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DSA brand bidding

Brand Bidding by Direct Selling Agents in BFSI Industry: Know How to Detect & Prevent

Blog, Affiliate /

For BFSI brands, branded keywords should be the cheapest conversions in the funnel. Instead, they’re becoming the most expensive.  Across banking, insurance, and lending, direct selling agents are aggressively bidding on brand terms, redirecting ready-to-convert users through affiliate links, and claiming payouts for demand the brand already created.  Wondering how it shows in your data?   In the form of higher acquisition costs, misattributed performance, and zero incremental value. In this blog, we breakdown how direct selling agents are using brand bidding technique to hijack organic traffic and how financial institutions can identify and take action against it.   The Problem: What is Brand Bidding by Direct Selling Agents?   Simply put, brand bidding by direct selling agents is when an agent or a partner runs paid search ads using: A brand’s name or related search terms  Brand keywords and product keywords Close variants or misspellings of a brand  Using brand name in ad copy or display URL  Cloaked ads shown only to search engines  This is essentially done to hijack the organic high-intent traffic; people already searching for your brand. As a result, instead of directing the users to the brand’s official website or app, these ads often lead to agent-owned landing pages or alternative conversion paths.  On the surface, these ads seem relevant. Click-through rates are high, and conversions might as well look strong. But the cost is that BFSI brands end up paying for traffic that might have anyway come to them organically.  Why Brand Bidding by Direct Selling Agents is Accelerating in BFSI Industry?  Direct selling agents are compensated based on outcomes like approved leads or disbursals. When that’s the case, any efficient-looking tactic that brings traffic and conversions, including brand bidding violations, becomes tempting.   Outcome-Based DSA Compensation Model  How the traffic is sourced often becomes less visible than the final outcome. This way, brand keywords become an efficient way for partners to meet performance targets, even if the demand already exists.  Limited Real-Time Oversight Across Large DSA Networks  Most BFSI brands work with large, distributed partner ecosystems. Monitoring every keyword, ad creative, and landing page manually across this network impossible. As a result, brand bidding activity is often detected only after performance anomalies or cost leakage becomes visible.  Localized and Intermittent Campaign Execution by Agents  Many campaigns are often limited to specific cities or regions, run in regional languages, or activated only during short time windows. This narrowed and fragmented approach rarely shows up in central dashboards or routine audits. As a result, brand bidding continues quietly without triggering immediate red flags, even though it directly impacts brand search performance and control. The Impact of Brand Bidding by Direct Selling Agents  Brand bidding impacts more than just media budgets for any brand:  You End Up Paying For The Demand You Already Created  When someone searches for your brand, that intent is usually the result of your marketing, your brand presence, or prior engagement. When a direct selling agent bids on that search keyword and routes the user through their own journey, brands often end up paying twice – once for the brand click and again through partner commissions. The conversion still happens, but no incremental demand is created. The existing demand becomes more expensive.  Performance Metrics Become Misleading  Agent-led brand traffic often shows strong click-through rates and quick conversions, which can make CPAs and ROI appear stable. But these metrics don’t always reflect true acquisition performance. In many cases, they indicate branded intent being captured outside the brand’s owned funnel, making it harder to distinguish real growth from demand.  Attribution And Visibility Become Questionable  When brand traffic flows through agent-controlled landing pages, brands lose direct visibility into how users are being handled. Messaging, form design, and data capture move outside central oversight. This fragments attribution and makes it increasingly difficult to understand which channels and partners are genuinely contributing to growth.   Compliance And Brand Risks Also Increase  In BFSI, control over messaging and data handling is extremely critical. Direct selling agents may often use unapproved claims, miss required disclosures, or collect user data in ways that don’t fully align with regulatory expectations. Even when this happens unintentionally, the responsibility ultimately rests with the brand.   Internal Competition Replaces Incremental Growth  Brand keyword abuse by partners competes directly with organic brand search, brand-owned paid campaigns, app or direct traffic. Instead of expanding reach, multiple channels end up chasing the same user. The outcome is higher costs, blurred ownership, and little to no incremental gain.  Know why standard marketing and analytics tools fail to detect brand bidding violations.  How BFSI Brands Can Prevent Brand Bidding Violations by Direct Selling Agents? Solving brand keyword abuse doesn’t require restricting partners. Brands can protect their search presence and with a structured approach using brand bidding detection software and by proactively governing brand keyword usage.  Start By Defining Brand Search As A Protected Channel  Clearly specify what qualifies as brand search (including variants and combinations), who is authorized to bid onx these terms, and what actions constitute a violation. Clear policies create a shared understanding across internal teams and partner networks.   Closely Monitor Brand Keywords Usage Across Geographies And Beyond Own Campaigns  Standard campaign reporting won’t show you ads running outside your own accounts. Continuously monitor third-party ads using brand terms, ad creatives and display URLs, final landing destinations, geographies, devices, and language variations to ensure visibility beyond your own campaigns.   Shift From Reactive Audits To Proactive Monitoring Using Brand Bidding Detection Software  Periodic audits and manual keyword checks are not enough. Brand bidding by direct selling agents tends to be periodic, localised, and low visibility, which means it often goes unnoticed until after impact occurs. A more effective approach is continuous, independent, and advanced monitoring of brand search activity across markets.   Here’s How mFilterIt’s Brand Bidding Detection Software Helps  Solutions like mFilterIt introduce structure and consistency into how brand keyword misuse is identified and addressed:  Comprehensive Brand Keyword Coverage  Monitors brand names, product combinations, misspellings, and close variants across search environments.  End-to-End Ad Journey Visibility  Captures live ads, analysing ad

