavailable, the consequences often extend beyond the immediate loss of revenue. Ranking positions weaken. Visibility declines. Recovery takes time. Brands frequently calculate the cost of the missed transactions. They rarely calculate the cost of rebuilding the momentum that disappeared with them. This is why inventory planning should not be viewed purely as a supply chain exercise. Inventory influences discoverability. Inventory influences ranking. Inventory influences revenue after Prime Day ends. Few digital shelf gaps create a longer-lasting impact. Gap #6: Reviews Are Telling You What Prime Day Results Will Look Like Customer reviews are often treated as a reputation metric but they are actually a forecasting tool. Months before Prime Day arrives, customers are already documenting the issues that could hurt conversion rates during the sale. Packaging complaints. Product quality concerns. Misleading descriptions. Expectation mismatches. Recurring delivery frustrations. Brands that analyze review sentiment early can identify recurring issues, improve product communication, and remove conversion barriers before Prime Day traffic arrives. The brands that ignore those signals often discover them again through lower conversion rates during Prime Day. By then, the feedback has already influenced thousands of buying decisions. The Hidden Cost of Digital Shelf Gaps Most brands think of digital shelf optimization as a visibility exercise and less of a regular exercise, but the reality is, it is both. Every missing product image, outdated product description, pricing inconsistency, or poor review by customers leave shoppers with questions, impactingt their buting journey and your brand’s revenue. During normal periods, these gaps may appear manageable but during Prime Day, they become performance bottlenecks. When millions of shoppers are actively comparing products on one of the largest ecommerce platforms, Amazon’s algorithm becomes less forgiving. Listings with stronger content, better engagement signals, higher ratings, and stronger sales history receive greater visibility. This creates a compounding effect: Better visibility generates more clicks. Better product pages generate more conversions. More conversions strengthen ranking signals. Stronger ranking signals create even more

Amazon Prime Day 2026: 6 Digital Shelf Gaps Brands Must Fix to Maximize Sales Read More »

Is Your Product Page Analysis Turning Shoppers Away?

Blog, Digital Commerce Intelligence / mFilterIt Experts

Your organic traffic is dropping but do you look for the most obvious reason behind it? Teams run audits on campaign spends, revisit keyword strategies, and tweak bidding structures. Rarely does anyone open the actual product page and ask the most obvious question – is this page actually doing its job? When a perfect page analysis is neglected, right information regarding your product doesn’t reach to customers, making them abandon your product before completion. Suppose a mother visits your product page in order to search for a highly nutritious powder for their children but she did not find the ingredients on the product page, she did not find the measurement per scoop, what will be her quickest action? To explore similar products. There will be no notification regarding abandoned cart or bounce alerts. This is just one of the many cases that show the importance of perfect page analysis. It shifts the focus from just getting discovered to actually earning the purchase, making sure that when a shopper finds your product, the page does everything it needs to close the gap between interest and decision. In this blog, we will discover – Why optimizing product page is crucial for brands 6 product page optimization factors every brand must monitor Operational challenges of page optimization How product page optimization enhances brand’s product performance What is the Importance of Optimizing your Product Page? Page optimization for brands is crucial let’s understand this from an example – Take a brand managing 200 SKUs across platforms. At any given point, some percentage of those listings have a content issue. Not a catastrophic one but something subtle. A title that drops the primary keyword because a marketplace algorithm reindexed it. A main image that appears blur on a mobile screen. This is the operational reality that most ecommerce brand teams aren’t built to catch. They are structured around campaigns, not continuous page-level monitoring. Perfect page analysis runs a continuous check across every live SKU title structure, image compliance, keyword presence and flags drift the moment it happens, not three months later when the rank loss is already real. Because marketplaces are live systems and treating your listings as set-and-done is how you hand over the top slot to someone who simply had a cleaner page on the right day. Six Product Page Optimization Factors Every Brand Should Monitor Page analysis is not a one-time thing. It is the discipline of tracking everything that affects whether a shopper finds your product, trusts it, and buys it. That breaks down into six areas based on content and creative parameters that brands must monitor – Product Title The title is the first thing both the shopper and the marketplace algorithm read. It needs to work for both. At minimum, a title should clearly mention the product name and the details that matter — size, quantity, variant, measurement unit. A protein powder listed without its weight, or a baby lotion that doesn’t mention the age group, makes the shopper work harder than they should. And when shopping online, most people won’t bother working harder. They will just pick the listing that was clearer. Content Quality The description exists to answer questions before the shopper thinks to ask them. What is this? How do I use it? What’s in it? How much do I get? For categories like food, supplements, skincare, or baby products where the purchase feels personal, an incomplete description doesn’t just leave gaps, it creates doubt. Expiry formats, ingredient lists, usage steps, these aren’t optional extras. They are what make a shopper feel confident enough to add to cart. Support images within the description matter too. If they are blurry or don’t add anything to what is written, they are just taking up space. A+ Content By the time a shopper reaches A+ content, they’re already interested. What they need at this point is a reason to commit. Comparison charts, related products with rich mediax description. These help shoppers make up their mind faster. The mistake most brands make is treating A+ content as a branding exercise. It isn’t. It is the last conversation you have with a shopper before they decide. Make it count. Visuals Shoppers look at the image before they read anything else on the page. A blurry thumbnail or a cluttered hero image can end the consideration right there before the title, before the price, before anything. And platform standards change. An image that worked fine two years ago may not clear compliance checks today. A visual that looks sharp on desktop may render poorly on a phone screen. Visuals aren’t a one-time job. They need to be revisited the same way any other live content does. Ratings and Reviews A 4-star rating or above builds trust quickly, especially for a first-time buyer who knows nothing about the brand. But the number alone isn’t enough. The actual reviews tell you what is working and what isn’t. If multiple customers mention confusing instructions or damaged packaging, that’s a signal both for the product and the page. Customer-uploaded review images also matter. These are unfiltered, real-world photos of your product. If they consistently look off or don’t match what the brand imagery shows, no amount of polished A+ content will make up for it. How Product Page Optimization Enhances Brand’s Product Performance Product page optimization is one of the most effective strategies that many brands of various categories know. mFilterIt ran a deep-dive category analysis on the Atta segment on Flipkart Grocery. What the data revealed was striking, some of India’s most recognisable FMCG names are being outpaced on digital shelves by brands that simply optimise better. What did the PDP Audit Track? Support Images – Number, quality, and variety of images beyond the primary product shot. Video Presence – Whether a product video exists on the listing. Video-enabled PDPs consistently see higher dwell time and improved add-to-cart rates. Description Length – Depth and completeness of written content. Thin descriptions signal low relevance to platform algorithms and fail to address shopper intent. Key Findings: Overall Brand’s Content Score for last 3 months: These scores do not just highlight the basic content errors but the real content gaps that were creating major challenges in listings. This data highlights categories based on which their product page was analyzed including and the highlights were- The analysis revealed a clear divide between how category leaders and other established brands approach their product pages. Brands such as Aashirvaad, Fortune, and Pillsbury consistently delivered more complete and informative product experiences, with stronger content across key PDP elements. In comparison, listings from Amul, Organic Tattva, and 24 Mantra Organic showed noticeable gaps in content depth and shopper-focused information. These shortcomings

