#ad fraud solution

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.

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. 

Affiliate marketing, as a concept, is revolutionary. It enables businesses to make money by using the influence of popular online publishers. When the right affiliates are involved, affiliate marketing not only drives more sales, but also boosts long-term brand performance metrics like brand recall value and trustworthiness of the brand.  

At the same time, by design, affiliate marketing ensures the publishers bring their A-game in getting brands maximum visibility and ensuring their audiences view them in a positive light. After all, more sales also translate to better affiliate payouts for the publishers.  

Thanks to this symbiotic nature of affiliate marketing, it is nearly the perfect marketing activity. Nearly.  

For years, impressions and viewability have been the go-to metrics for evaluating campaign success. They help understand whether the ad was delivered and seen. But was it actually paid attention to?  

That is the real question. And that’s where the conversation is shifting. 

As brands expose themselves to more open forums like websites, apps, OTT, CTV, etc., they begin to look beyond visibility and focus on something far more valuable, i.e., attention measurement, meaningful human attention your ad received.  

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

You launch a referral program with the intention to acquire new users to your app. When analysing your campaign progress, you see that your referral champion is a single user who somehow ‘invited 200 people overnight’.   

You feel confident to spend more on these campaigns.

On your dashboard there are 200 people, but upon deeper analysis you see that only a few retain as users. That’s a sign that something is not right. It’s not the usual invalid traffic, but a more advanced and difficult version of ad fraud. Referral and coupon fraud are advanced types of mobile ad fraud which impacts the action driven KPI’s and can skew your data.

Meta’s advertising ecosystem (Facebook, Instagram, Messenger, and the Facebook Audience Network) is where billions of marketing dollars flow every single year. The platform offers unmatched reach, precise targeting, and a direct line to almost every consumer demographic.

But here’s a number that should make every CMO pause. Meta reportedly projected 10% of 2024 sales came from scam and fraud ads.

This is not just a statistic. This directly impacts your budget, brand, and business. And considering that Facebook is an ecosystem in itself, it carries the three major risks that you need to be aware of:

Ad fraud that drains your budget invisibly

Brand safety violations that put your ads next to content you would never approve

Brand infringement that lets fake versions of your brand steal your customers

Now the question is, what each of these risks include and how to fight back? This is exactly what we are going to cover in our detailed guide. Continue reading further to know more.

Why Meta Deserves Your Closest Attention While Evaluating Fraud Risks

Not all platforms carry equal risk. Meta is unique because of two things working at the same time: enormous scale and an open, user-generated content environment.

When you run a campaign on Facebook or Instagram, your ad does not just appear on those apps. It travels to thousands of third-party apps and websites that Meta partners with through the Facebook Audience Network. You never approved those placements individually. You often do not even know where your ad ended up.

And while Meta does have its own fraud filters, no platform can catch everything because of its closed walled garden nature. The numbers from mFilterIt’s tracking across geographies make this very clear:

However, these numbers only address ad fraud risks. But the problem goes beyond that, including brand safety and brand infringement risks.

Risk 1: Ad Fraud on Facebook- When Ad Budget Gets Drained on Invalid Traffic

Ad fraud is one of the major growing concerns in digital advertising today. Your dashboards look healthy. Clicks and leads flow in but somewhere in that data, real impact gets affected.

What Ad Fraud Actually Looks Like on Meta

There are various types of Facebook ad fraud. It operates in layers across the full campaign funnel:

Invalid Clicks & Bot traffic

Bots and automated scripts click on your Facebook ads at scale, inflating CTR, traffic volumes and campaign performance signals. Due to this, your algorithm thinks the ad is performing well and keeps spending on placements where bots are visiting.

Fake Leads or Punched Leads

CRMs get flooded with fake leads containing incorrect contact details like fake phone numbers, invalid emails, or zero intent to buy leads, often generated by click farms or automated scripts. This creates a false impression of campaign success while wasting the sales team’s time on low quality prospects.

Click Injection

Fraudsters fire fake click right before conversion is registered for app install campaigns, hijacking the attribution and getting paid for installs they didn’t influence.

Duplicate Users

The same user or bot keeps interacting with ads multiple times across sessions, artificially inflating your reach and frequency metrics.

Engagement Fraud

Fake page interactions, likes, comments, and video views generated by bots incentivized networks to make campaigns appear successful.

Audience Network Fraud

Low-quality publishers, device farms and invalid traffic originating from third-party apps and websites participating in Meta’s Audience Network.

Ad Stacking/Hidden Ads

Multiple ads are layered on top of one another, so only one is visible to the user, while impressions are counted for all ads. This is more common in programmatic ecosystems but can affect traffic sources feeding Meta campaigns.

These fraudulent ad fraud techniques used to generate invalid traffic directly create financial damage. You pay for every fraudulent click, every fake form fill, every bot-generated impression. However, the more serious damage is to your data.

The Domino Effect of Ad Fraud

Marketing teams optimize toward fraudulent signals, pushing more budget into bot-heavy placements. Genuine audience reach shrinks as budget gets misallocated.

Sales teams waste hours chasing junk leads, higher CPL, lower morale, and missed revenue targets. Attribution breaks entirely. Nobody knows which campaign, creative, or channel actually worked. Future campaigns are planned on corrupted historical data, repeating the cycle.

Risk 2: Brand Safety Violations on Facebook- When Ads Get Placed Beside Irrelevant or Harmful Content

Imagine spending weeks on planning a campaign; it’s creatives with impactful visuals and precise targeting. And then those ads end up running beside video glorifying violence, drug abuse, etc.

We know you did not choose that placement yourself; you might not even know it happened. But your audience saw it, and they associate your brand with that content.

This is what brand safety violations on Meta or Facebook impact: your brand reputation and brand perception.

Why is Brand Safety Harder to Control on Meta?

Facebook’s algorithm decides where your ad appears based on audience matching and bidding. Content is created by billions of users in real time. The Facebook Audience Network extends your ads to thousands of external apps and websites, none of which you have individually approved or audited. As a result, your brand ads end up appearing alongside content that violates every value your brand stands for.

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. 

Device Fingerprinting & IP Analysis

Fraudsters often disguise their identity using spoofed devices, anonymized browsers, and rotated IPs. Your tool should apply advanced device fingerprinting to track devices across campaigns, combined with real-time IP reputation scoring to catch proxies, VPNs, fraud networks, and repeated offenders. 

This helps detect same physical devices with multiple accounts running via app cloning, parallel spacing, or VPN cycling, creating multiple device environments on a single device. IP-only tools cannot detect this. Device environment analysis can. 

AI-Powered Detection Engine

Look for a solution that uses machine learning trained on large-scale, multi-industry datasets to flag both known and emerging fraud patterns. The ability to adapt to emerging patterns is what makes the difference between catching fraud that existed last quarter and catching fraud that is happening now.  

Sampled detection is a known gap. Fraud actors deliberately keep volumes below sample thresholds to avoid detection. Hence, a key evaluation question should be whether the click fraud prevention tool evaluates every data set, or does it work from a statistical sample. 

Custom Rules Engine

Every brand runs unique campaigns. A good tool should offer flexible custom rule configurations, letting you set thresholds for frequency, geo-targeting, source type, traffic origin, and campaign duration. This enables a fraud strategy that aligns with your media goals and market dynamics.