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Affiliate Compliance in USA

Why the Most Trusted Affiliate Programs in the US Invest in Monitoring

Blog, Affiliate /

In United States, affiliate marketing surged to $11.2 billion in 2025, up from $9.1 billion in 2023, reflecting the growing confidence brands place in this channel.   However, as affiliate ecosystems scale, ensuring consistent brand messaging, transparency, and compliance becomes equally critical. A strong compliance layer not only safeguards brand integrity but also empowers high-quality affiliates, builds long-term trust, and fosters a healthier, more sustainable ecosystem for everyone involved.  To help brands navigate this evolving landscape, this blog explores:  What non-compliance really looks like in affiliate marketing programs  How non-compliance impacts both brands and honest affiliates  What your current affiliate program might be lacking  What effective, real-world compliance monitoring truly entails  What Non-Compliance in Affiliate Programs Look Like Non-compliance by partners is not clearly visible till you dive deeper into the programs and see the difference in the results. This non-compliance creates a pool of violations that drain ROI and come to surface level only when the loss escalates, subsequently contributing to affiliate fraud and draining advertising budget. Here’s what non-compliance includes –  Brand Bidding Violations Brands hold exclusive rights over their own keywords, and affiliate partners are strictly prohibited from bidding on them. Yet dishonest partners often violate this guideline by running paid ads on branded terms. This not only results into organic traffic hijacking but also increases bid price of brand’s own keywords.  Coupon Abuse Some partners abuse coupon abuse by misusing discount codes to capture commissions. For example, an affiliate leaks a private 20% discount code onto public coupon sites. A customer who was already ready to buy uses the code, and the affiliate still earns commission even though no new demand was created.  Unauthorized Creatives Brands run seasonal campaigns and offer exclusive discounts whose validity expires during the off-season time. However, some affiliates wrongly run old banners, offers, or messaging, tricking consumers for faster wins.  Misspelled Brand Names Partners perform typo squatting by registering misspelled or lookalike versions of a brand’s domain name and using them to divert users who accidentally type the wrong URL. These domains often host fake brand-like pages or silently redirect visitors to the official website through affiliate tracking links, making them earn wrongful commissions.  How Non-Compliance Hurts Both Brands and Honest Affiliates The impact of non-compliance in affiliate marketing programs is not confined to just brands, it extends beyond that, impacting honest affiliates, breaking the trust that binds them. Firstly, let’s understand the impact of non-compliance on brands –  Inflated CAC (Customer Acquisition Cost): Brands end up paying more to acquire each customer because commissions are being paid for sales that would have happened anyway.  Poor LTV (Customer Lifetime Value): Low-quality or incentive-driven users don’t stay loyal, make repeat purchases, or build long-term value, reducing the overall lifetime value of customers.  Wrong optimization decisions: Since the data is polluted by fraudulent activities, brands invest more in the wrong channels, partners, or campaigns.  Misleading ROI (Return on Investment): Performance looks strong on paper, but actual business impact is much lower.   Here’s how non-compliance in affiliate programs impact honest affiliates –  Loss of rightful commissions & unfair competition: Honest affiliates lose earnings, while rule-breakers take credit for sales they didn’t generate.  Distorted performance benchmarks & misleading targets: Fraudulent data skews performance metrics, making it harder to set fair goals and judge real success of affiliates.  Stricter compliance checks & increased operational burden: Due to dishonest affiliates, monitoring and audits become stricter, increasing workload and operational pressure on partners.  Erosion of trust, partner demotivation & limited growth: Loss of transparency weakens relationships, leading to lower motivation, higher churn, and slower ecosystem growth.  Why Your Current Affiliate Monitoring Is Not Sitting Right? If your current affiliate monitoring solution is indicating compliance issues after payout, it is not protection, it is simply reporting. Let’s know why it is not enough –  Limited real-time visibility – Insights arrive after campaigns run, not while they’re live.  Delayed issue detection – Problems are found late, reducing prevention opportunities.  Partial data coverage – Only a slice of activity is reviewed, leaving gaps.  Dependence on network checks – Independent validation is often missing.  Basic detection methods – Advanced abuse can go unnoticed.  Post-campaign optimization – Improvements happen after budgets are spent.  Reactive control model – Focus remains on reporting, not prevention.  The most trusted affiliate marketing programs are the ones that are not just backed by holistic compliance but also with KPIs that measure quality, not volume.   What they do right? Choose verified partners only – Work with partners ho have a proven track record and clean traffic sources.  Set clear rules & expectations – Define promotion guidelines, bidding policies, and compliance standards upfront.  Monitor traffic quality regularly – Track clicks, conversions, and behavior to ensure genuine user engagement.  Use transparent tracking & reporting – Maintain clear attribution and real-time performance visibility.  Reward quality, not just quantity – Incentivize affiliates for genuine conversions, not inflated volumes.  What they avoid? Don’t allow brand bidding violations – Prevent affiliates from competing on your branded keywords.  Don’t ignore suspicious traffic patterns – Sudden spikes, low engagement, or abnormal conversions are red flags.  Don’t rely only on surface metrics – High clicks and installs don’t always mean real users.  Don’t skip compliance audits – Regular checks are essential to prevent misuse and affiliate program violations.  Don’t delay action on fraud signals – The faster you act, the more revenue and brand trust you protect.  What Your Affiliate Compliance Monitoring System Should Have? The real and advanced affiliate monitoring solution provides a comprehensive approach to brands instead of making them shuffle between multiple tools. One such solution is mFilterIt’s Effcent that unifies compliance monitoring and empower brands to achieve –  AI-Powered Creative & Keyword Intelligence: Leverage NLP-driven systems to continuously scan digital platforms, uncover keyword misuse, misleading creatives, and content violations in real time.  Instant Alerts & Evidence-Based Reporting: Receive real-time alerts supported by screenshots, logs, and proof, allowing your teams to act quickly and decisively on typosquatting and counterfeit issues.  Consistent Brand Messaging: Prevent misuse of brand creatives, block lookalike domains and remove counterfeit or misleading product listings to maintain consistency in brand messaging.  Ensure Compliance & Controlled Reach: Run campaigns only in approved regions while eliminating expired, fake, or unauthorized promotions to maintain full brand and regulatory compliance.  Conclusion Affiliate programs function on one belief: trust. If trust shakes, metrics suffer and reliance on affiliate hamper. That’s why smart US brands invest in monitoring to put a defined halt to affiliate fraud. With the right affiliate monitoring software like mFilterIt’s Effcent, brands can surpass the checks and augment the outcomes of their affiliate programs.  FAQs Why is affiliate monitoring critical for protecting your brand? Affiliate monitoring helps ensure that partners follow brand guidelines, use approved creatives, and drive genuine traffic. It protects your brand reputation, prevents misuse, and ensures your marketing spend delivers real value.  What are the main risks of not monitoring affiliates? Without monitoring, brands risk brand bidding, fake or low-quality traffic, coupon abuse, misrepresentation, and rising customer acquisition costs — all of which lead to wasted budgets and loss of customer trust.  What are

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brand safety

Why Brands in MENA Need to Go Beyond Keyword Blocking Approach for Brand Safety in 2026?