Is Your Product Page Analysis Turning Shoppers Away? Read More »

5 Signs of Domain Spoofing: The $9 Billion Fraud Hiding Inside Your Programmatic Buy

Blog, Ad Fraud / mFilterIt Experts

Would you hand your best creative to a fraudster? Of course not. But you might already be doing exactly that, and your dashboard would never tell you. Brands pour millions into programmatic advertising with campaign strategy, audience precision, and brand safety tooling. The reports look spotless and the placements appear premium too. But they don’t show what’s happening underneath. Somewhere between your media buy and the actual impression, a fraudster quietly swaps the domain. Your ad which is built for a trusted, high-quality environment, ends up serving on junk inventory. The bill still comes to you. The performance still gets logged. And nothing in your reporting raises a flag. This isn’t an edge case. It’s a $9 billion problem. Estimated global annual losses from domain spoofing alone are projected to exceed $9 billion and that’s a conservative figure. (Source) Budgets meant for premium publishers are being silently rerouted into low-value, risky, and outright fraudulent inventory — every single day. The cruellest part isn’t the money lost. It is the wound created on brand reputation. That’s what makes domain spoofing in programmatic advertising so dangerous. It doesn’t announce itself. It just quietly drains your budget while wearing the face of a legitimate buy. This is exactly what we will cover in this blog – What is domain spoofing? What are the mediums of domain spoofing? 7 Signs to Watch for If Your Campaigns Have Become a Prey of Domain Spoofing How a Third-Party Intelligence Framework Solves Domain Spoofing? What is Domain Spoofing? Domain spoofing is a kind of ad fraud technique where brand’s ads run in the environments, brands never paid for at the very first place. These spaces are not relevant for brands, and such placements only inflate metrics like impressions and clicks for brands, coming from irrelevant spaces. What are the Mediums of Domain Spoofing? Domain spoofing in digital advertising occurs through various mediums, making it nearly impossible for brands to identify. Most prominent ones include – Open Ad Exchanges Fraudsters manipulate bid requests in open marketplaces to make low-quality inventory appear as premium publisher inventory. The lack of direct publisher relationships makes spoofing easier at scale. Made-for-Advertising (MFA) Websites MFA sites are those where ads run in bulk only to exhaust ad revenue and give false impression of a successful campaign to brands hence no real user engagement is involved. Fraudsters often use them to host spoofed inventory or imitate premium publisher environments. AI-Generated Publisher Networks Fraudsters now use AI to rapidly create fake publisher websites, synthetic content, and realistic engagement patterns, making domain spoofing a major tactic for disguising fraudulent inventory as legitimate media properties. 5 Signs to Watch for If Your Campaigns Have Become a Prey of Domain Spoofing Brands running marketing campaigns in programmatic advertising can look out for the below signs to check if their ad is present on spoofed domains. Such early warning signals, if identified, can safeguard brand reputation – Unusually high impression volumes at suspiciously low CPMs If a domain delivers exorbitantly high impression counts with cost-per-impression below market levels, brands must question the credibility of such domains. Domains that are fraudulent reduce their prices to win bids at scale while barely adding any value. Zero or near-zero engagement rates despite strong reach Any legitimate user action will include clicking, hovering, or scrolling. If your marketing campaigns are just recording impressions without any engagement and user action, the traffic is certainly non-human. Traffic from unexpected geographies You targeted US-based audiences for a particular campaign but analytics show that your campaign is receiving traffic from Eastern Europe or Southeast Asia, then it is a major red flag where spoofed domains frequently serve invalid traffic from bot farms in regions inconsistent with the publisher’s claimed audience. Abnormal traffic spikes at odd hours Real human audiences follow predictable patterns every day. If impression delivery rises at 3 AM in your target time zone or shows unnatural uniformity across hours, it suggests that bots are engaging with your campaigns. Publisher’s reported data doesn’t align with your own tracking A meaningful and clear gap between what a publisher reports and what your own ad server or analytics platform records is a classic fraud indicator. How Third-Party Intelligence Frameworks Solve Domain Spoofing Domain spoofing has outpaced the defenses most brands rely on. Standard filters and platform reports were built for a simpler threat landscape- and fraudsters know it. Hence, for a streamlined programmatic advertising brands need an independent verification layer that closes the gap. Here’s what it delivers: Detection of Sophisticated Spoofing Signals Modern spoofing hides in traffic behavior anomalies, suspicious reseller chains, and device inconsistencies that never surface in a standard dashboard. Third-party intelligence actively scans for these signals – stopping digital ad fraud that platform-native filters are simply not built to see. Cross-Platform Fraud Visibility A single fraudster can spoof the same domain across dozens of SSPs simultaneously. When each platform only sees its own slice, the scheme goes undetected. Cross-platform aggregation connects the dots – turning isolated anomalies into visible, actionable fraud patterns. Real-Time Risk Scoring Detection is worthless if it comes after the budget is spent. Real-time risk scoring evaluates inventory quality continuously, blocking suspicious domains before impressions are served — not after the damage is done. Conclusion Domain spoofing doesn’t look like fraud. That’s precisely why it works. The brands winning this fight aren’t just spending more carefully. They’re verifying independently. They’re closing the gap between what their platforms report and what’s actually happening. And they’re treating digital ad fraud detection not as a one-time audit, but as a continuous, always-on layer of their media strategy. Domain spoofing is sophisticated. But it’s not invisible, especially when you are looking with the right ad fraud detection tool. The question isn’t whether your campaigns have been targeted. Given the scale of this problem, the safer assumption is that they have. The question is whether you have the visibility to know for certain — and the infrastructure to act on it before the budget walks out the door. Don’t Let Your Next Campaign Fund a Fraudster’s Operation Claim Your Free Fraud Audit Frequently Asked Questions How does domain spoofing affect advertisers? Domain spoofing wastes advertising budgets by serving ads on fake or irrelevant websites instead of trusted publisher platforms. It also impacts campaign performance, audience quality, and brand reputation. What are the common signs of domain spoofing? Common warning signs include unusually high impressions at very low CPMs,

