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Sharing insights, strategies, and trends to help businesses grow in the digital world. From marketing and SEO tips to brand protection and analytics, this blog is your go-to space for practical business knowledge LinkedIn: https://www.linkedin.com/in/tannusainii/

[businessblogss]

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

Are you proactively analyzing the ad traffic of your campaigns?  

Is it really coming from genuine users or just being generated by bots? 

Yes, a significant portion of traffic that makes your ad campaigns seem successful could be invalid traffic. According to mFilterIt’s analysis featured in FICCI Report 2025, invalid traffic contributes to as much as 30–50% of activity across digital channels, directly distorting performance metrics and draining ad budgets. 

This means the performance you see on dashboards may not always reflect real user intent. Instead, it could be influenced by automated systems, proxies, or manipulated interactions that inflate impressions, clicks, and even conversions. 

In this blog, we break down what invalid traffic really is, why it’s increasing, differences between general invalid traffic and sophisticated invalid traffic, and how you can identify and mitigate its impact to ensure your campaigns deliver genuine results. 

What is Invalid Traffic? Why is it Increasing Rapidly?

Invalid traffic simply means ad activity that doesn’t come from real users but still shows up as genuine impressions, clicks, or visits. This happens when bots or automated systems interact with ads, making it look like people are engaging when they actually aren’t. 

Over time, this traffic has become more advanced and harder to spot. Bots now easily mimic real user behaviour, such as browsing pages, scrolling, or clicking on ads.  Moreover, developments in technology, AI usage, and advertising infrastructure also contribute to this. Here’s how: 

AI is making bots smarter

Earlier, bots were easy to detect because they behaved like machines. Today, AI-powered bots can scroll, pause, click, and even mimic browsing patterns. Some can simulate entire user journeys, making fake engagement look real in analytics tools. 

The ad ecosystem has become more complex

Modern advertising runs through multiple layers and channels, DSPs, SSPs, ad exchanges, networks, and resellers. This fragmentation creates blind spots, making it easier for low-quality or fraudulent traffic to enter without being noticed. 

Cheap infrastructure fuels large-scale ad fraud

Server farms allow fraudsters to generate massive volumes of ad traffic at very low cost. What once required physical devices can now be scaled instantly using virtual environments. 

Limited transparency and visibility

Limited transparency and visibility across the digital ecosystem make it harder for advertisers to verify traffic quality. With restricted access to detailed user-level data, identifying whether engagement is coming from real users or sophisticated invalid traffic becomes more challenging. 

As long as advertisers pay based on clicks or impressions, there’s always a chance for misuse. Fraudsters take advantage of this by generating invalid clicks or views to earn money, especially when proper checks are not in place. 

Therefore, detecting invalid traffic has become more important than ever. Invalid traffic is generally classified into two main types: General Invalid Traffic (GIVT) and Sophisticated Invalid Traffic (SIVT). 

Two Major Types of Invalid Traffic

Invalid traffic is broadly classified into two categories based on how complex the fraud technique is.  

What is General Invalid Traffic (GIVT)?

It refers to non‑human or automated interactions that inflate ad metrics, caused by easily identifiable bots, spiders, or crawlers. These bots typically do not attempt to mimic real human behavior. They’re not malicious in intent but can distort campaign reporting and waste ad spend due to their automated nature. 

Because the patterns are predictable, platforms and verification tools can often identify and block this traffic using ad fraud detection techniques.  Here’s what we observed in one of the campaigns .

VPN and proxy traffic contributed 12.4% of total activity, indicating that a significant portion of traffic was not genuinely coming from real users. 

Several visits appeared to come from genuine mobile users but traced back to VPN and proxy networks, that were being used to hide the real user’s location. A deeper analysis showed that these IP addresses were linked to data center hosting providers (DCH) instead of real user networks.  

Explore The Complete Guide Here

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

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. 

Click Here To Read Complete Guide

[businessblogss]

How To Navigate Brand Safety in 2026? Key Considerations To Implement

Did you know? 

15% of ad spend gets wasted on unsafe inventory. 

Brands lose 30% ROI because of brand safety incidents. 

75% of consumers avoid brands with unsafe ads.  

Brand recall reduces by 25% on unsafe placements. 

That’s the cost of brand safety risks when not taken care of on time. 

Brand safety has become a growing concern for digital advertisers and marketers in 2026. Synthetic, AI-generated content, deepfakes, misinformation, disinformation, and wrong messaging are being spread across large volumes all over the internet. 

As online fraudsters become more creative and innovative with the techniques they use to commit fraud, and as AI reshapes what “content” even means online, advertisers are also becoming more cautious. If you are one such advertiser, this article is for you. 

In the upcoming sections, we will discuss how advertisers and brands can prepare themselves to face and triumph over the ever-evolving brand safety issues in 2026, including the biggest structural shift the industry has seen in years. 

Without wasting any time, let’s jump right in. 

Why is Brand Safety Essential for Brands in 2026?

Brand safety is important to every single stakeholder involved in the advertising supply chain. Starting from the ad networks to the publishers, it is important to do their part in maintaining brand safety to ensure that their customers continue trusting them. 