Blog, Brand Safety & Suitability /

“If I block risky keywords and categories, my ads won’t appear next to unsafe content.” That’s the belief many brands operate on today and it’s a dangerous oversimplification. Keyword blocking was a good approach when internet was a simple place where URL based tracking was enough. Today, consumer associate brands with the kind of placement they are appearing. Therefore, the context and sentiment analysis of the content is perennial, where keyword blocking as a technique fails. The challenge has grown with the rise of AI slops, massive volumes of low-quality, auto-generated content created at scale. These pages often look legitimate, avoid obvious risky keywords, and slip past basic filters, increasing the risk of ads appearing next to misleading or low-quality content. When your ads appear in such environments, viewers often assume your brand is endorsing or even funding that content, directly impacting perception and trust. Hence, we have broken down how media brand safety measures need to evolve, why legacy tools no longer suffice, and how brand can stay safe without compromising reach and relevance. Why Keyword Blocking is not Effective Anymore in 2026? A word that a brand may label as “risky” can often appear in completely safe and relevant contexts such as news articles, educational videos, sports commentary, or everyday conversations. For instance, a keyword like “junk food” might appear in a nutrition awareness video or a healthy eating guide. If brands blindly block such keywords, they risk over-blocking, which can prevent their ads from appearing next to high-quality, brand-safe content.  On the flip side, genuinely unsafe or unsuitable content often avoids obvious trigger words. Instead, it relies on coded language, slang, abbreviations, or even visual cues. This leads to under-blocking, where harmful content slips through filters and ads appear in inappropriate environments.   In visual-first formats such as reels, thumbnails, and shorts, the lack of text led to frequent misclassification, allowing unsafe or irrelevant contexts to go undetected. Similarly, vernacular UGC with emotional or culturally sensitive undertones was often marked safe because legacy systems cannot interpret tone or sentiment in regional languages.   This flags major concerns especially in regions like MENA, where religious and cultural sensitivity strongly influence brand perception. Relying only on keyword blocking is not enough, because much of the content is vernacular. A video may seem neutral to an English-based system, but still carry political, emotional, or culturally sensitive undertones. As a result, such content often gets wrongly marked as safe, making contextual advertising more important there.  The Reality: Legacy Systems Don’t Understand Context Platform-built brand safety tools focus on what’s easiest to detect: keywords, metadata, and surface-level signals. What they miss is contextual intelligence: tone, intent, visuals, sentiment, and cultural relevance.  How Does an Advanced Brand Safety Approach Keep You a Step Ahead? Our campaign analysis revealed that 7–9% of YouTube impressions ran on Made-for-Kids content, wasting spend on non-converting audiences and weakening brand relevance. Ads were also found on Satta and gambling-related sites, where coded language and neutral-looking metadata slipped past platform filters. These findings underline a clear reality: the most significant brand safety risks lie beyond keywords, in context platforms fail to see.  You would not wish this for your brand, right?  To combat this, an advanced approach, combining AI, NLP, machine learning, enable advertisers to –  Understand content in local and regional contexts By looking beyond keywords to understand tone, sentiment, and cultural nuance in regional and vernacular content. This helps brands avoid placements that may seem safe on the surface but are misaligned with local sensitivities or brand values. Interpret visual and video-led environments In formats like reels, thumbnails, short videos, and OTT content, where text is limited, it analyses visual signals to assess whether the surrounding content is appropriate for a brand. Balance protection with reach By focusing on contextual ads rather than rigid word lists, it reduces unnecessary blocking of relevant inventory while still identifying genuinely unsafe environments. Apply brand safety consistently across channels The same contextual approach is used across YouTube, UGC platforms, OTT, mobile apps, and programmatic media, helping brands maintainconsistent standards regardless of where ads appear. Close gaps left by platform-level checks Using multi-signal, post-bid contextual analysis and continuously updated blacklists and whitelists, it addresses blind spots that keyword and category-based controls often miss—supporting more accurate media brand safety decisions in 2025. Conclusion As content becomes increasingly visual, contextual, and culturally nuanced, traditional brand safety measures can no longer keep up. Platform-level controls are often reactive and lack the intelligence to understand intent, sentiment, or environment. To safeguard reputation while maintaining reach, brands need solutions that adapt in real time, analyze context, and anticipate risks before they escalate. In today’s landscape, where trust is built on perception, updating brand safety strategies isn’t just prudent—it’s critical.  FAQs What are the key aspects of brand safety? Following are the key aspects of brand safety –  Safe and suitable content placement  Context and sentiment understanding  Cultural and regional sensitivity  Fraud, MFA, and AI slop detection  Transparency and advertiser control  Why is keyword blocking no longer effective? Because it lacks context and intent understanding. Keyword blocking often over-blocks safe content and misses unsafe content that uses coded language, slang, visuals, or regional terms, making it inaccurate in today’s complex digital environment.  What are AI slops and why are they a risk to brands? AI slops are large volumes of low-quality, auto-generated content created mainly to attract ad revenue. They often look legitimate but lack credibility and brand-safe intent, increasing the risk of ads appearing next to misleading, low-value, or unsafe content, which can damage brand trust and performance. 

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Brand Infringement

What is Brand Infringement? Its Types, & How to Keep Your Brand Protected in 2026

