Commentary

AI's Growing Role In Fighting Marketing Fraud

Fraud happens in most areas of digital. It’s not surprising. Bad actors move in whenever lots of money is being spent. But just because fraud’s out there, doesn’t mean you have to accept it without a fight. 

In the arena where my team and I focus — partnerships — marketers need every available tool to discover and mitigate fraud. To do so, many brands are engaging early warning systems to spot it. And more and more brands are discovering that AI-powered anomaly detection is one of the most effective systems available.

Anomaly Detection: An Early Warning System

Anomaly detection examines campaign data for peculiar changes that may indicate problems. Advanced anomaly detection leverages AI to automatically identify when data changes are noteworthy — outside of usual norms. By combining anomaly detection with automatic alerts, you can be apprised of important issues whenever they arise. 

Of course, not all data anomalies are fraud related: competitive activity, economic changes, and tagging issues frequently drive ups and downs in data. But many companies first activate anomaly detection systems because of fraud concerns. Whatever the causes of these peculiar data patterns, it makes sense to have a way of surfacing them.

Separating Signal From Noise

Consumer marketing data rarely follow “rock steady” patterns — no one sees exactly the same sales every day. For example, a partner that drove 15 sales a day on average might drive 20-25 sales on a great day, and 8-10 on a poor day. That 8-25 range is the natural range. When figures fall outside that normal range, it may signal attempted fraud or another problem. AI and machine learning come in handy because data aren’t usually so clear cut. With AI-powered anomaly detection, the tool can use machine learning to identify the right thresholds quickly and easily. 

Fraudster greed often means that the data variances they cause will be large. That’s important, because alerts are a reason for investigation, not accusation. As a rule of thumb, look for big patterns and variances, and then collaborate with your media partners to understand the whys. 

What to Do Now

Anomaly detection systems aren’t universal. But many tools and platforms offer real-time alerts for data anomalies that may reveal fraud.

AI-based systems will determine the right thresholds and sensitivity over time, based on both the quantity of data available and its distribution. Such systems usually deliver good results and get more accurate over time. 

If your tools rely on manual settings, prepare for some trial and error. You’re unlikely to get it right with your first try. Additionally, think through your partner notification strategy. Clear and direct communication will help you resolve issues faster and drive better results for all parties. 

Finally, remember that not all anomalies will be because of fraud. But data variances are important to detect and investigate, whatever the causes.

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