It helps ensure your brand ads are always aligned with the values your brand carries and does not show up against  

Paying attention to brand safety and protection isn’t just important from the point of view of preventing a negative impact on brand reputation. There are many positive benefits to actively pursuing brand safety and monitoring.  

For a Positive Brand Image

Brand safety threats take many forms, from ads appearing on websites hosting extremists or questionable content to placements alongside sites engaging in fraudulent activity. Either can seriously damage a brand’s reputation. 

While advertisers often have little control and transparency over exact ad placements, consumers don’t see it that way. They can easily assume a brand endorses the views or activities on the site its ad appears next to. This isn’t a hypothetical risk.  

The world’s biggest brands have faced this exact issue and used their scale to push some accountability onto publishers like Google. Smaller brands don’t have that leverage, making proactive brand safety monitoring essential rather than optional. 

To Make a Strong First Impression

Established brands have history and loyal audiences to fall back on, which helps them recover faster from a brand safety incident. 

New and emerging brands don’t have that cushion. A single incident can be far more damaging early on, and even in less severe cases, it often drives up advertising costs, since rebuilding a reputation from scratch usually demands a bigger-than-usual ad spend. 

The Post-GARM Reality: Why the Old Brand Safety Methods No Longer Work

For years, brand safety conversations leaned heavily on one shared reference point: the Global Alliance for Responsible Media (GARM), a cross-industry body that gave advertisers a common taxonomy for “unsafe” content. 

That one-size-fits-all approach doesn’t hold up in 2026. Content moves faster; formats have multiplied, and risk looks different for every brand. A fixed, generic keyword list cannot keep pace. 

What this means practically for advertisers: there is no more shared checklist to lean on.  

Many teams have shifted to a “dirty dozen” list of content categories as a baseline, then customized it to their own risk tolerance, which is exactly the approach outlined in the “Key Considerations” section below.  

Static keyword blocklists, long treated as a safety net, have also proven unreliable and are increasingly seen as a legacy tool rather than a real safeguard. They tend to over-block legitimate publishers while still missing genuinely unsafe placements. 

The net effect is that brand safety in 2026 depends far more on real-time, contextual, AI-driven analysis than on any fixed industry list, which is precisely the gap a dedicated brand safety solution is built to close. 

The New Frontier: AI-Generated Content and Ad Adjacency

If misinformation and bot traffic defined the brand safety conversation in 2023, AI-generated content adjacency defines it in 2026. This is a genuinely new category of risk, not a rebrand of an old one: 

Over one in five videos recommended by platform algorithms are now estimated to be AI-generated, often low-quality “slop,” per analysis of social platform data. 

However, the real risk isn’t AI content itself. Its low-quality, unlabeled AI content sitting next to a brand ad without any way to tell the two apart.  

It is also because of this that disclosure is now emerging as an important theme within the industry. The IAB’s framework for AI Transparency & Disclosure, published in early 2026, advocates that advertisers use machine-readable metadata to disclose the use of AI in advertisements, and most members of Gen-Z and millennial generations believe that transparent disclosure of the use of AI would positively influence their purchasing intentions. 

Explore The Complete Guide Here

[businessblogss]

Top 3 Cost Benefits of Using Ad Fraud Solution in BFSI Sector

Among a plethora of challenges faced by a brand and a marketer, ad fraud makes it to the top of the battle list. The battle is real where bots are eating up the digital ad budgets and burning a hole in the effectiveness of ad spending. What’s the outcome? 

The advertising has no effect on your business and money spent on ads goes down the drain or the bots which means that brands and marketers pay the fraudsters one out of every four times when an ad has been clicked. Not really the most effective way to spend money on advertising, right? So where and how does an ad fraud solution help your business? 

In the BFSI sector, the number of transactions and requests that must be looked at and analyzed are in millions. Here, the most prominent cost is the cost per lead because the conversion value in BFSI is of high, cost per lead becomes high automatically. It is no surprise that their marketing departments are becoming a hide-out for fraudsters cashing out the ad budgets through lead fraud. With high incentives and minimal chances of being caught, these fraudsters are feasting off the spending while the businesses deal with the loss. 

Here we list the top 3 cost benefits of using an ad fraud solution to make the most out of your ad spend. 

Best Ad Fraud Detection Solution: Top 3 Cost Benefits

Meanwhile, your ads might be created with the right intent, backed by solid creative and precise audience targeting. But the moment they enter the digital ecosystem; they step into territory you no longer fully control. 

This is why brands need more than surface-level reporting. They need genuine transparency and visibility into who is filling the lead forms and how many of these leads are actually qualified.  

That’s precisely where an ad fraud solution helps brands fight mobile ad fraud, Following are the top 3 cost benefits of an ad fraud solution: 

Reduce Customer Acquisition Costs with High-Quality Leads

A fake lead isn’t a single wasted click, it triggers a chain of downstream costs. Bots and click farms submit form fills that look legitimate, so sales reps spend hours chasing people who were never real, CRMs fill with junk records, and the numbers meant to show marketing performance stop reflecting reality. 