Blog, Brand Protection /

When customers search for your brand online, they expect to find you. But sometimes, what they find instead is a fake version. A fake website, a counterfeit product, or an offer you never approved. This is the alarming reality brands face today. A brand’s value lies in the unique identity and reputation it builds with customers over time. They create digital representations that are instantly recognizable and trusted. However, that very identity is increasingly being misused by others for unethical and fraudulent gain. Brand infringement isn’t just about copied logos or trademark infringements anymore. It is now more prominent across marketplaces, social media, and other platforms designed to closely mimic genuine brands. What makes this challenge even more complex is how it unfolds if left unchecked — directly impacting customer trust, revenue, and long-term brand equity. Hence, the need to understand what is brand infringement, its types (to be able to identify immediately), and how to keep your brand protected from such threats. Let’s dig in. What is Brand Infringement? Brand infringement refers to unauthorized use of any brand’s trademarked assets, like logo, name, ads, creatives, domain name, products, or any other branding elements. The only goal is to create confusion, harm brand identity, or sell counterfeit products or services. In simple terms, if someone uses your brand identity to mislead customers, divert traffic, or profit unfairly, it comes under the umbrella term of brand infringement. Moreover, due to the expansion of ecommerce marketplaces, complex paid media ecosystems, social commerce, and AI-generated content over the years, it has become a much bigger challenge in 2026. Brand infringement today spreads faster, looks more authentic, and causes damage long before brands can react manually. Common Types of Brand Infringement With digitalization, violators have developed multiple ways to deceive the audience. Below are the most common forms of brand infringement brands face today: Trademark Infringement It is one of the most common forms of infringement. Trademark infringement occurs when a third party uses a brand’s registered name, logo, slogan, visual identity or a combination of the same that a company uses to distinguish its products, solutions, or services from others. Example: An admin of a Facebook group using the name of a legit travel brand without their permission to earn bookings. Read more to know how to protect your brand from domain infringement. Brand Impersonation Brand impersonation is when fraudsters pose themselves as genuine brands using fake websites, emails, messages, or accounts to deceive customers into transacting money, sharing personal data, or sensitive information. Example: A fake customer website claiming to represent banks, airlines, or ecommerce brands to scam users. Counterfeit Fraud Another type of brand infringement, counterfeit fraud involves selling fake or duplicate products on various marketplaces under a brand’s name without authorization, often mimicking original packaging, design, and branding to appear genuine to customers. Example: Fraudsters listing and selling duplicate products of a luxury brand like Gucci, Prada, etc. on ecommerce marketplaces. Copyright Infringement It is another major form of brand infringement. Copyright infringement involves unauthorized use of the original expressions and ideas of another seller. Violators produce counterfeit products or other assets that are visually identical to assets of an existing brand, created with no knowledge of the original brand. Example: Websites or sellers copying a brand’s product descriptions, blogs, videos, or marketing creatives to appear legitimate or improve visibility without authorization. Typosquatting Typosquatting occurs when infringers register domain names (also known as domain squatting) that are slight misspellings or variations of a brand’s official website to mislead users and redirect them to fake websites, counterfeit products, or scam pages. Example: Fake websites with domain names amaz0n.com selling duplicate products under original brand name. Cybersquatting Cybersquatting involves registering or using domain names that include a brand’s trademark with the intent to profit from it, often by reselling the domain, running ads, or redirecting traffic for commercial gain. Paid Media & Search Infringement Paid media infringement happens when third parties misuse a brand’s name or trademark in online ads to divert traffic, inflate ad costs, or mislead users into visiting unauthorized or deceptive landing pages. Example: Affiliates bidding on brand keywords in search ads and redirecting users to competing websites or fake promotional pages. App Infringement App infringement is when fraudsters make fake or misleading mobile applications using a brand name, logo, or identity to trick users into downloading apps, sharing personal information, and making transactions. Example: Malicious apps claiming to offer rewards, cashback, or services under a well-known brand’s name. Social Media Infringement Social media infringement includes fake brand accounts, unauthorized influencer promotions, or misleading giveaways that misuse brand identity to gain followers, engagement, or financial benefits without brand’s approval. Example: Fake Instagram accounts running giveaways using brand logos and visuals to collect personal information from users. The various forms of brand infringement call for high awareness of a brand’s digital surroundings, strict vigilance, and proactive brand protection practices. Get your complete social media brand protection checklist. How to Prevent Brand Infringement? In 2026, brand protection is no longer about reacting to individual incidents; it requires a structured, proactive, and continuous approach. Secure Your Brand Foundations The first step to prevention is ownership and clarity. Brands must ensure their trademarks are registered across key markets and categories, especially where they actively operate or plan to expand. Alongside trademarks, owning critical domain variations and safeguarding brand assets such as logos, creatives, and messaging helps reduce opportunities for misuse at the source. Without strong foundational control, enforcement becomes difficult and inconsistent. Maintain Control Over Your Digital Presence Brands today operate across marketplaces, apps, search engines, and social platforms. Enrolling in marketplace brand protection programs, verifying official social media accounts, and maintaining clear ownership of apps, landing pages, and customer touchpoints ensures customers can easily distinguish between genuine and fake brand interactions. This visibility also makes it easier to identify misuse early. Educate Internal Teams and External Partners Brand protection is a shared responsibility. Marketing teams, ecommerce managers, affiliates, agencies, and resellers

