One of the harder problems I’ve faced: figuring out which fraud signals matter across different markets. A bot engagement pattern that screams “fake” on a US Instagram account might look totally normal on a Russian platform, just because of how algorithms and bot networks operate differently.
We started building a fraud detection workflow that flags things like:
- Sudden follower spikes that don’t match engagement spikes
- Comments that are mostly generic emojis or low-effort responses
- Engagement concentrated in specific geographic regions that don’t match where the creator claims to operate
- Posting patterns that align suspiciously with bot farm operating hours
- Audience composition that seems artificially uniform (no demographic variance)
This works okay on the home turf, but when we started looking at Russian creators serving US audiences (or vice versa), the rules got fuzzy. Like, different platforms have different baseline engagement rates in different regions. Something that looks low in the US might be totally normal for Russian platforms.
The other challenge: we’re mostly looking at public signals. What we’re NOT seeing are the backend indicators—like, how much of that follow-up engagement is actually from real users versus coordinated bot accounts. We’re pattern-matching based on what’s visible, but the real fraud is often invisible until a campaign goes live and the conversions don’t materialize.
I’m wondering if the fraud detection problem is actually solvable with just data and algorithms, or if there’s always going to be a level of fraud that only shows up in actual campaign performance. And if that’s true, what’s the minimal viable vetting we should be doing upfront?
How are you handling this? Are you relying on automated fraud detection, or do you have a different approach?