Ad fraud detection filters invalid traffic before it reaches the funnel, so only genuinely interested people convert into leads. Sales and marketing spend their time on real opportunities instead of cleaning up after fraud. 

Business Impact 

Lower cost per acquired customer 

Sales capacity spent on real prospects rather than dead records 

Cleaner, more trustworthy CRM data

Eliminate Budget Waste Caused by Fraudulent Campaign Optimization

Ad platforms optimize toward whoever converts. When fraudulent form fills get counted as conversions, the algorithm “learns” that fraudulent users are your best audience  and pushes more budget toward the exact sources producing fake leads. Left unchecked, the fraud compounds itself: the more fake leads come in, the more spend gets steered toward generating even more of them. 

Ad fraud detection flags invalid conversions before they feed the optimization signal, so platforms train on real buyers instead of bots. Your media budget gets pulled back out of the fraud loop and pointed at genuine demand. 

Explore To Complete Guide Here

[businessblogss]

What is Install Fraud? How to Solve Install 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. 

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.

Explore The Complete Guide Here

[businessblogss]

How mFilterIt’s Full Funnel & Omnichannel Approach Helps Detect Advanced Ad Fraud?

Many marketers still view ad fraud from a linear lens. They think bots are easy to spot, and platforms flag it. However, this assumption is no longer true.  

Over the years, advertising has transformed into a deeply interconnected, automated, and omnichannel ecosystem. Brands no longer run isolated campaigns. They operate across open web, apps, platforms, affiliates, influencers, CTV, and ecommerce media simultaneously.  

With this scale comes complexity, and with complexity comes a new class of ad fraud. One that hides deep inside the user journey, behaviour, blends into engagement, and surfaces only after real business impact has already been compromised. 

This means ad fraud is no longer a traffic problem. It does not operate in straight line. It moves across channels, adapts to campaign objectives, and embeds itself deeper into the funnel—quietly influencing optimization, attribution, and budget decisions. Therefore, to protect campaigns, brands need ad fraud solutions that must follow the full campaign journey, across environments and down the entire funnel to detect ad fraud. This is precisely where mFilterIt’s advanced ad fraud solution is designed to operate. 

How Ad Fraud Has Evolved and Why Omnichannel Protection Is the Foundation of Modern Fraud Prevention 

Sophisticated invalid traffic is engineered to resemble genuine user behaviour. It mimics human interaction patterns, rotates devices, locations, and stays just below platform thresholds long enough to be considered legitimate. The goal is no longer just to generate fake clicks or installs; it is to influence how marketers optimize campaigns across multiple channels and platforms based on false data. 

As ad fraud evolved from a visible threat to a systemic risk, protection had to evolve as well, beyond basic checkpoints – invalid ad traffic validation, click fraud prevention, into continuous fullfunnel protection. 

At the same time, brands now run branding and performance campaigns simultaneously across web, app, programmatic, search, social, OTT/CTV, and affiliate ecosystems. In such a fragmented environment, fraud naturally migrates to the least protected channel. This makes omnichannel protection not a feature, but the foundation of effective, modern ad fraud prevention. 

mFilterIt’s Omnichannel Coverage: How Protection Works Across Campaigns and Channels

mFilterIt uses an advanced approach for detection. Instead of treating channels in isolation, the ad fraud solution aligns the detection process with campaign intent, environment-specific risks, and user journey stages, powered by a unified intelligence layer across the ecosystem. Here’s how it works: 

Web Traffic Validation: Branding Campaigns – Protecting reach, visibility, and brand exposure

Branding campaigns are often assumed to be low risk, as they are optimized based on CPM (impression) models and not for conversions. But in reality, they are highly vulnerable to fraud that drains budgets without triggering immediate alarms.  

Viewability, while widely used as a quality metric, is not a measure of authenticity. Bots and spoofed environments can easily generate viewable impressions that technically meet industry thresholds but are never seen by real users. At the same time, ads are frequently served on low-quality or made-for-ad environments where content exists solely to host ads, offering no real audience value. 

Moreover, when impressions are repeatedly served to the same users due to frequency cap violations, reach appears inflated while true exposure shrinks. In such scenarios, simply validatingimpression counts is not enough. Without deeper validation of where ads appear, how often they are served, and whether exposure is genuine, branding budgets risk optimizing for visibility metrics that look healthy but deliver minimal brand impact. 

Explore The Complete Guide Here

[businessblogss]

How Out-of-Stock Issues Lead to Lost Availability and Lost Opportunities

There’s never a good time for a product to go out of stock. But it’s more problematic when it happens just when the buying intent is high. 

This not only leads customers to move towards competitors but can undo months of hard work and marketing efforts that you put into your brand and digital shelf to rank at the top.  

The impact is bigger than you might think. Global retailers lose an average of 4% of their annual revenue to out-of-stock (OOS) issues, while up to 69% of online shoppers abandon an unavailable product and purchase from a competitor instead.  

What begins as an inventory issue quickly snowballs into wasted advertising spend, declining marketplace rankings, damaged customer trust, lost repeat business, and revenue that may never return. 