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ad fraud

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

Ad Fraud, Blog /

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

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brand safety

What is Brand Safety? The Role of Brand Safety in Digital Advertising

Blog, Brand Safety & Suitability /

Imagine this: your audience associates your brand with inappropriate content that you never had the intention of funding. Recently, advertisers raised concerns when their ads on Spotify were found appearing alongside sexually explicit audio content. The issue wasn’t the platform but the association. Source: Storyboard18 These brands never intended to be associated with such content. The ads were placed through automated digital advertising systems designed to maximize reach and efficiency. Yet, the brands got associated. This is exactly what happens to brands in the digital advertising ecosystem. Ads today travel across thousands of websites, videos, and platforms through automated systems. While brands carefully plan their messaging and targeting, they don’t always have visibility into where their ads finally appear. When an ad shows up next to misleading, controversial, or inappropriate content, the brand gets associated with it, regardless of intent or awareness. Sometimes, one wrong association is enough to damage the brand reputation or trigger backlash. This is where the role of brand safety in digital advertising becomes prominent. It helps brands maintain control over where their ads appear and ensures marketing efforts build trust instead of unknowingly damaging it. That’s why understanding what is brand safety, why it matters, and who needs to care about it is the first step towards ensuring safe advertising. What is Brand Safety? In digital advertising, brand safety refers to ensuring your brand ads do not appear next to irrelevant, inappropriate, illegal, or unsafe content that might harm your brand’s reputation, credibility, and brand values. It includes measures taken to ensure safe ad placements across social media platforms, apps, and websites. For example, when a reputed brand’s ad appears on a gambling or lottery results website, it creates a risky association, even if the ad itself is legitimate. Such placements can mislead users, violate brand safety norms, and damage trust, making brand safety a critical concern for advertisers. However, brand safety is ambiguous as the approach or definition of safety may vary from brand to brand and also from product to product that is being advertised. Thus, the approach taken by different brands, advertisers, or publishers also depends on two other related concepts: Brand suitability Brand suitability focuses on whether content aligns with a brand’s tone, values, and risk tolerance. Content may be safe, but still not appropriate for every brand. Brand relevancy Brand relevancy ensures ads appear in environments that make sense for the audience’s mindset, context, and intent. Therefore, brand safety prevents ads from appearing next to harmful content. Brand relevancy and brand suitability help you ensure your ads appear next to not only safe but also contextually and sentimentally relevant content. Why is Brand Safety Important in 2026? The audience interacts with brands through ads and associates them with the content alongside. However, with digital advertising controls shifting from manual placements to algorithm-driven distribution, content is no longer static. User-generated videos, regional language content, short-form media, live streams, and AI-assisted content now dominate digital platforms. Furthermore, in the era of AI, content is created, amplified, and modified quickly. This also means misinformation and disinformation also spread faster than ever, making it harder for brands to distinguish credible environments from misleading or manipulated ones. Consumers don’t separate ads from the content around them. That is why, when an ad appears next to questionable or misleading content, the negative association sticks, often subconsciously. What are the Risks of Brand Safety Violation? The impact is not always immediate or dramatic. More often, brand safety failures lead to: Loss of trust as users start doubting the brand Wrong brand perception due to controversial or risky surrounding content Lower engagement because users ignore ads in unsafe environment Poor brand recall because the brand is remembered negatively or not at all Weaker campaign results such as lower clicks and conversions Wasted ad spend on impressions or views that bring no real value Higher compliance risk, especially for regulated industries Long-term damage to brand value, which is hard and costly to fix Brand safety issues can cause instant backlash, weaken brand reputation, credibility, and create damage that is harder to reverse easily. Who Should Care About Brand Safety and Related Issues? Brand safety is a shared responsibility. Ensuring safe ad placements is not just a one-person job. It affects everyone involved in the digital advertising ecosystem. Therefore, everyone, including advertisers, publishers, agencies, and ad platforms, plays a distinct role in keeping advertising environments safe, credible, and effective. Advertisers: Protecting Brand Trust and Long-Term Value Advertisers face the most visible and immediate risk when brand safety filters fail. When an ad appears next to inappropriate, misleading, or unsafe content, consumers don’t blame the platform or the algorithm; they associate the experience with the brand. For example, if a baby product ad shows up next to a terrorist attack video will instantly feel out of place. If advertisers don’t actively monitor such placements, the impact goes beyond reputation. Media budgets get wasted on low-quality environments; engagement drops, and performance metrics become misleading. Over time, this erodes brand equity that took years to build. Therefore, for advertisers, brand safety is not just about avoiding embarrassment; it’s about protecting trust, credibility, and ROI. Publishers: Maintaining Credibility and Revenue Potential Publishers depend heavily on advertiser confidence. When a website, app, or channel becomes known for hosting unsafe, misleading, or low-quality content, advertisers start pulling back, even if the publisher has strong reach or traffic. For instance, publishers running sensational or unverified content may still attract impressions, but premium advertisers often avoid such environments. This leads to lower CPMs, reduced demand, and long-term monetization challenges. If publishers don’t prioritize brand safety, they risk being labelled as unsafe or low-quality sites. Once that perception sets in, it becomes difficult to attract high-value advertisers again. Therefore, for publishers, brand safety is directly linked to credibility, sustainability, and long-term revenue growth. Agencies: Preserving Client Trust and Strategic Value Agencies are responsible for planning, buying, and optimizing digital campaigns for various brands. Clients trust agencies not only

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What is Click to Install Time? Why Advertisers Need to Map this to Detect Mobile Ad Fraud?

Blog, Mobile Ad Fraud /

Bots are becoming sophisticated and more human-like every passing day. And with the emergence of AI, it is becoming a dominant force for shaping online traffic.   According to Imperva BadBot Report 2025, 51% of the internet traffic is driven by bots, which is further amplifying with the introduction of AI and LLM. Unlike the basic bot traffic showing abnormal signs like high number of clicks/installs etc., the sophisticated bots can mimic human behaviour, therefore bypassing the validation checks.    As a precautionary measure and to check if your campaigns are impacted by bots/invalid traffic, there are signs that you can look for in your campaign data.   One of them being Click to Install Time to identify invalid installs in your mobile app campaigns.  In this blog we will breakdown how CTIT can be seen as a signal to identify invalid traffic and how marketers can use it to take proactive action against mobile ad fraud.   What is Click to Install Time? How to Identify Invalid Traffic Evaluating CTIT?  Let’s simply breakdown what CTIT means before moving forward to understand the kind of patterns that reveal exploitation of mobile ad fraud and click to install time. Click Time: The moment a user clicks on your ad.  Install Time: When the app actually finishes installing.  Click-to-Install Time (CTIT): The time gap between these two.  It is basically a metric used in mobile advertising to map the time it takes for a normal user to download an app after clicking on an ad.   This gap varies naturally. Real users don’t install apps instantly every time; there can be delays, pauses, network differences, and human behaviour involved. What a genuine user’s install journey looks like  This process takes time, usually a few seconds to a few minutes, depending on network speed and app size.  However, fake installs show different timing patterns.   Here’s are the two types of abnormal CTIT patterns we observed recently that clearly indicate towards install fraud:  Examples of Abnormal CTIT Patterns   Case 1: Extremely short click-to-install time (click injection)  This snapshot compares the click time and install time for multiple installs coming from the same publisher. The gap between click and install is consistently just 1–3 seconds, and in several cases, the values are identical or nearly identical.  Why it is a problem? A real user cannot click an ad, get redirected to the Play Store, download the app, complete the installation, all within a few seconds, repeatedly.   This pattern strongly indicates click injection, where fraudsters:  Detect that an app install is already in progress  Inject a click at the last possible moment  Steal attribution credit for a genuine install  mFilterIt insight: Why this matters? Although these installs appear valid in attribution reports, the timing exposes manipulation. Extremely short and repeated click-to-install times are a strong indicator of high-risk fake attribution, not real user engagement.  Learn more about common techniques of install fraud here. Case 2: Google Play install begins before the user clicked on an ad  In this snapshot, the timestamps reveal something even more concerning. The Google Play install begin time occurs before the recorded ad click time. This results in a negative click-to-install time, meaning the install process started before the user supposedly clicked on the ad.  Why is it a problem? This breaks the basic logic of attribution. A real user cannot start installing an app first and then click an ad for the same app afterward. When install activity precedes the click, it clearly indicates:  Manipulated or falsified timestamps  SDK tampering or fabricated attribution signals  This is not caused by reporting delays or tracking errors; it points to deliberate attribution manipulation.  mFilterIt insight: Why this matters? Any case where the install begins before the ad click should be treated as install fraud by default. These patterns strongly indicate fake attribution attempts, even if the installs are being credited by attribution platforms.  Signs to Identify Abnormal CTIT Patterns CTIT mapping should be approached in two layers: what you can validate manually and what requires advanced detection at scale.  As an advertiser, the following click to install time red flags should immediately raise concern, especially when they appear repeatedly.  Installs within 1–3 seconds of a click  Real users need time to reach the app store, download the app, and complete installation. Consistently instant installs are not normal human behaviour patterns.  Identical CTIT values across multiple installs Human actions vary. When multiple installs show the same or near-identical timing, it often points to automated or scripted activity.  Long delays followed by sudden attribution This pattern is commonly associated with click spamming, where random clicks are generated and later receive credit when an install happens.  Negative CTIT values If an install begins before the recorded ad click, it breaks basic attribution logic and strongly indicates manipulated timestamps or fake signals.  How Advanced Mobile Ad Fraud Detection Solutions Help Mobile ad fraud is often distributed across campaigns, publishers, and devices, making it difficult to detect without advanced analysis.  Attribution platforms answer one primary question: Who gets credit for the install? They do not answer whether the install journey itself was genuine or a fake one. While they work on assigning credits rather than behavioural validation, brands need an advanced mobile ad fraud detection solution to ensure campaign efficiency. Here’s how it helps:  Source-Level CTIT Pattern Analysis – Know who is installing your app Advanced solutions analyze click-to-install time across all campaigns and channels simultaneously. This makes it easier to spot publishers or sources that consistently show unnaturally fast or uniform CTIT patterns. It also helps identify install fraud patterns that may look normal in isolation but become obvious when viewed across the entire account.  Analysis of CTIT with Click Behaviour – Don’t let sophisticated bots slip by Click-to-install time is evaluated alongside click signals such as click frequency, burst patterns, and timing alignment. This helps distinguish genuine user clicks from injected or spammed ones.  Correlation with Device and Environment Signals – Differentiate between bot & human Advanced

