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Fighting Ad Fraud by Combining Machine Learning With Human Supervision

Ad fraud schemes are becoming more sophisticated, and marketers everywhere are suffering the consequences. 

They’re also becoming increasingly difficult to detect and therefore prevent. No matter what marketers do, they’re finding that ad fraud is seeping into every campaign, skewing every key metric, and diminishing return on ad spend (ROAS). 

With fraudsters seemingly one step ahead of them at every turn, marketers are turning to more sophisticated prevention technologies like machine learning and artificial intelligence. These technologies have become important tools in the ad fraud prevention space, touted to save advertisers over $10 billion in ad spend

While machine learning and artificial intelligence are on the rise within ad fraud prevention efforts, it’s becoming clear that machines, despite their competence for processing knowledge, can’t fight the battle alone. Ultimately, humans are much more adept at understanding and applying logic. That’s why a combination of machine learning and manual, human intervention is crucial in the fight against ad fraud. 

The multiple benefits of using AI/ML in the fight against ad fraud

In an ad fraud detection context, Machine learning (ML) is first used to extract data patterns and identify relationships within large, historical datasets. Artificial intelligence (AI) algorithms are then trained to detect instances of ad fraud within new datasets and suggest potential risk rules. 

At this point, these rules can be implemented to block suspected occurrences of ad fraud. While the reproduction of patterns hidden in huge volumes of data can be done manually, the implementation of ML to eliminate ad fraud has several advantages: 

  • Extracts general patterns from large subsets of traffic: an important trait, maybe the most of all, is the ability to extract general patterns out of subsets of traffic: from a limited set of identified fraudulent traffic, ML can learn patterns that can be used to identify larger portions of fraudulent traffic.
  • High levels of accuracy: When it comes to pattern detection, ML technology is much more accurate than the human eye, and won’t be tarred by human error. Sophisticated traffic derived from bots skillfully mimics human behavior which ML can instantly pick up on.
  • ML is a highly adaptable technology: As ad fraud prevention technology evolves, fraudsters advance their techniques to elude detection. ML can take each instance of traffic and analyze its parameters, while rules-based programs (which follow rules that are input by humans) are not as agile. Plus, the more data you input into an ML engine, the better it becomes at identifying new patterns, which helps you sidestep even the newest ad fraud techniques.
  • Cost-effective ad fraud detection: Rules-based, human detection eats up a huge amount of resources. Just one ML system, however, can run through high volumes of data automatically.

What are the limitations of machine learning in combating ad fraud? 

ML is by no means a panacea for ad fraud detection and prevention. As with most technology, there are limitations to ML’s effectiveness, including: 

  • Ultimately, humans still need to guide the ML model: As it stands, humans still need to direct ML engines when it comes to context, and also feedback to the machine to help it improve over time and avoid generating false positives. 
  • Rules and reputation lists need to be updated by humans: These also need to be put in place by humans initially since they can’t exist without the experience and knowledge of data teams. To make sure valid traffic isn’t blocked, humans need to manually review and approve ML decisions.
  • Humans can consider the context: Ad fraud detection is usually dependent on context, and while ML can identify some patterns and impute all traffic matching such patterns to fraudulent traffic, in other contexts, these patterns might not be indicative of fraud. There are some factors that only humans can consider when deciding if ad fraud is present.
  • Other stakeholders are affected by ML decisions: Ad fraud affects many players across the chain, and they must be all involved and communicate with each other to avoid any adverse decision-making elsewhere (i.e. product teams). 

Overcome these challenges by combining machine learning and human intervention

There are numerous advantages of implementing ML into your ad fraud prevention strategy. However, certain knowledge and expertise cannot as easily be incorporated into ML models. While ad fraud prevention is a priority for marketers, one of their other main concerns is making sure that valid traffic and leads aren’t blocked. 

The good news is that marketers don’t need to choose one or the other: They can have the best of both worlds by combining ML with human intervention to help them stay one step ahead of the industry’s most notorious money drainer.

Pairing the development of ML systems with human interaction means that while your ML system detects where ad fraud may be present, human knowledge, expertise, and intelligence can help to review and refine its suggestions. Plus, humans are ultimately responsible for training the model and feeding it data to ensure the highest levels of accuracy in ad fraud detection. 

In fact, establishing an ad fraud prevention committee comprised of individuals from different affected units (marketing functions, sales functions, finance teams, and growth and analysis teams) is a great way to ensure that all fraud prevention processes and tools are aligned.

Ultimately, only by combining human insights and ML fraud detection methods can you be sure that you’re putting up the best fight against fraudsters’ ever-evolving tricks. 

This article originally appeared on Street Fight