5 Signs of Domain Spoofing: The $9 Billion Fraud Hiding Inside Your Programmatic Buy Read More »

guide-to-click-fraud -tools-for-marketers

Most Click Fraud Protection Softwares Stop at Detection. Here’s What Marketers Need in 2026

Blog, Ad Fraud / mFilterIt Experts

You know you need an answer when your campaigns don’t perform as you expect them to. Or maybe they do perform, but just on dashboards? Click fraud is no longer limited to just basic bot traffic. In 2026, it has evolved into more sophisticated threats that many traditional detection tools are not equipped to identify. Fraudsters now deploy AI-powered bots that simulate real user behaviour to evade behavioural detection. They operate across performance marketing ecosystems, including Google Search, display, GDN, Pmax, Meta, affiliate networks, app install campaigns, lead generation, and re-engagement campaigns. According to mFilterIt, 18% of global digital ad traffic was invalid in 2025. Moreover, with global digital ad spend projected to reach $866.2 billion in 2026 and $916 billion by 2027, the scale of fraud opportunity is growing at exactly the same rate as advertiser investment. Therefore, the traditional method of identifying fraudulent clicks is not enough. Marketers need a tool that has advanced specifications that can help them stay ahead of such evolving threats. But the question remains, “What exactly should marketers look for in a click fraud prevention tool in 2026?” We have simplified this search for you in this blog. It breaks down: What features should an advertiser prioritize in a click fraud protection tool? How is mFilterIt different from standard ad fraud detection solutions? Why isn’t click-level protection enough for web and app campaigns? So, if you’re comparing solutions or preparing to invest, this is the clarity you need to make the right decision. Key Features to Look for in a Click Fraud Protection Software The right click fraud protection tool is supposed to give you actionable insights, measurable improvements, and cross-channel protection. Here’s what to expect from a tool that actually solves your business problems: Proactive Click Validation Click fraud operates in milliseconds. Your click fraud protection tool should have the capability to detect fraud proactively before it reaches your deep funnel or MMPs. Click validation ensures invalid traffic is flagged and filtered before it drains your ad budget. Here are some checks that a robust solution must perform: Click Repetition Behavior: Spamming on the same Device ID, click and impression injections, IP address repetitions, clusters, and spikes Malicious IPs / VPNs: VPNs, proxies, and data center traffic should be identified in real time Invalid Device Make-Model: Invalid devices detected via User Agent analysis Invalid Geo: Non-applicable geographies flagged via IP address checks Multi-Channel Compatibility Across Performance Campaigns Fraud is not confined to one platform.It spreads across Google Ads, affiliate programs, Meta, DV360, mobile app networks, and even OEM and influencer traffic. Your protection tool should have omnichannel compatibility to work seamlessly across all environments to give you consolidated protection. Integrated platform coverage must include Google Search, Google Search Partners, GDN, DV360, Facebook Audience Network, FB.com, YouTube, Bing, affiliate and direct publisher networks. Know how ad fraud spreads across channels and what you can do about it. Full-Funnel Traffic Scoring from Click to Conversion Since ad fraud doesn’t stop at click, the right click fraud solution doesn’t just analyze a single click. It evaluates the entire customer journey from impression to post-click behaviour, and scores each interaction based on engagement, path anomalies, and conversion likelihood. This helps identify suspicious traffic that may initially look normal. Here’s what full-funnel validation covers: Impressions: Ad visibility and post-bid checks for invalid inventory. Clicks: Real customer clicks vs. bot-generated traffic. Visits: Actual customer visits vs. bot-simulated sessions, scored by intent. Leads/Events: Genuine leads vs. malicious leads; form submissions validated against bot detection and geo-IP matching. Purchase/Sale: Organic sales vs. falsely attributed conversions. Behavioural & Session-Based Analysis