We’ve been using AI-powered fraud detection tools for about eight months now, and I’m at the point where I trust them about 70% of the time. Which means I’m manually verifying the other 30%.
The issue is specific to bilingual and cross-market work: fraud patterns look different in different regions. What counts as suspicious in the US (sudden follower spikes, bot-like engagement patterns) might be completely normal in other markets. Or the fraud might be more sophisticated in one region and easier to spot in another.
I ran a test recently where I flagged 20 influencers as potential fraud risks based on AI red flags across both markets. We investigated manually. What I found:
- 8 were genuinely fraudulent (75% accuracy on actual fraud)
- 7 were false positives (the engagement patterns were just culturally different)
- 5 were borderline cases that probably had some bot engagement but were still worth partnering with
So the AI gave me a useful shortlist, but it created as many false positives as it caught real fraud. And in a bilingual context, false positives are expensive—you miss real partnerships.
I’ve started using the AI tool as a first filter (“here are the obvious red flags”), then I layer in secondary validation: manual profile review, comment quality assessment, audience demographic consistency. The AI is honestly better at catching egregious cases, but for the gray zone—which is where most cross-market influencers live—I don’t trust it yet.
Does this match what you’re seeing? At what confidence threshold do you actually move forward with a partnership, and how much time are you spending on manual verification?
Your 70% trust threshold is actually realistic, which is refreshing to hear. I’ve seen teams swing too far in either direction—either trusting AI completely (dangerous) or treating it as useless (wastes a good signal).
What we’ve built is a tiered trust model: we use AI as a screening layer for obvious fraud (the 70% it catches reliably), but for partnerships over a certain budget threshold, we add manual verification. The cost of verification is worth it versus the cost of a fraud partnership.
For cross-market work specifically, we’ve started tracking false positive rates by market. If AI fraud detection has a 30% false positive rate in Russian-speaking regions but only 15% in the US, that tells us the model was trained primarily on US data. We factor that into our threshold decisions.
My recommendation: don’t try to make AI reliable at 100%. Get it reliable enough to save you time on obvious cases, then use that time for strategic verification on the margin cases. The hybrid approach is probably the honest solution for at least the next couple years.
This is exactly why I tell my team that fraud detection is a process, not a tool. AI is one input. We also do community checks (do other agencies report this influencer as problematic?), we look at contract history, we check if they’ve worked with real brands before.
In bilingual markets specifically, I’d add this: get eyes on the influencer from someone who actually understands the cultural context. A Russian-speaking market expert might catch fraud signals that an algorithm trained on US data would miss.
And be honest with your clients about limitations. If you’re recommending an influencer and you used AI fraud detection, tell them what the detection actually caught and what it didn’t. It sets expectations and protects everyone.
From the creator side—and please take this constructively—AI fraud detection sometimes flags creators like me who are just… growing authentically. I had one brand almost drop me because their AI flagged my engagement rate as suspicious. Turns out it was because I’d posted something that really resonated with my community, so engagement spiked legitimately.
My worry is that as brands rely more on AI fraud detection, genuine creators in emerging markets get automatically filtered out because their growth patterns don’t match US norms. That’s a real risk in bilingual spaces.
Maybe the solution is for the bilingual hub to help calibrate what “normal” looks like in each market, so the fraud detection isn’t just comparing everything to a US baseline?
Chloe raises a really good point. We’ve had to educate clients on this: if you’re using a single fraud detection model across markets, you’re going to bias against genuine creators in smaller or faster-growing markets. That’s a business risk—you miss partnerships, and word gets out that your vetting process is unfair.