That’s why product availability is no longer just an inventory concern. It’s a business growth priority.  

In this blog, we’ll explore the hidden impact of out-of-stock products, why they happen, and how brands can stay ahead before stockouts start costing them sales and customers. 

What Happens When a Product Goes Out-of-Stock?

The real impact of out-of-stock goes far beyond lost sales. Here’s what actually happens: 

It Harms Brand Perception

According to Accenture’s 2025 Retail Insight Report, 76% of shoppers say repeated stockouts affect how they view a brand’s reliability. That’s three out of four customers quietly downgrading their trust in you, not complaining, just drifting.  

It Triggers Permanent Defection

A 2024 NielsenIQ report found that 9% of shoppers who encounter an out of stock or availability issue switch retailers permanently after just one experience. On platforms where switching costs are zero and alternatives are one scroll away, that 9% compounds rapidly.

It Hands the Buy Box to Your Competitor

When your product goes out of stock, the platform algorithm doesn’t wait. It reassigns the buy box, the coveted first-mover purchase position, to whoever is available. That competitor now captures your demand, accumulates reviews, and builds ranking credibility. Moreover, they carry that algorithmic advantage forward even after your product is restocked.

It Degrades Your Entire Digital Shelf Presence

Out of stock inventory creates a cascade. Availability drops lead to bounce rates rising by up to 32% on OOS product pages (Adobe Commerce, 2024). Due to this, organic ranking falls, sponsored bidding efficiency worsens, and content scores lose context. One empty shelf slot quietly corrodes the health of your entire catalogue.

In Quick Commerce, It’s Zero-Recourse

On platforms like Blinkit, Zepto, and Swiggy Instamart, where the global quick commerce market is now valued at $123.82 billion and growing at a CAGR of over 23% (Source: Root Analysis), there is no “similar product” recommendation grace period. It’s directly a lost sale.

Out of Stock is also a Geographical and Platform Level Issue

This is where real competitive intelligence is needed.

Your product may be perfectly stocked in Mumbai but out of stock in other locations. Available on Amazon but invisible on Flipkart. In-stock in the morning, gone by afternoon on Zepto because a festive push wiped out one dark store’s inventory.

Out of stock is not a single switch that flips across your business. It’s a geo-fragmented, platform-fragmented, time-fragmented phenomenon. And if you’re looking at it through a single aggregate lens, you’re missing most of the picture.

This matters enormously for brands with Tier-2 and Tier-3 ambitions, which is now virtually every FMCG, BFSI, and consumer brand operating in India. The Tier-2 and beyond ecommerce opportunity is projected to exceed $250 billion by 2030. (Source: Delloite)

Therefore, if you’re facing stockouts in these geographies while competitors stay stocked, you’re not just losing a transaction; you’re ceding a growth frontier.

Explore The Complete Guide Here

[businessblogss]

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

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.  

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. 

Explore The Complete Guide Here

[businessblogss]

Protection from Brand Bidding with AI and Automations for Brands and Ad Networks

Competitors or affiliates may bid on your brand keywords, potentially driving up costs and diluting your brand’s presence. A comprehensive affiliate monitoring tool needs to be in place to safeguard your affiliate marketing spend and protect your brand reputation.  Identify affiliate and competition bidding on your brand keywords along with whitespaces across locations for own and competition keywords.   

Brand bidding fraud costs approx. $1.3 billion each year as competitors bid on branded keywords. Over 60% of brand terms are targeted by competitors, inflating cost-per-click (CPC) by up to 30% and reducing conversion rates by 5-10%. This not only drives up advertising costs but also dilutes brand recognition, as 50% of users may click on a competitor’s ad when searching for a brand name. This leads to confusion and lost customers.  

Businesses must actively monitor and optimize their paid search campaigns while implementing robust brand safety solutions to mitigate these risks. 

What is Brand Bidding?

Brand bidding refers to a practice that falls under paid search marketing. It’s a digital marketing strategy where brands bid on their own brand keywords in search engine advertising platforms like Google Ads and when someone searches for your brand name, your company’s sponsored listing is more likely to appear at the top of the search results page.  

But this turns into a brand reputation threat and leads when aggressive affiliate bids, potentially aiming to capture traffic and drive sales through affiliate programs or competition bids on your brand keywords.   

Why is affiliate monitoring needed for Advertisers?

Affiliate monitoring ensures the integrity of campaigns, protects brand reputation, and optimizes performance.  

Concerns in Brand Bidding:  

Campaign Integrity:

Monitoring affiliate activities ensures that campaigns are running as intended. It helps to identify if affiliates are engaging in practices like brand bidding, misleading advertising, or promoting the brand inappropriately. 

Protecting Brand Reputation:

When affiliates are bidding on brand-specific keywords (e.g., your company’s name or a competitor’s), it can confuse customers or misdirect them to other sites. Without monitoring, there’s a risk that your brand reputation could be damaged if affiliates don’t align with your brand values or make misleading claims. 

Optimizing Performance:

By closely tracking affiliate performance, advertisers can identify high-performing affiliates and cut out low-performing ones. This ensures that advertising spend is allocated effectively, improving overall return on investment (ROI). 