for GIVT & SIVT Detection Basic filters cannot catch sophisticated click fraud. It needs a deeper context, including behavioural and session-level analysis. The industry-standard classification splits invalid traffic into two categories: General Invalid Traffic (GIVT): Traffic from known crawlers and bots behaving in obviously non-human ways, easier to detect. Sophisticated Invalid Traffic (SIVT): Advanced ad fraud techniques like advanced bots, click spam, fake attribution, cookie stuffing, ad pixel stuffing, domain spoofing, fake clicks, click injection, punched leads, reseller fraud, duplicate users, and device farms, require ML and behavioural analysis for detection. Therefore, the advanced ad fraud prevention solution must analyze session depth, scroll behaviour, dwell time, bounce rate, and other engagement signals to understand true user intent. This helps distinguish a curious customer from a bot. Check out samples & the difference between GIVT and SIVT here. Device Fingerprinting & IP Analysis Fraudsters often disguise their identity using spoofed devices, anonymized browsers, and rotated IPs. Your tool should apply advanced device fingerprinting to track devices across campaigns, combined with real-time IP reputation scoring to catch proxies, VPNs, fraud networks, and repeated offenders. This helps detect same physical devices with multiple accounts running via app cloning, parallel spacing, or VPN cycling, creating multiple device environments on a single device. IP-only tools cannot detect this. Device environment analysis can. AI-Powered Detection Engine Look for a solution that uses machine learning trained on large-scale, multi-industry datasets to flag both known and emerging fraud patterns. The ability to adapt to emerging patterns is what makes the difference between catching fraud that existed last quarter and catching fraud that is happening now. Sampled detection is a known gap. Fraud actors deliberately keep volumes below sample thresholds to avoid detection. Hence, a key evaluation question should be whether the click fraud prevention tool evaluates every data set, or does it work from a statistical sample. Custom Rules Engine Every brand runs unique campaigns. A good tool should offer flexible custom rule configurations, letting you set thresholds for frequency, geo-targeting, source type, traffic origin, and campaign duration. This enables a fraud strategy that aligns with your media goals and market dynamics. Auto-Blocking, Publisher Blacklisting & Post-Back Control Detection is just one part of the job. Choose a click fraud protection software that instantly: Block invalid clicks in real time before they are billed or processed. Blacklists fraudulent publishers from your affiliate or display ecosystem automatically. Controls post-back firing: affiliate attribution postbacks are fired only for validated, fraud-free events. Transparent Reporting with Source-Level Granularity Data transparency is critical for trust and decision-making. A trustworthy platform offers intuitive dashboards with campaign-level and source-level insights, including traffic diagnostics, high-risk locations, time-based fraud trends, and publisher-level threat analysis. Shareable reports should help your media, product, and performance teams to adjust strategies and make data-driven decisions accordingly. How mFilterIt’s Click Fraud Prevention Tool Stands Apart from Other Competitors Most tools stop at surface-level detection. mFilterIt offers a comprehensive, customizable, and omnichannel ad traffic validation solution that addresses ad fraud at every point in your ad journey, built for marketers who demand accuracy, control, and performance clarity. Here’s what it delivers that point solutions don’t: Capability mFilterIt PPC/Click-Fraud Tool MMP-Native Fraud Tool DSP/Verification Tool App Campaign Fraud Install & Event Validation Available Not available Available Not available Event Spoofing Detection MMP vs backend reconciliation Not available Partial Not available Retargeting / Re-engagement Fraud Full detection Not available Not available Not available Incent Fraud (50+ walls) Available

Most Click Fraud Protection Softwares Stop at Detection. Here’s What Marketers Need in 2026 Read More »

How to Stop Brand Bidding by Affiliates? Can Standard Marketing Tools Track This?

