We’ve been running campaigns for long enough now that I have historical data on which influencer partnerships worked and which ones failed due to fraud or misrepresentation. What I’ve been trying to do is use that to build a predictive model—can we spot fraud risk before we actually launch the campaign and spend the money?
The theory makes sense: if we can identify patterns that predict failed partnerships, we should be able to flag them early. In practice, it’s messier.
Some of the predictors are obvious—sudden follower growth without engagement increase, audience composition that doesn’t match the creator’s claimed expertise, engagement rates that are statistical outliers. But the predictive power is less than you’d hope for. A creator can look fine on paper and still deliver a disaster, and vice versa.
What’s been more useful is combining the AI scoring with campaign performance predictions from other sources. Like, we can forecast what kind of ROI we’d expect from a given creator based on similar past partnerships. When the AI fraud score is high AND the expected ROI is really optimistic (too good to be true), that’s a stronger signal that something’s off. Conversely, if the fraud score is borderline but the creator has a track record in our database of consistent performance, we’re probably okay.
I’m also noticing that cross-market comparisons help. If a creator is operating in both US and Russian markets, we have more data points, more history, more opportunities to spot inconsistencies.
The honest answer is: AI can probably reduce fraud risk by 50-60% if you set it up right. But you’re never going to eliminate it without either being extremely conservative (refusing too many good creators) or keeping humans involved in every single decision.
What metrics are you actually looking at to measure whether your fraud detection is working? And how are you balancing false positives against false negatives—is it better to reject some good creators, or accept some risk?