Organic poaching:

A major concern that brands should be careful about. It occurs when affiliates or competitors bid on a brand’s keywords, capturing traffic that would have naturally come to the brand’s website. This misappropriates organic leads and increases costs as the brand must pay commissions to affiliates for traffic that would have come organically. 

Ensures unauthorized affiliates are not bidding on brand keywords or misdirecting customers. Prevent unnecessary commissions and preserve your organic search efforts. 

Prevent affiliates from hijacking brand visibility, ensuring the brand stays front and center for potential customers without competitors benefiting from organic traffic. 

Stopping organic poaching ensures that their customers are consistently directed to the brand, which enhances trust and customer loyalty. 

Case Study 1: Brand Bidding for a Popular Shoe Brand

Problem statement: Brand was facing challenges in ensuring effectiveness of keyword the brand bidding on across multiple geographies and wants to identify competitor bidding on their brand keyword.  

mFilterIt Solution:

mFilterIt analysis and insights into the effectiveness of brand bidding, we conducted a comprehensive analysis of a well-known shoe brand across major Indian cities.  

We meticulously monitored 35 keywords related to the brand, including brand keyword variations across 35 cities. This allowed us to understand the search queries users were employing to find the brand.  

The brand garnered a significant search volume across time slots. This indicated strong interest in and demand for the brand.  

A substantial 50% of the total searches were Google Ads for the brand’s keywords. This demonstrated the competitive landscape and the efforts of various entities to capture search traffic.  

We identified 28% were competitors count which were actively bidding on the brand’s keywords. This highlighted the intense competition for visibility and market share.  

 22% of the total came from affiliates and coupon websites who were also bidding on the bidding keywords. This revealed that affiliates were also using brand keywords and running ads.  

Explore The Complete Guide Here

[businessblogss]

How Click Fraud Fuels Lead Fraud in BFSI Sector: Impact and Solution

The BFSI sector runs some of the most high-stakes digital campaigns in advertising. With customer lifetime values stretching across years of loans, credit cards, investments, and insurance renewals, every lead matters enormously. But that’s precisely what makes BFSI such a lucrative target for fraud. 

Performance campaigns in BFSI are built around a simple promise: pay per lead, optimise for cost per acquisition. It’s efficient in theory. In practice, it’s where exploitation begins. Because a bad click isn’t just a bad click — it travels downstream, pollutes the funnel, and contaminates every stage below it. Fake clicks become fake leads. Fake leads inflate CAC. And inflated CAC quietly bleeds campaign budgets dry. 

This blog covers – 

Why BFSI Brands Are Prime Targets for Lead Fraud  

Average Bot Traffic on BFSI Campaigns   

The Invisible Journey of Lead Fraud in BFSI   

How Lead Fraud Impacts BFSI Campaigns   

Why Surface Level Detection Falls Short 

What Smarter Click Fraud Detection Looks Like in BFSI   

Conclusion 

Why BFSI Brands Are Prime Targets for Click Fraud

Few industries spend on digital ads as aggressively as banks, insurers, fintechs, and financial services. The reason is clear: customer value in BFSI is unusually high. Hence the cost running campaigns is higher. 

A single customer can generate returns for years through loans, credit cards, investments, insurance renewals, or financial subscriptions. 

Because of this, keywords like “instant loan,” “credit card instant approval,” “trading app,” and “term insurance” often carry some of the highest CPCs in digital advertising. 

For fraudsters, that creates a perfect setup. The higher the cost per lead, the more profitable fake leads become. 

Fraud networks know BFSI brands can’t slow acquisition cost, especially during festive seasons, loan drives, tax-filing months, or insurance renewals. That’s why BFSI campaigns often attract unusually high levels of invalid traffic, fake installs, and manipulated engagements across both web and app ecosystems. 

What makes it worse: today’s fraudulent activity rarely looks obviously fake. It blends into normal user behaviour, making detection far harder than before. 

The Scale of the Problem: Bot Traffic on BFSI Campaigns

On BFSI campaigns, bot and invalid traffic routinely accounts for a significant share of total traffic often sitting well into double digits. 

The above table reveals a far more serious reality for BFSI advertisers globally.  

Bot traffic for different BFSI campaigns ranged from 9% to 28% of total campaign traffic. It is not a minor inefficiency; it contributes to click fraud and later contributing to lead fraud, a massive setback for brands.

In BFSI, losing nearly one-fourth of campaign traffic to bots means brands are potentially pouring millions into fake engagement, distorted performance metrics, and audiences that never truly existed. 

The Invisible Journey: How Click Fraud Becomes Lead Fraud in BFSI

BFSI marketers are accountable for metrics that sit deep in the funnel: 

Cost per approved loan 

Cost per activated credit card 

Cost per funded account 

Cost per issued policy 

These aren’t click metrics. They are business outcomes. Which is exactly why click-level fraud gets missed for so long, it enters at the top and the damage only becomes legible at the bottom, by which point the cause is buried under months of campaign data. 