Blog, Affiliate / mFilterIt Experts

“I have a bunch of shady affiliates bidding on our brand search keywords. Every time I ban them, they come back with a new account. I spend hours searching manually on incognito windows, geo-switching, digging URLs, and still miss half of them. Meanwhile, the CAC for our brand keywords keeps rising for no real reason, draining budget and even hijacking our organic traffic.” Many marketers find themselves stuck in this exhausting loop: ban one affiliate for bidding on your brand terms… only to see them resurface under a new account the next day. Despite hours spent checking different SERPs, tracing URLs, or monitoring CPC fluctuations, a significant portion of this activity still goes undetected. Frustrating, right? What makes it worse is the assumption that traditional SEO or analytics tools can catch brand bidding violations. They can’t. Affiliates rotate accounts, cloak redirects, and trigger ads only in specific regions; tactics that most visibility tools were never designed to detect. Let’s understand the problem and solution in detail. What is Brand Bidding in Affiliate Marketing? Why Affiliates Bid on Your Brand Search Keywords? Brand bidding is when someone runs paid search ads targeting your branded keywords (your company name, product names, or trademarked terms) to intercept users who were already looking for you. Affiliates bid on brand keywords for the same reason – to divert organic users to their links, stealing the attribution of a traffic source that was directly coming to your brand’s website. According to our 2025 analysis, we have found that 40-50% of affiliate traffic is generated using invalid patterns. (Source: FICCI EY Report 2026) Your brand terms cost less, are high-intent, and convert far better than generic keywords. This makes an easy shortcut technique for affiliates to boost their numbers without putting in real effort. Instead of driving genuine incremental traffic, they piggyback on the demand you’ve already created through your own marketing. In performance marketing programs, brand keyword violations are among the most common and costly forms of affiliate abuse often going undetected for weeks or even months. Here’s why it gets even more difficult to detect affiliate brand bidding: Affiliates rarely use one stable link. They create multiple domains, subdomains, sub-IDs, and tracking variations to stay undetected. They rotate multiple accounts quickly. Even if you ban one, another pops up immediately exhausting marketers trying to track these fake accounts. They trigger most ads only when you are not looking, like during night hours, in specific cities, and on certain devices, timing chosen to avoid manual detection. Affiliates scale brand bidding activities aggressively during high-demand periods (sales, launches, festive shopping windows) when the value of each conversion is higher to earn more payouts. The result? Rising CPCs and higher CAC. And unless you have visibility into every link and every identity behind the ads, the cycle continues. Get a deeper understanding of affiliate fraud. Explore our complete guide for marketers. Can Standard Marketing & Analytics Tools Track Affiliate Brand Bidding Violations? No, the standard marketing tools cannot detect brand bidding violations. They can provide signals based on data, but miss the kind of visibility required to ensure enforcement and affiliate compliance in search campaigns. What Traditional Tools Can Track What They Cannot Catch Which advertisers appear on search results Real-time brand bidding abuse Old ad copies and landing pages Hidden geo- or device-specific ads Keyword trends and impression data Affiliate IDs and masked redirects Competitor activity and estimated spend Rotating accounts behind the ads Long-term campaign patterns Proof needed for affiliate enforcement Overall search market trends Cloaking and hidden redirects Regular campaign activity Short burst campaigns during peak hours General competitor insights Repeat offenders using new identities What Traditional Marketing and Analytics Tools Can Detect? Show which domains or advertisers appear in your auction over time. Provide historical ad copies and landing-page snapshots when captured during routine crawls. Reveal keyword-level insights such as search share, impression trends, and estimated spend. Help you understand broad auction dynamics and track competitors running stable, long-term campaigns. These insights are useful for market visibility and strategy, but they only reflect a portion of what’s happening. What Traditional Marketing and Analytics Tools Cannot Detect? Affiliates don’t follow a stable pattern for brand bidding abuse. It is fast, fragmented, and intentionally designed to stay hidden. Here’s what they miss: Cannot monitor brand bidding violations in real time. Fail to capture ads targeted to specific geos, devices, or audiences that only some users see. Don’t identify affiliate sub-IDs, masked redirects, or rotating accounts behind the ads. Don’t capture thousands of tiny link variations or provide the forensic evidence (screenshots, redirect chains, timestamps) needed to enforce affiliate rules or deny payouts. Cannot detect cloaking in brand bidding, where a clean page is shown to you, but a redirect is shown to the user. They don’t track short-lived “burst campaigns” during nights, weekends, or peak sale hours. Fail to map patterns of affiliate brand bidding abuse, such as repeated offenders switching identities or redirect networks working in clusters. Why Marketers Need an Advanced Affiliate Monitoring Tool to Detect Brand Bidding? An advanced AI/ML-based affiliate monitoring tool is specifically built to offer visibility and transparency across all affiliate activities. Here’s what all provides: Continuously monitors brand keywords across multiple geo locations and devices. 24/7 coverage, not periodic scans. Detects hidden or time-specific ad triggers. It can catch short bursts and late-night campaigns. Captures every link and ad variation, even thousands of them. Including tracking parameters, sub-IDs, and UTM permutations. Identifies the source of each violation by mapping the publisher, sub-publisher, affiliate ID, or the redirect owner behind the ad. Generates enforceable evidence. Full screenshots, timestamps, and the redirect/log trails that serve as proof. Alerts you immediately when an unauthorized ad appears. Proactive notifications that allow fast action. Provides daily/weekly reports for pattern detection. Aggregate findings into actionable intelligence for program decisions and payout validation. Check out the affiliate monitoring audit checklist every brand needs for fraud-free growth. Foxtale’s 21% CPC Dropped: How mFilterIt Helped Them Combat Brand Bidding Foxtale, the fast-growing skincare brand, invested heavily in TOF and video campaigns to boost search volumes and drive high-intent users to their website. However, they noticed CPCs were rising on brand search terms by 25–30%, even though demand was strong. Search scalability was getting harder, and ROAS was dropping. The Challenge Ad networks and affiliates were secretly bidding on Foxtale’s brand terms. Manual checks barely caught a fraction of what was happening. High-intent traffic was being hijacked, increasing Foxtale’s acquisition costs. What Monitoring Revealed 3,436 unique links were found bidding on Foxtale’s branded keywords. Many ran only in specific locations or time windows. Daily evidence-based reports helped the team take action immediately. The Results Within weeks, Foxtale saw a 21% drop in CPC, improved ROAS, and clearer

How to Stop Brand Bidding by Affiliates? Can Standard Marketing Tools Track This? Read More »

