Stopping Ad Fraud by Using Machine Learning

Machine Learning
Stopping-Ad-Fraud | Business Magazines | CIOLook

Stopping Ad Fraud by Using Machine Learning

Since the start of the new millennium, mobile phones have become one of the most essential pieces of technology, which is being used every day by billions of people around the planet. With each passing day, these small handheld devices became smarter and replaced many of its larger cousins. However, just like any other technology, there’s always someone sitting somewhere who wants to extract money from people without doing much.

Ad fraud is one such fraudulent activity that could eventually break an industry. Every year few shady fraudsters cause ad fraud damages of as much as $20 billion, which is expected to rise even further. Do you know what the worst part is? Despite all the efforts to curb the fraud, it still evolves and wreaks havoc.

During the early days of making fraud, fraudsters had used a straightforward method. They turned the helpful bots into a villain, which in return drove substantial traffic to websites and bought cheap traffic through auto redirects or employed people to install apps in click farms. As one clicks, the app gets installed automatically, and the fraudsters’ job is done. However, advertisers soon caught this method, and they shifted their focus towards engagement, ROI and last click attribution.

Sadly, nowadays it seems like ad fraud has turned in to the famous fictional supervillain Thanos. Currently, these shady fraudsters are even employing human employees, in order to imitate human behavior. They have also developed apps that can hijack smart devices to create simulators that generate fake install from the bot networks.

So! Now you might ask how these scams are affecting digital advertising. The answer is quite simple; financial gain is the main motto of these scams. Nevertheless, it is insufficient information that eventually affects various critical decisions related to the campaign. Adding to the woes of wasted resources, inaccurate statistics and biased campaign results leave the campaigners shocked.

Now the question is, “how to stop this nuisance?”

The answer is we need very sophisticated detection tools powered by Machine Learning. Now some might ask, Why Machine Learning? Because traditional approaches mostly relies on rules created by humans. The main problem with this is, it’s completely predefined, and it’s only a matter of time when fraudsters figure out ways to bypass these traditional approaches.

Whereas Machine Learning has the much-needed ability to reduce labor costs, generate insights that are entirely unexpected and new and it can also create predictive models from raw data. Machine Learning also has the power to take real-time automated decisions, which weren’t possible earlier. If harnessed correctly, the low latency manner of the technology can eventually affect business activities offering a real competitive advantage for the organizations.

That eventually leaves Machine Learning powered solutions that constantly learns new fraud patterns and refine its rules automatically. With each passing days fraudsters are basically employing newer techniques that can copy human behavior. However, here the machine learning algorithms comes into action and eventually helps to detect fraudulent behavior before it meets any human eye.

Thanks to Machine Learning and AI, nowadays organizations can detect data abnormality in as low as five to ten milliseconds and can take a decision based on the information. AI and Machine Learning coupled with each other can be very affective, which can result into significant advancements within many areas of businesses.

But! But! But! No System is perfect!

Yeah, you are also thinking this! Right?

Yes! Just like any other piece of technology Machine Learning also has its fair share of drawbacks. Machine learning has the much-needed capability to become the means of fraud detection. In spite of this, hard work needs to be done in order to build filters instantly rather than focusing on a technology that can lead to less transparency. Filters that can stop fraudulent activities without even rejecting install from genuine resources needs to be built.

Lastly, there’s no substitute for human intelligence. So, as of now, in addition to using Machine Learning, one must take the approach supported by data, insights and human intervention for better results. Also additionally, having knowledge and experience in fighting ad-fraud is the key to develop better algorithms and models that can eventually curb ad fraud to a large extent.

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