The journey looks like this: 

Impression → Fraudulent Click → Fake Lead → Inflated CAC → Low Conversion → Revenue Gap 

Three Ways the Funnel Gets Exploited

Fake Leads

Bots or traffic farms fill out lead forms with fabricated or recycled personal data. These submissions pass basic validation — name, phone, email format checks — but carry no intent. They exist only to trigger the publisher’s payout. 

Punched Leads

More deliberate than bot-generated fakes. Publishers or affiliates manually manufacture form fills, sometimes using real personal data sourced from other lists, to meet contractual lead volume targets. These are harder to filter because they look human. They fail at sales qualification, not at form submission.  Explore The Complete Guide Here

[businessblogss]

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

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.

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.

Explore The Complete Guide Here

[businessblogss]

AI Slop: The New Brand Safety Crisis

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.

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. 

Explore The Complete Guide Here

[businessblogss]

Brand Bidding: Your Best Defence or Your Costliest Blind Spot?

You are running ads, building brand recall, spending on video and awareness campaigns and then you notice something odd. Somebody else carrying your own brand name is showing up in sponsored search results, and you are not the one paying for it. 

Welcome to the darker side of brand bidding. 

In this blog, we will discover – 

What is brand bidding? 

How brand bidding violations impact brands? 

What mFilterIt discovered on brand bidding violations? 

How can brands safeguard themselves from such violations? 

What Is Brand Bidding?

At its core, brand bidding is simply the practice of bidding on your own brand name as a keyword in platforms like Google Ads so when someone searches for you, your sponsored listing appears right at the top. Straightforward in theory but messy in practice. 

Because here’s what nobody tells you upfront about brand keyword bidding: you are rarely the only one bidding on your brand name. Your affiliate marketing partners are chasing eassy comissions: competitors are angling for your customers hence they are secretly in the auction of your brand keywords. And when that happens, what was meant to protect your brand quietly starts working against it. 

How Brand Bidding Violations Impact Brands

The damage of brand bidding violations done by partners and competitors isn’t always visible on the surface, which is precisely why it persists for so long before brands catch on. Affiliate compliance breaks as these violations happen. Here’s what’s actually happening behind the scenes: 

Revenue walking out the door: Affiliates intercept users already searching for you, collect the commission, and your conversion costs go up for a sale that was practically already yours. 

Competitors in your spotlight: Rival brands bidding on your keywords means confused customers, diluted brand perception, and a search landscape where your name benefits everyone but you. 

CPCs climbing without warning: During high-demand periods, aggressive bidding can push cost-per-click up by 25–30%, right when you can least afford it. 

ROAS quietly bleeding out: Higher acquisition costs hit profitability first, then scalability. Growth targets that looked achievable on paper start slipping. 

The compounding effect is what makes this particularly painful. The more successful your brand-building becomes, the more attractive your keywords get to everyone else.

What mFilterIt Found on Brand Bidding Violations: A Case Analysis

A leading D2C skincare brand in India was caught in exactly this loop.  cap that momentum, bidding on branded keywords across South, North, and North-East cities without authorisation. 

When mFilterIt deployed its brand protection solution, the numbers told a stark story: 

3,400+ ad network links detected bidding on the brand’s keywords, a scale that had been flying completely under the radar.

21% drop in CPCs recorded during the festive sales window, precisely when every percentage point matters most.

Full affiliate traffic transparency achieved, separating genuine clicks from hijacked ones and cleaning up payouts in the process.

Daily reporting gave the brand the visibility to act in near real-time, turning what had been an expensive blind spot into a managed, measurable problem. 

What Most Brands Overlook in Brand Bidding

 Many Brand bidding conversations usually stay confined to the media buying lane. But the downstream effects are far broader than CPC numbers suggest. 

Misleading ads erode trust. A customer who clicks a competitor’s ad expecting your product has a broken experience before they’ve even reached your page. Over time, that confusion compounds into a brand reputation problem, not just a performance one. 

There is also the accountability gap most brands never close: 

Affiliate agreements rarely include clear trademark bidding clauses 

Even when they do, enforcement without active affiliate monitoring is just paperwork 

Violations are often only discovered after the damage is already done 

Explore The Complete Guide Here

[businessblogss]

AI Is Accelerating Brand Impersonation: Can Brands Keep Up with the Speed of Abuse?

AI is helping businesses move faster and do more. But it’s helping fraudsters too, and that’s the part brands need to pay attention to. 

Faking a brand now takes minutes.  One prompt, and fraudsters have fake website copy, fake creatives, fake social posts, and fake communication ready to launch. It’s faster, cheaper, and a lot harder to spot. 

It’s also not limited to one platform. Fake versions of a brand can show up on social media, search results, marketplaces, apps, and messaging channels, sometimes all at the same time. 

For brands, this adds up fast: lost customer trust, lost revenue, and reputation damage that’s tough to reverse. Reacting after the damage is done just doesn’t cut it anymore. 

What brands really need is to stay ahead of it, keep watch all the time, quickly tell real from fake, and take down threats before they reach customers. That takes more than just tools or just people. It takes both, working together. 