Types of Online Scams Brands Need to Watch Out For

Blog, Brand Protection / mFilterIt Experts

In its March 2026 Global Financial Fraud Threat Assessment, global fraud losses have climbed to a staggering $442 billion. You still believe your brand is untouched? Every second, somewhere on the internet, a brand is being impersonated. A customer is being deceived. And a business is losing more than just money; it’s losing trust. Imagine a fraudster impersonating your brand by using your logo, your colors, and your tone, keeping hawk’s eye on your customers. They camouflage themselves in your brand and your customers see the difference only when it is too late. The modern fraud ecosystem is growing more sophisticated. Fraudsters are sitting at every touchpoint including the ad your customers clicked on, the website they landed on, and the checkout they trusted. Every interaction is an opportunity. Fraudsters have known this for years. The question is, do brands know it too? In this blog, we unpack the many faces of brand fraud and how brands can fight with them. Breaking Down the Modern Fraud Ecosystem: Types of Online Scams Financial fraud does not exist in one corner of the room. It is part of a broader and growing digital fraud ecosystem that includes: Shopping Scams Where fraudulent websites and social media sellers deceive customers on account of goods that never arrive or are nothing like what was advertised. These spike dramatically during festive sales seasons, exploiting consumer urgency. Take this example. This fake website is a near-perfect clone of top ecommerce platform storefront – Big Billion Days banner, product categories, familiar layout and all. It’s a shopping scam designed to take customers’ money for orders that will never arrive. This is what modern fraud looks like not obviously fake, but deliberately convincing. And when customers realise they’ve been deceived, the reputational damage doesn’t fall on the fraudster. It falls on brands who become they prey of brand impersonation and create ruckus for both brands and customers. Carding Scams Under this, stolen credit and debit card details are systematically tested for validity and then sold or used for unauthorised transactions, often available publicly over social media channels. Job Scams Fraudsters take advantage of the vulnerability of job seekers by posing themselves as genuine recruiters to extract money or personal data from desperate job seekers. The stolen data travels to a secondary black market, compounding the harm way beyond the initial victim. This Telegram channel is a clear example of how scammers use messaging platforms to promote fake task-based earning opportunities. The group tricks users with an easy money tactic, manipulating them to post reviews, like content, or complete simple online tasks. These offers are designed to look easy and harmless, designed especially for students or people looking for part-time income. At first, scammers may even pay small amounts to build trust. Once users become active, they are often asked to deposit money, share personal details, or complete “premium tasks” that lead to financial loss. Such fraud groups rely on urgency, easy earnings, and social proof to attract victims. Read in detail about investment scams Subscription Scams Here, fraudsters sell fake or pirated subscriptions to OTT platforms and apps at discounted rates, making both the end customer and brand, its ultimate prey. This Telegram channel shows how cybercriminals use online groups to illegally sell digital accounts and subscription access at extremely low prices. These channels often circulate stolen credentials, hacked accounts, or unauthorized shared access for popular streaming and premium platforms. To appear trustworthy, such groups use large member counts and disclaimers like admins are not responsible for online scams. Innocent users who engage with these marketplaces risk privacy and financial fraud subsequently leading to customer distrust. Each of these scam types shares a common objective of exploiting the digital equity that brands have built for years, using it as a camouflage to deceive consumers who have no way of differentiating between fake and real. How Brands can Safeguard Themselves Against Such Online Scams? When the customer trust comes at stake, it becomes brand’s responsibility to tackle it. To combat this ecosystem effectively, brands need a multi-layered intelligence and enforcement framework that works across websites, social media, messaging platforms, marketplaces, and ad ecosystems in real time. Here’s what it covers- Continuous Detection Across Digital Channels Most fraudulent activity begin with one platform but do not remain there, instead fraudsters travel across platforms such as social media platforms, Telegram groups, WhatsApp chats, malicious websites, app stores, and sponsored advertising campaigns. A proactive detection mechanism can help brands: Identify clone sites, fake stores, and imitation landing pages Find scam campaigns that use official branding, logos, graphics, and campaign creatives Monitor messaging apps and social media for scamming groups and scam networks Identify fake applications, phishing domains, and fake sellers By doing this, it ensures there is less time for fraudsters to engage in their activities while limiting consumer contact. Brand Impersonation Detection Today, most online scams attempt to look credible. Traditional detection based on set rules has become increasingly ineffective. AI-powered systems with visual and behavioral detection capabilities can detect brand imitation even with different domains or altered usernames. This becomes important whenever an organization is experiencing traffic spikes from events like holiday sales, hiring, or product launches. Fast Takedown and Removal Processes: Merely detecting such threats is not enough. Brands need more enforceable approach that also takes down fraud entities in real time. An effective takedown must include – Domain suspension requests Fake social profile reporting Telegram and messaging-platform abuse reporting Counterfeiting The faster fraudulent assets are removed, the lower the financial and reputational impact on both consumers and brands. Conclusion These scams are becoming a serious challenge for brands trying to protect customer trust and their gateways are expanding with every new hotspot. But these threats can be stopped – without adding operational complexity. With the right fraud intelligence and brand protection solution seamlessly integrated into existing workflows, brands gain end-to-end visibility across the digital ecosystem, enabling them to detect threats early, accelerate takedowns, prevent revenue leakage, protect customer trust, and preserve brand reputation at scale. Instead of reacting after damage is done, businesses can proactively stay ahead of fraudsters while ensuring safer digital experiences for their customers across every touchpoint. Take control of your digital presence before scams impact your customers, reputation, and revenue. Connect with us to strengthen your brand protection strategy. Frequently Asked Questions What are the most common types of online scams? Shopping scams, job scams, carding scams, investment fraud, and subscription scams are among the most common online frauds targeting consumers and

Types of Online Scams Brands Need to Watch Out For Read More »