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Ad Fraud in 2026

Ad Fraud Explained: Types, Impact, and How Advertisers Can Fight Back

Blog, Ad Fraud /

“Half the money I spend on advertising is wasted; the trouble is, I don’t know which half.” This line has followed marketers for decades, and in today’s digital-first world, it feels more relevant than ever.  Ad fraud in 2026 is emerging as a global problem.  With global digital marketing spends hitting USD 21.2 billion in 2024 and projected to grow to USD 51.1 billion by 2034, expanding at a 9.2% CAGR, brands are investing heavily across platforms, formats, and audiences, betting on data-driven precision to deliver results.  But as digital advertising scales, so does its biggest hidden challenge: digital ad fraud. Brands still see ad fraud as a linear problem, neglecting the roots till which it has extended its feet.  This blog is going to highlight –  The common types of ad fraud impacting each funnel  Impacts of ad fraud on brand campaigns  Measures brands can take against ad fraud  What are the Common Types of Ad Fraud? Digital ad fraud is no longer a linear problem, it has extended its reach across the marketing funnel, impacting performance at each level. Let us understand the types of ad fraud based on each funnel.  Stage 1 – Impression Level Fraud Viewability of your ads does not define whether your ads are viewed by the right audience. Fraud is happening at that level as well including,  Ad Stacking In ad stacking, multiple ads are placed on top of each other in the same ad slot. Only the top ad is seen, but advertisers are charged for all of them.  Pixel Stuffing In pixel stuffing, ads are squeezed into extremely small spaces that users cannot notice, yet impressions are still counted and billed.  Frequency Cap Violation Fraudsters show ads to the same user far more times than the set frequency limit. It often happens due to bot activity, cookie manipulation, or device spoofing, causing ads to be repeatedly served to non-genuine users. As a result, budgets are drained, reach is distorted, and real users may see fewer ads than intended.  Domain spoofing Fraudsters disguise low-quality websites as premium publishers to sell cheap inventory at higher prices.  Made-for-Advertising (MFA) sites These websites are built to only generate ad revenue, with thin content and little to no real user engagement.  Stage 2 – Click Fraud Once your ad is viewed, it is important to know who have clicked on your ad. When you believe your campaign is getting all the right clicks, here’s a trap that fraudsters have laid, baiting you to believe that your campaigns are performing well in the metrics, whereas conversions fail. Types of click fraud include –  Click Farms Fraudsters hire low-paid workers or coordinated setups that manually generate fake clicks, installs, or engagements on ads to make campaigns appear more successful, even though there is no real user interest or intent.  Organic Hijacking Fraudsters take credit for genuine user actions like app installs or conversions that would have happened naturally, making it look like their traffic drove the result and stealing attribution from the real source.  Click Spamming Fraudsters generate a large number of fake or low-quality clicks across multiple ads in the hope that one of those clicks gets credit for a conversion. These clicks usually come from bots or automated scripts and inflate click metrics without showing real user intent.  Click Injection Fraudster sends a fake click at the exact moment a user is about to convert (such as installing an app). This tricks attribution systems into crediting the fraudster for a conversion that would have happened anyway.  Stage 3 – Event Fraud While you may not notice, fraud is happening even at the stage of soft KPIs (installs, signups, etc.) where low-quality users are draining your ad budgets. The kinds of fraud happening at the event level include –  Incent Fraud Incent fraud happens when fraudsters run incentive campaigns to attract users by offering points, cash, discounts, or in-app currency for completing actions like clicking an ad, installing an app, or signing up.   Coupon/Referral Fraud Here, fraudsters misuse discount codes or referral programs to gain benefits they are not entitled to. They may create multiple fake accounts, use bots, or exploit loopholes to repeatedly apply coupons or generate false referrals, leading to revenue loss and skewed performance metrics.  Lead Punching Lead punching happens when fraudsters submit fake or low-quality leads into a system—often using bots or fake forms—to claim credit or commissions, even though these leads have no real potential to convert.  Retargeting Fraud Retargeting fraud occurs when fake users or bots are made to appear as interested visitors so ads can be repeatedly shown to them. Since these “users” are not real potential customers, retargeting budgets get wasted on impressions and clicks that have no chance of converting.  What is the Impact of Ad Fraud on the Campaign Budget of Advertisers? Ad fraud affects not just spend, but also how campaigns are measured, optimised, and scaled. The following are the impacts of ad fraud –  Loss of Media Spend to Invalid Activity: Budgets are spent on clicks and impressions generated by bots, click farms, or MFA sites that never lead to real users or conversions.  Reduced Efficiency of Campaigns: When invalid traffic consumes impressions and clicks, genuine users see fewer ads, lowering reach, conversion rates, and overall return on ad spend.  Misleading Performance Signals: Inflated metrics such as CTR, installs, or engagement make low-quality inventory look effective, leading to repeated investment in the wrong channels.  Brand Safety and Trust Impact: Fraudulent traffic often originates from deceptive or low-quality environments, increasing the risk of ads appearing alongside misleading or unsafe content.  Rising Acquisition Costs: Artificial demand created by fraud drives up CPMs and CPCs, forcing advertisers to pay more to reach legitimate audiences.  How can advertisers solve ad fraud? To fight ad fraud, advertisers must see Ad Fraud Beyond the Linear Lens. For this, the right ad fraud detection tool like mFilterIt’s Valid8 is required which will not only track your ad performance funnel-wise but also ensure all your channels (app and web) are covered to make it your one-point destination for all the traffic validation activities. Below are the key areas this approach covers:  Validate Impression Quality at the Source: Continuously monitor placements, domains, and apps to detect impression-level fraud such as ad stacking, pixel stuffing, MFA sites, and domain spoofing, ensuring ads are served in viewable, brand-safe, and genuine environments.  Stop Invalid and Manipulated Click Activity: Identify and block click fraud tactics like click spamming and click injection by analyzing click frequency, timing and source anomalies before