 This infographic reveals how artificial intelligence speeds up brand impersonation, where such threats occur, and what capabilities modern brands need in order to ensure their digital identity protection. 

Learn more about mFilterIt’s Brand Protection Solution that can help you discover, monitor, and mitigate digital impersonation threats, reduce the risks of fraud, secure your customer trust, and preserve the integrity of your brand. 

Explore The More Infographic Here

[businessblogss]

How Click Fraud Fuels Lead Fraud in BFSI Sector: Impact and Solution

The BFSI sector runs some of the most high-stakes digital campaigns in advertising. With customer lifetime values stretching across years of loans, credit cards, investments, and insurance renewals, every lead matters enormously. But that’s precisely what makes BFSI such a lucrative target for fraud. 

Performance campaigns in BFSI are built around a simple promise: pay per lead, optimise for cost per acquisition. It’s efficient in theory. In practice, it’s where exploitation begins. Because a bad click isn’t just a bad click — it travels downstream, pollutes the funnel, and contaminates every stage below it. Fake clicks become fake leads. Fake leads inflate CAC. And inflated CAC quietly bleeds campaign budgets dry. 

This blog covers – 

Why BFSI Brands Are Prime Targets for Lead Fraud  

Average Bot Traffic on BFSI Campaigns   

The Invisible Journey of Lead Fraud in BFSI   

How Lead Fraud Impacts BFSI Campaigns   

Why Surface Level Detection Falls Short 

What Smarter Click Fraud Detection Looks Like in BFSI   

Conclusion 

Why BFSI Brands Are Prime Targets for Click Fraud

Few industries spend on digital ads as aggressively as banks, insurers, fintechs, and financial services. The reason is clear: customer value in BFSI is unusually high. Hence the cost running campaigns is higher. 

A single customer can generate returns for years through loans, credit cards, investments, insurance renewals, or financial subscriptions. 

Because of this, keywords like “instant loan,” “credit card instant approval,” “trading app,” and “term insurance” often carry some of the highest CPCs in digital advertising. 

For fraudsters, that creates a perfect setup. The higher the cost per lead, the more profitable fake leads become. 

Fraud networks know BFSI brands can’t slow acquisition cost, especially during festive seasons, loan drives, tax-filing months, or insurance renewals. That’s why BFSI campaigns often attract unusually high levels of invalid traffic, fake installs, and manipulated engagements across both web and app ecosystems. 

What makes it worse: today’s fraudulent activity rarely looks obviously fake. It blends into normal user behaviour, making detection far harder than before. 

The Scale of the Problem: Bot Traffic on BFSI Campaigns

On BFSI campaigns, bot and invalid traffic routinely accounts for a significant share of total traffic often sitting well into double digits.  

The above table reveals a far more serious reality for BFSI advertisers globally.  

Bot traffic for different BFSI campaigns ranged from 9% to 28% of total campaign traffic. It is not a minor inefficiency; it contributes to click fraud and later contributing to lead fraud, a massive setback for brands.

In BFSI, losing nearly one-fourth of campaign traffic to bots means brands are potentially pouring millions into fake engagement, distorted performance metrics, and audiences that never truly existed. 

The Invisible Journey: How Click Fraud Becomes Lead Fraud in BFSI

BFSI marketers are accountable for metrics that sit deep in the funnel: 

Cost per approved loan 

Cost per activated credit card 

Cost per funded account 

Cost per issued policy 

These aren’t click metrics. They are business outcomes. Which is exactly why click-level fraud gets missed for so long, it enters at the top and the damage only becomes legible at the bottom, by which point the cause is buried under months of campaign data. 

The journey looks like this: 

Impression → Fraudulent Click → Fake Lead → Inflated CAC → Low Conversion → Revenue Gap 

Three Ways the Funnel Gets Exploited

Fake Leads

Bots or traffic farms fill out lead forms with fabricated or recycled personal data. These submissions pass basic validation — name, phone, email format checks — but carry no intent. They exist only to trigger the publisher’s payout. 

Punched Leads

More deliberate than bot-generated fakes. Publishers or affiliates manually manufacture form fills, sometimes using real personal data sourced from other lists, to meet contractual lead volume targets. These are harder to filter because they look human. They fail at sales qualification, not at form submission. 

Attribution Fraud – Click Spamming, Click Injection, & Cookie Hijacking

These two tactics specifically target app-based BFSI campaigns and they operate differently. 

Click spamming is probabilistic A malicious app running silently on a device floods attribution systems with fake click signals across many publisher IDs. The bet: if a user eventually installs a banking or investment app organically withing the time period of attribution window, there is a fraudulent click already logged that will claim credit. The conversion happened legitimately. The payout goes to a fraudster.

Click injection is surgical. Malicious apps on a device monitor signal that announce when another app is downloading. The moment a fintech app begins installing, the malicious app fires a fake click, timed to arrive in the attribution window milliseconds before install completes. It hijacks attribution with near-perfect timing. Detecting it requires comparing click timestamps against install timestamps at a resolution most attribution setups don’t maintain.