What Are Attention Metrics? Why Brands Need to See Beyond Viewability

Blog, Viewability / Shiraz Noor

Marketers have relied on viewability metrics for years to measure ad performance. Afterall, an ad seen means the ad is working, right? But with the auto-play video ads, endless scrolling, and shrinking attention spans, the ad being just ‘viewable’ or ‘seen’ is not enough. That’s the hard truth modern marketers are realizing nowadays. Viewability alone cannot tell whether users actually noticed, understood, or engaged with an ad, or was it getting completely ignored by the audience. That is why the industry has shifted its focus from traditional viewability to a more advanced ad metric, attention metrics, to evaluate ad performance. Attention measurement tools adoption grew 4x from 2022 to 2025. And looking forward, attention-based media buying is projected to grow 4–7x by 2026. (Source: Marketing LTB) In this blog, we’ll talk about: Why are viewability metrics falling short? What are attention metrics? How they differ from traditional viewability? How Valid8 by mFilterIt helps brands optimize campaigns for genuine attention, not just impressions. Keep reading further to know more. Why Is Viewability No Longer Enough? For years, viewability has been the go-to metric to validate ad delivery. According to the IAB (Interactive Advertising Bureau) standard, an ad is considered “viewable” if 50% of its pixels are in view for at least one second (for display) or two seconds (for video). Let’s take an example, think about a banner ad placed in the middle of where the page scroll ends. As per the IAB standard, if it is 50% visible, it will be considered viewed. But in reality, your ad failed to get the attention of your viewers. Or a muted video ad that auto plays in PIP mode, technically viewed, but your user never saw it. This is where improvement was really needed from traditional viewability standards, which didn’t account for whether the user ever saw the ad at all. Moreover, according to the IAB, 47% of buy-side decision makers said they would focus more on attention metrics in 2024, up from just 36% in 2023. (Source: eMarketer) One key reason is that viewability is uniquely vulnerable to ad fraud. Attention signals expose what viewability hides. Viewability is a binary metric. It doesn’t reveal: Whether the user noticed the ad Whether it resonated Whether it drove engagement or action What are Attention Metrics? Attention metrics is a more advanced, holistic way to measure ad engagement. They go beyond visibility and ask: Did the ad actually capture the user’s attention? Rather than relying on a single data point, attention metrics pull together a wide array of proxy measurement signals – behavioral, device context, user intent. Here’s how each signal helps reveal true engagement: Time in View An ad seen for 1 second isn’t equal to one seen for 7 seconds. Example: If a user pauses scrolling and watches your ad for 8 seconds, it signals genuine interest, unlike someone who scrolls past instantly. Scroll Depth How far a user scrolls before encountering your ad can impact its effectiveness. Example: If your display ad is placed lower on a webpage but still gets noticed, it reflects active user engagement, not passive viewability. Position on Screen Ads placed at the top of the page are more likely to be seen but not necessarily remembered. Example: An ad shown at eye-level in the content zone is more likely to draw attention than one placed in the banner blind spot. Audio Status (Mute vs. Unmute) Muted ads play on PIP. Unmuted ads demand attention. Example: If a user unmutes a video, it’s a strong indicator they want to hear your message, far more valuable than just a view. Pause/Play Behavior This signal captures active intent to watch rather than passive exposure. Example: If someone pauses your video mid-way and resumes later, they’re engaged. That’s meaningful attention; viewability cannot track that. Skip Rate & Skip Point In skippable ads, when users skip matters more than if they skip. Example: If 80% of users skip at 3 seconds, your hook isn’t working. If most watch for 7-10+ seconds, you’ve captured their attention. Screen Orientation Device orientation changes reflect real-time distraction or focus. Example: A user flipping their phone from portrait to landscape to watch your video indicates commitment. Switching apps mid-ad signals lost attention. Click or Interaction Activity Clicking, swiping, or engaging with ad elements shows active intent. Example: Hovering over a CTA or clicking to expand a product carousel shows curiosity, an attention metric that impressions can’t quantify. Dual-Screen Behavior This detects whether users are actively watching your ad or multitasking on another screen or app. Example: If a user switches to another app mid-ad (like messaging or social media), it signals attention drop-off, even if the ad was technically in view. The Core Differences: Viewability vs Attention Metrics Challenges Brands Face Without Attention Metrics in Measuring Ad Performance Viewability metrics create blind spots across different ad formats, ultimately affecting how brands measure, optimize, and scale their ad campaigns. Here how: Display Ads Lack of Depth in User Engagement: Without attention metrics, brands can’t differentiate between a view and actual user interest or interaction. No Insight into On-Screen Placement Performance: Ads may be technically viewable but shown in low-engagement zones, leading to misjudged campaign effectiveness. Inability to Identify and Prioritize High-Intent Ad Impressions: Without behavioral data like time-in-view or interaction rates, valuable signals for retargeting and optimization are lost. Exposure to Display Ad Frauds like Ad Stacking: Without attention validation, fraudulent tactics such as ad stacking, where multiple ads are layered on top of one another, inflate viewability numbers while delivering zero real attention. Skippable Video Ads Misleading Viewability and Completion Metrics: Videos may be counted as viewed even when skipped early, hiding poor creative ad performance. No Visibility into Drop-Off Trends: Without skip point tracking, brands cannot identify where audience interest fades or how to refine the first few seconds. Missing Behavioral and Device-Level Signals: Important indicators like mute status or screen changes are untracked, leading to incomplete understanding of attention quality. Non-Skippable Video Ads Over-Reliance on Completion Rates: Assuming full attention just because the ad plays to the end overlooks passive or distracted viewing. No Data on Real-Time Interaction: Without signals like pause/play, mute/unmute, and dual screen observation, there’s no visibility into how users respond during the ad. Blind Spots in Device and Format Experience: Attention can vary significantly depending on screen size, orientation, and playback mode, but these factors remain unmeasured without deeper metrics. Why This Shift Towards Attention Metrics Matters for Marketers The transition from viewability to attention isn’t just a measurement metric upgrade, it’s a strategic necessity in 2025. Here’s why attention metrics matter: Context is King An ad watched with sound on, in a full-screen CTV environment, delivers exponentially more impact than one barely glanced at during a busy scroll. Attention metrics help decode where and how ads are most likely to land effectively.