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install fraud

What is Install Fraud? How to Solve Install Fraud?

Blog, Mobile Ad Fraud /

Advertising platforms optimize for signals—not intent.   In mobile marketing, the most important signal is the install. More installs usually mean a campaign is working. Platforms see this, assume success, and push more budget in the same direction.   This is where install fraud begins.   Fake installs are easier and cheaper to generate than real users. Fraudsters use bots, device farms, or incentivized tactics to create large volumes of installs that look genuine on the surface. Since the numbers look good, platforms assume the campaign is performing well. Budgets increase. The same sources get more spend.   But the users aren’t real.   At first, nothing feels wrong. Cost per install may even go down. Install numbers keep growing. The problem only becomes visible later, when users don’t open the app, don’t register, and don’treturn. What looked like growth turns into wasted spend.   That’s why fake app installs are so hard to catch early. It doesn’t break campaigns overnight. It quietly trains platforms to invest in fake activity while genuine users get pushed out.   In this blog, we’ll explain what install fraud is, the common ways it happens, and how marketers can spot and prevent it—before it starts impacting real growth.  What is Install Fraud in Mobile Advertising?  Install fraud occurs when fake app installs are generated or manipulated to claim attribution and payouts, without real user intent.  In simple terms, a fake install appears as a genuine app download on your dashboard but doesn’t come from a genuine user who intends to engage with your app. These installs may be created by bots, emulators, manipulated devices, or deceptive techniques designed to game attribution systems.  Install fraud falls under the broader category of mobile ad fraud, and it primarily targets CPI-driven campaigns. Since advertisers pay for installs, fraudsters focus on triggering that one event, regardless of what happens afterwards.  What makes this problem more complex is that modern mobile ad fraud techniques don’t just stop at installs. When install traffic isn’t verified, the same fraudulent activity extends to post-install events as well, such as sign-ups, in-app actions, or other action-driven KPIs. These events may look legitimate in reports, but they’re often designed to reinforce false performance signals.  The result? You end up paying for volume, but you don’t get real value in return, leading to weaker optimization signals and campaign inefficiency.  Common Types of Install Fraud Techniques You Need to Know About  Fraudsters use various techniques to generate fake installs and manipulate last-click attribution. These techniques closely mimic real user activity, making it impossible for basic tools to identifymobile ad fraud. Here are the most common install fraud techniques performance marketers should be familiar with:  Click Injection Click injection happens when a fraudulent source identifies that an install is about to take place. A click is fired right at that moment (by exploiting the narrow attribution window) to steal the last click attribution from the channel that actually drove the install. This is also known as organic poaching or install hijacking.  Click Spamming Click spamming is when a large volume of fake ad clicks are sent and injected into devices in advance. This increases the chance that one of those clicks gets credited whenever an organic install eventually takes place, stealing the attribution as a result.  SDK Spoofing SDK spoofing fakes app installs by imitating devices and app signals through emulators or scripts, making it appear as a real user installed on the app, without any actual download taking place.Fraudsters generate installs only to exhaust advertising budgets and spoof installs.  Fake App Versions Fraudsters use altered or cloned versions of the app that appear legitimate but generate fake installs and in-app events. These versions mimic normal activity and deceive attribution systems into counting non-genuine traffic.  Know what unusual app version patterns look like and how they reveal bot traffic.  What makes all these techniques dangerous is not just how they work but also how normal they appear to human eyes in standard reports.  How Does Install Fraud Impact Mobile Advertising Performance? Install fraud operates silently. It passes basic attribution checks, mimics normal install behaviour, and avoids sudden spikes that might raise alarms. This happens because installs are counted before user quality is proven.   The moment an install is attributed, it’s treated as success, long before anyone knows whether that user will engage, return, or convert. Therefore, the business impact begins to fall. It doesn’tjust affect one metric or one campaign. It spreads across attribution, the entire funnel, optimization, teams, and long-term strategy. Here’s how:  Confusing performance metrics Fake installs inflate metrics like CPI and install volume, masking real weaknesses in retention, engagement, and long-term value.  Misleading attribution signals & optimization decisions Mobile ad fraud techniques steal credit from genuine channels, making fraudulent partners look better than they truly are, leading teams to invest where value isn’t being created.  Lower audience quality Fake installs never engage meaningfully post-install. When these users enter retargeting lists or lookalike pools, overall audience quality drops.  Budget misallocation Because dashboards don’t always show red flags early, money keeps flowing toward channels that appear efficient but deliver little real return.  Cross-team impact Product, growth, and analytics teams end up working with skewed signals, affecting feature prioritization, engagement strategy, and user journey decisions.  Unreliable forecasting and planning Cohort trends, lifetime value projections, and performance forecasts become unreliable when they’re built on compromised data.  Skewed event-level analysis Low-intent users trigger surface-level actions, making it appear as though users are progressing through the funnel. This skews event-level analysis, weakens action-driven KPIs, and makes it harder to identify where genuine drop-offs are actually happening.  But my attribution platforms flag mobile ad fraud? Most attribution platforms are designed to assign credit, not to validate whether an install or event came from real user intent. As a result, sophisticated mobile ad fraud techniques manipulate attribution logic, steal credit from genuine channels, and reinforce false performance signals. This often leads teams to trust reports that look complete, but miss the underlying quality and

