This article is taken from our ebook- ‘Understanding machine learning in fraud prevention’. To read the full book, download here.
Some commentators think machine learning is too new a field to be deployed for ad fraud mitigation. In the last few years, expertise and technological developments have come a long way in the field of machine learning. Widely used in the cybersecurity space already, ad tech seems to only just be catching on to the value of machine learning in ad fraud prevention.
Did you know the term machine learning dates back to the 1950s? So how is it that a topic older than the compact cassette tape, is one of the hottest topics in technology today?
The key drivers behind machine learning’s prominence today include:
- Scalable infrastructure – Machine learning needs high volumes of data to reliably identify fraud. Data volumes can fluctuate depending on a variety of factors including season, so being able to scale infrastructure has driven investment in machine learning.
- High compute power – Ingesting high volumes of data, and in TrafficGuard’s case, streaming data requires a lot of computing power to invalidate, report and mitigate traffic in real time.
- Affordability – Previously, in order to access the processing power required, you would need to own your own infrastructure, making machine learning prohibitively expensive. Today, businesses can easily access powerful infrastructure on a scalable subscription model, making it more affordable.
- Access to expertise – As machine learning has become more feasible from a technological standpoint, data science, infrastructure and operations expertise have quickly evolved to make use of the technology.
The combination of accessible tech, affordability and skills development means that machine learning is finally at a point that it can be used to solve critical business challenges.
Nae-sayers that say machine learning is too new are likely the ones that have been slow to invest in the space and are now trying to discredit the field.
As well as expertise and technology, there is one more key reason that the time for machine learning in fraud prevention is now. That is the adversaries. We are in an arms race with fraudsters. Every day new tactics are discovered and new fraud operations make the media.
Two dimensional analysis, static rules and blacklists can only take us so far. Fraudsters today are well funded operations that funnel billions out of the digital advertising industry.
At TrafficGuard, we are less concerned about the fraud-du-jour and more focused on what characterises genuine advertising engagement. While our competitors are busy talking about click injection, SDK spoofing, app install farms, our stance might seem novel. But in our view, chasing tactics is an unsustainable approach to fraud prevention. Why? Because fraud adapts – if one tactic is blocked based on a certain signature, fraudsters will innovate (like any business) in order to do their job better.
Using machine learning we bring a sustainable approach to fraud prevention to protect ad spend from tomorrow’s fraud, as well as today’s.
Want to learn how sophisticated machine learning is the only way to stop the constantly evolving ad fraud? Download our eBook.