Cookie Hijacking targets web-based journeys. Fraudsters manipulate, overwrite, or drop tracking cookies on a user’s browser shortly before a conversion occurs. When a customer later completes an application, account opening, or purchase, the attribution system incorrectly credits the fraudster’s affiliate, publisher, or traffic source instead of the channel that genuinely influenced the conversion. The user action is real, but the attribution trail has been tampered with, diverting marketing spend and performance credit away from legitimate partners. 

Explore The Complete Guide Here

[businessblogss]

Know the Difference Series: Click Spamming and Click Injection Explained

Digital ad fraud is growing at breakneck speed. This year, marketers are expected to lose over $114 billion to ad fraud collectively. The situation is scary, and the future seems bleak. However, many marketers and brands still successfully turn a blind eye to ad fraud, and they do it because of a lack of awareness. Awareness of the different types of ad fraud can help you be more vigilant and cautious towards the rising threats and help you save your ad budgets from getting wasted. With this blog, we hope to help you bring this awareness.

As part of the “Know the Difference series”, first, we will discuss two of the most common types of mobile ad fraud prevalent in the industry. These are click spamming and click injection. While both terms may seem similar, they follow different procedures to carry out ad fraud and steal your marketing budget.

Let’s look at them in more detail:

Know the difference: Click Spamming and Click Injection

What is Click Spamming?

Click spamming is a relatively primitive way to commit ad fraud. As the name suggests, click spamming is the act of generating a large number of fake clicks on an ad. This type of fraud mostly takes place on mobile apps but isn’t limited to them. In some cases, click spamming can also be observed on websites accessed through mobile devices.

In most cases, an unsuspecting user downloads an app laced with malware. In others, the websites visited by the users are operated by fraudsters. The malware allows fraudsters to click on ads without the user’s knowledge. This kind of fraud can take many forms. Some examples are:

The user never sees the ads, but they are live in the background, and the fraudsters are clicking on them. This is also known as click flooding.

Apps running in the background can generate clicks anytime or throughout the day (and night).

Common examples of such apps include launchers, battery-saving apps, and memory-cleaner apps.

Some fraudsters mask views as engagement by the user and get paid for that engagement. And in some cases, fraudsters may also send clicks from the device to different vendors to collect a payout.

While Google Play Store and Apple’s App Store have security measures to detect and ban apps loaded with malware, fraudsters have found innovative ways to bypass them. For instance, some apps download malware after the app has been downloaded on a user’s device by disguising it as an update for the app.

Impact of Click Spamming

The most apparent impact of click spamming is the wasted advertising budget. However, click spamming has a deeper, much more dangerous impact on advertisers- skewed advertising data. Because of click spamming, certain advertising platforms and apps (publishers) may deliver an impressive number of clicks on your ad. When this is reflected in the reporting of your campaigns, it may make said ad platforms and publishers appear more impactful than they are. This keeps the advertisers in the dark and they make business decisions based on these skewed metrics which eventually impacts the performance of the digital ad campaigns. Moreover, the advertisers also keep spending on these platforms under the impression that it is providing them with performance.

What is Click Injection?

Click injection is an ad fraud technique similar to click spamming but more sophisticated. For advertisers, that means that detecting and avoiding instances of click injection is exponentially more difficult than detecting click spamming. Instead of frantically clicking on an ad, click injection uses a single click to conduct organic traffic poaching. This is done by ‘injecting’ a click right at the point of download.

Fraudsters make use of Android apps to listen to “download broadcasts”.

Simply put, these broadcasts are sent by Android apps whenever a user downloads a malicious app that has an Android broadcaster that notifies the fraudsters about a new install. When the fraudsters are notified of an app install, they ‘inject’ a click right before the installation is complete. When this happens, the fraudulent app gets access to the user’s unique device tracking code. Using this code, the fraudsters can make their click appear authentic.

By doing this, the fraudsters receive the credit (and the payout) for the app install, even though the app install is usually organic.

Impact of Click Injection

This sophisticated fraud technique not only leads to the wastage of ad spending but also hampers the organic traffic of the advertiser. Not just the advertisers, but the genuine publishers are also victims of click injection. Due to organic traffic stealing, they lose payout to a fraudulent install.

Furthermore, click injections attribute organic downloads to fraudulent websites and apps. This can mess with the ad reporting data advertisers use to make decisions about their future campaigns. Using this skewed data, advertisers may continue spending their budgets on ineffective platforms, resulting in more wasted ad spend.

This also costs advertisers in the form of lost opportunities by diverting their attention from other effective platforms that may deliver better results.

How can advertisers protect their ad spends?

As mentioned earlier, protection begins with awareness. Now that you know about click spamming and click injection, you can look at ways to detect these activities and take corrective actions. While click spamming can be detected manually, click injection is a sophisticated form of mobile ad fraud that is hard to detect by humans. These sophisticated fraud techniques are discreet, and human-like which makes it difficult to detect with general ad fraud detection practices. For these reasons, marketers valuing holistic protection must look beyond basic ad fraud tracking techniques to prevent mobile ad fraud.

Explore The Complete Guide Here

[businessblogss]

AI Slop: The New Brand Safety Crisis

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. 

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. 

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