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Click fraud

AI vs AI: How AI powered tech can help to detect advanced click fraud?

Blog, Ad Fraud /

AI is no longer just accelerating digital advertising; it’s powering a new generation of bot-driven fraud.  As AI adoption surges, growing from USD 8.6 billion market in 2023 to a projected USD 81.6 billion by 2033, it has unlocked unprecedented speed and scale. But this same power is now being exploited to generate massive volumes of intelligent bot traffic that looks, behaves, and performs like real users.  These AI-driven bots don’t raise obvious red flags. They blend into campaigns. AI has made click fraud faster, smarter, and harder to detect. And the only way to fight it is with AI itself.   This blog will uncover –  How AI becomes a fraud enabler?  What are the impacts of AI-driven fraud on ad performance  Signs to Identify Advanced AI-Driven Click Fraud  How AI shields brands against advanced fraud tactics   How AI Becomes a Fraud Enabler   Earlier, fraudulent activity was easier to spot repetitive patterns, obvious spikes, or low-quality traffic that clearly looked non-human. Today, AI has changed the game click fraud has evolved with sophisticated tactics like click spamming and click injection. Fraudsters now use intelligent bots that analyse campaign behaviour, mimic real user journeys, and continuously adapt to evade detection. The result is an illusion of performance.  The most damaging outcomes of AI-driven click fraud include:  Bot-Driven Automated Clicks AI-powered bots now simulate real human browsing behavior, mimicking scrolling, dwell time, and natural click patterns to quietly manipulate engagement metrics and drain ad budgets without raising suspicion.  Emulator and Device Farm Traffic Fraudsters deploy emulators and device farms, using AI to manage thousands of virtual devices that generate fake clicks, installs, and events. To ad platforms, this traffic looks legitimate, diverse devices, consistent behavior, and clean signals.  Ad Stacking and Hidden Ads AI also enables ad stacking and hidden ad techniques, where multiple ads are layered or concealed behind visible elements. Impressions and clicks are generated without any real user intent  Geo and IP Rotation To further evade detection, AI-driven systems continuously rotate IP addresses, geographies, and device identities, making fraudulent traffic appear like it’scoming from genuine users across multiple regions.  Know how click fraud impacts performance campaigns in walled gardens What is the Impact of AI–Driven Bots on Ad Performance? As the evolution of AI is expanding its feet across the digital ecosystem, its real-world impacts on ad performance are clearly visible. Here’s how they function –  Because these bots adapt to platform rules, they often bypass basic fraud checks and continue running undetected.  By copying real user behavior, bot clicks look genuine, making fraud hard to spot.  Fake clicks and engagement corrupt performance data, so reports no longer reflect reality.  This misleads bidding, targeting, and optimization algorithms, pushing spend toward fraudulent traffic.  Over time, ROI, attribution, and conversion metrics get distorted, hiding real performance issues.  Worst of all, this activity can look clean in dashboards, while quietly eroding returns across paid media campaigns.  AI as the Defense Layer: Role of AI Against Click Fraud  AI-driven fraud prevention systems track unusual user behavior and uses past data to predict fraud, helping advertisers stay ahead of scammers causing click injection. Here’s how AI-powered ad fraud detection solution like Valid8 empower brands against click fraud –  Detecting Click Repetition and Abnormal Behaviour Patterns AI keeps an eye on clicks across devices, IP addresses, and sessions to spot unusual patterns—like repeated clicks, sudden spikes, or traffic coming from suspicious IPs, proxies, or VPNs. By identifying these signs of bot activity in real time, AI can block fraudulent clicks before they waste your budget or give you misleading performance data. Filtering Invalid Devices Through User-Agent Analysis Fraudulent traffic often reveals itself through abnormal or manipulated user-agent strings. AI examines device, OS, and browser combinations to detect inconsistencies that don’t align with real-world usage patterns. Invalid or spoofed devices are flagged before their clicks are counted as genuine engagement. Know what to look for in a click fraud detection tool Validating Geographic Authenticity Through IP Intelligence AI verifies whether traffic is coming from applicable and relevant geographies. Mismatches between campaign targeting, and user behaviour often indicate fraud. By performing geo-validation in real time, AI ensures only legitimate, location-relevant clicks influence campaign performance metrics. Detecting MFA Sites Using Impression/Click-Level Intelligence Made-for-Advertising (MFA) sites are designed to generate ad revenue rather than real engagement. AI analyses impression and click level data coming from low-quality users of these sites, captured via tracking pixels and runs it through fraud detection algorithms and blacklists. Once identified, these MFA sources are automatically blocked within ad managers, preventing further spend leakage in real time. Enabling True Source-Level Transparency AI-driven defence systems provide granular visibility into traffic sources, revealing exactly where clicks originate. This source-level transparency helps marketers distinguish high-quality inventory from fraudulent or low-value placements, allowing smarter optimisation decisions and greater control over media spend. Conclusion The real challenge for marketers isn’t whether to adopt AI, it’s how to use it responsibly and defensively. As AI-driven bots become increasingly human-like and adaptive, traditional fraud controls are no longer enough. Invalid traffic blends seamlessly into campaigns, and performance metrics alone can no longer be taken at face value.  The solution is clear: AI must fight AI. With mFilterIt’s AI/ML powered click fraud prevention tool, Valid8, brands can protect their campaigns, safeguard budgets, and boost ROI without compromising trust or data quality. 

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