Digital Ads have revolutionized the way Advertising is leveraged by businesses. Programmatic Media buying has made Ads more accessible and trackable.

The global Ad market has crossed a whopping $700 billion in Ad spending out of which more than 550 billion is projected to be spent in the form of programmatic Ads[Source].

But what makes them so special?

Programmatic ads not only make sure your Ad reaches the right audience but with the power of Advanced Data Analytics you can make sure to optimize your Ads in real time to generate exceptional ROAS. 

And by leveraging AI and ML, the possibilities end up being endless. This created a great way to minimize challenges and deliver what’s best.

But what if the algorithms are being manipulated using Bots or Stacking?
Is there a way to detect it? Yes, there is. 

Ad Fraud Detection

Ad fraud refers to any malicious or deceptive behavior aimed at generating fraudulent impressions, clicks, or engagements with digital ads. This leads to wasted ad spend and distorted campaign metrics. 

Ad fraud detection identifies and prevents fraudulent advertising activities in the advertising ecosystem. Detecting and combating ad fraud is crucial for maintaining the integrity and effectiveness of digital advertising campaigns.

Ok, Let’s understand Ad Fraud in detail.

Imagine investing in a digital ad that is performing amazingly on an ad provider’s dashboard, but you see no response or potential conversions whatsoever. You must be a victim of Ad fraud.

Ad fraud can take various forms. These include

Bot Traffic 

In advertising, a bot is a program or script that mimics the behavior of human beings and generates fake website impressions, clicks, or conversions by using automated scripts or programs to manipulate Ad outcomes.

Ad fraud detection systems aim to identify patterns and anomalies in traffic to differentiate between genuine human interactions and bot-generated activity.

Invalid Traffic

Among these types of traffic are non-human visitors (bots, crawlers, etc.), traffic that is artificially inflated, or traffic that is low quality or that does not provide genuine engagement with users.

Ad fraud detection mechanisms analyze traffic patterns, user behavior, and other indicators to identify invalid traffic. 

Click Fraud 

Click fraud involves artificially inflating the number of clicks on ads to generate higher ad revenue or exhaust competitors’ ad budgets. This is done by using automated computer scripts, manual labor, or other methods.

By doing so, fraudsters are able to mimic legitimate user behavior and generate false clicks that are not actually from genuine customers exhausting your budget faster.

Advanced Ad Fraud detection algorithms can detect suspicious click patterns and unusual behavior to identify potential click fraud instances.

Ad Stacking and Ad Injection

‘Ad stacking’ involves stacking multiple ads on top of each other, making only the top ad visible to users, and ‘Ad injection’ refers to the unauthorized insertion of ads into web pages without the knowledge or consent of website owners or advertisers. 

This will give you tons of impressions, but the ad won’t get any interaction or engagement at all.

Ad fraud detection algorithms can identify such fraudulent ad placements and save you unnecessary bills.

Each of the above-mentioned fraud’s detectable and can easily be prevented or resolved. Wonder how? 

Let’s understand Fraud Detection Techniques 

Ad Fraud Detection Techniques 

With some Ad platforms still lacking transparency and inducing dead clicks, scammers, and competitors deploy bots or humans that tend to mimic users’ behavior and rip off ad budgets. 

But how does one detect these frauds if they’re so human-like? 

Well, there are some preventive measures taken by both platforms and users to avoid them, but they’ve their own limitations. Thus, creating a need to implement Ad Fraud detection techniques. 

Some of the most common detection techniques are  

Traffic Analysis 

Ad fraud detection systems analyze traffic patterns, IP addresses, user agent data, and other characteristics to identify suspicious behavior. 

This includes examining traffic sources, frequency of clicks or impressions, and abnormal traffic patterns that may indicate fraudulent activities.  

Machine Learning and AI 

Advanced machine learning algorithms can be trained to recognize patterns and anomalies in large datasets. These algorithms can learn from historical data to detect fraudulent activities and adapt to evolving fraud techniques.  

They can identify suspicious patterns, flag potentially fraudulent activity, and improve over time as they encounter new fraud patterns.  

Device Fingerprinting 

Ad fraud detection can leverage device fingerprinting techniques to identify unique characteristics of devices and detect fraud across multiple ad impressions or clicks originating from the same device.   

Behavior Analysis 

Analyzing user behavior data, such as session duration, click-through rates, mouse movements, and other interaction metrics, can help identify abnormal patterns that may indicate fraudulent activity.  

Collaboration and Data Sharing 

Ad fraud detection benefits from industry collaboration and data sharing. Companies can share data on fraudulent activities and known fraud sources to build collective intelligence and enhance detection capabilities. 

Now that you’re aware of how to detect ad frauds, all that’s left to do is choose the right ad distribution platform and utilize programmatic Ads. And the right platform would be the one that takes care of these preventive measures 

Preventive Measures Against Ad Fraud  

Ad Verification 

Ad verification services provide pre-bid and post-bid solutions to assess the quality and validity of ad placements. These services help advertisers verify that their ads are being displayed in inappropriate and brand-safe environments.  

Traffic Filtering 

Ad fraud detection systems can filter traffic by analyzing various parameters, such as IP addresses, user agent strings, and historical data, to block suspicious or invalid traffic. 

Real-Time Monitoring 

Real-time monitoring and analysis of ad campaigns enable prompt detection of fraudulent activities. Automated systems can flag and block suspicious activity as it occurs, minimizing the impact of ad fraud.  

Ad Fraud Reporting 

Encouraging users, publishers, and advertisers to report suspected ad fraud instances helps in identifying new fraud techniques and sources, improving detection mechanisms.  

Final Words 

Ad fraud detection is getting more challenging by the day for both advertisers and advertising platforms. Fraudsters continuously develop new techniques to evade detection. Thus, leveraging advanced technologies, data analysis, and industry collaboration is essential in combating ad fraud effectively. 

We at Canopus Infosystems are experts in leveraging Advanced Analytics and implementing the power of AI and Machine Learning to create Ad Analytics Portals that can minimize ad fraud in real-time. 




5 mins read


Gaurav Goyal

He is the Chief Technical Officer and Co-Founder at Canopus Infosystems Pvt Ltd. He completed his graduation in Computer Programming in 2003 and has experience in managing data science teams, quantitative research, and algorithmic trading. He’s a proven track record in specialties like robust statistics, machine learning, large data analytics... with excellence and delivered 500+ projects to 200+ clients with his teams.

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