What Are Attention Metrics? Why Brands Need to See Beyond Viewability Read More »

How Investment Scams Are Draining the Digital Economy and What Brands Must Do Now

Blog, Brand Protection / mFilterIt Experts

The internet promised democratised investing. Instead, it handed fraudsters a megaphone. In 2025, people lost $1.1 billion to investment scams on social media. That number doesn’t include what went unreported. (Source) Today’s scammer isn’t hiding in the shadows. They’re on Instagram, Telegram, and WhatsApp using your brand’s name, your logo, and your customers’ trust to run financial fraud at scale. And when victims realise, they have been cheated, they don’t blame the scammer. They blame you. This isn’t just a fraud problem. It’s a brand impersonation problem. The good news is brands can still tackle with right vigilance and catch scams before they reach customers. In this blog, you will discover – What is an investment scam and its modus operandi What are the types of scams happening around Checklist for brands to identify investment scams How brands can detect investment scams The way ahead for brands What is an Investment Scam and Its Modus Operandi At its core, an investment scam is a deception built on false promises. Fraudsters most of whom operate without any regulatory registration lure individuals with guarantees of extraordinary returns. They do not operate through authorised financial channels. Instead, they solicit investments by extracting personal bank details, UPI IDs, and informal payment methods, making it nearly impossible to trace through conventional means. The modus operandi behind major brand impersonation follows an alarming pattern: Step 1: Build a fake presence Scammers create fraudulent pages, channels, and handles across Instagram, Facebook, Telegram, and WhatsApp. These look credible complete with testimonials, market jargon, and often, the branding of legitimate financial institutions. Step 2: Reach victims at scale Using social media algorithms and unregulated financial groups, they push their content to thousands of potential victims simultaneously. A single Telegram channel can reach tens of thousands without any advertising spend. Step 3: Manufacture trust They engage with victims for days or weeks, sharing fake “proof” of returns, manipulated screenshots, and convincing success stories. By the time a victim invests, they believe thy are in safe hands. Step 4: Collect and vanish Payments are routed through personal UPI handles and bank accounts specifically opened for fraud. Once the money moves, it disappears into a web of mule accounts and the victim is left with no response, no returns, and no recourse. Investment Scams Warning Signs: Checklist for Marketers Following is the key checklist for brands to identify investment scam at an initial stage. Brands must look for – Social media handles impersonating your brand Domains mimicking your URL Ads running under your brand name that you didn’t place Fake customer support handles directing users to external payment links Scam groups or channels using your logo on WhatsApp or Telegram Guaranteed returns or “exclusive” investment opportunities linked to your brand Flash sales or limited-time offers on cloned websites How Brands can Detect Investment Scams? The scale of investment scams is expanding hence the solution must arm up too. For this, a solution that is holistic in nature must be brought in use – A three-step pipeline that works in the background with – Zero integration required Step 1- Discover: AI-powered scam detection across every platform Sentinel+’s automated data harvesters crawl public signals across the web and social platforms to surface individuals running investment fraud before victims lose money. Platforms covered: Google · Facebook · Telegram · WhatsApp · Instagram Step 2- Verify: Human-validated payment trail Trained analysts confirm that payment instruments bank accounts, UPI IDs, and VPA handles are genuinely linked to the flagged individuals. No false alarms reach your bank. Verified across: Bank accounts · UPI / VPA IDs · Human review layer Step 3- Act: Intelligence delivered directly to your bank Verified scammer profiles are packaged and shared with your bank. Accounts get blocked or flagged for action stopping the fraud before it scales further. Actions triggered: Account blocking · Fraud escalation · Direct bank handoff The Outcomes Scammers exposed: Fraud actors identified across platforms before they reach more victims Accounts blocked: Fraudulent payment rails shut down at the source Brand protected: Your customers stop losing money to scams using your name The Way Ahead: Action and Accountability Reactive brand protection is broken. By the time a complaint is filed, the fraudster has vanished — and your customer has already lost money. The answer is always-on surveillance, not faster damage control. Our brand protection solution deploys AI and ML-powered crawlers across Google, Facebook, Telegram, WhatsApp, and Instagram, detecting fake handles, fraudulent payment links, and scam channels the moment they appear. Flagged entities are instantly reported to the relevant financial institution, triggering account blocks before the fraud reaches scale. Human validation sits at the core of this process, confirming that linked bank accounts and UPI IDs are genuinely fraudulent before any action is taken. Speed without precision creates noise. The brands that earn lasting trust won’t be the ones who responded fastest. They’ll be the ones who made sure fraud never reached their customers in the first place. See how proactive scam detection works in real time Frequently Asked Questions What are the most common investment scams? The most common investment scams include fake stock trading groups, crypto investment fraud, guaranteed-return schemes, impersonation of financial brands, and scams promoted through Instagram, Telegram, WhatsApp, and fake websites. Fraudsters use fake testimonials, screenshots, and success stories to build trust before collecting money through personal bank accounts or UPI IDs and disappearing. What are the investment scams on WhatsApp? WhatsApp investment scams usually involve fraudsters creating fake investment groups or contacting users directly with promises of high returns through stocks, crypto, or trading opportunities. These scammers often impersonate financial institutions or experts, share fake profit proofs, and ask victims to transfer money through UPI or personal accounts. How do investment scams affect brands? Investment scams damage brands by misusing their name, logo, and reputation to deceive customers. When victims lose money, they often blame the brand being impersonated, leading to loss of trust, customer complaints, and reputational damage. What are the warning signs of an investment scam? Common warning signs include guaranteed returns, pressure to invest quickly, fake social media pages, unofficial payment methods like personal UPI IDs, and investment offers shared through Telegram or WhatsApp groups. How can brands detect and stop investment scams? Brands can detect investment scams using AI-powered monitoring tools that track fake websites, fraudulent social media

How Investment Scams Are Draining the Digital Economy and What Brands Must Do Now Read More »

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

Blog, Ad Fraud / Jyoti Kalra

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?’

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