We’ve hit this wall a few times now and I’m looking for how others handle it. Our AI model was trained on a mix of Russian and US influencer data, and it’s started flagging things differently depending on the market context. An influencer in Moscow with 50K followers and 8% engagement gets a different risk score than someone in NYC with the same metrics. On the surface, that makes sense—different platforms have different baseline engagement rates. But when we’re deciding whether to green-light a campaign, we’re now facing decisions where the same creator gets a high-risk flag in one market and a low-risk flag in another.
I tried normalizing the data better, but we keep running into this: are we solving a technical problem or a cultural one? The AI doesn’t really understand why engagement patterns differ. It’s pattern-matching, not reasoning. So when I’m sitting in a meeting with a brand and they’re asking, “Is this influencer safe or not?” I can’t just point to the model and say “the algorithm says yes.” They want clarity.
I’ve started asking: what if instead of trying to make one model work across both markets, we treated market-specific validation as a feature, not a bug? Like, build parallel playbooks for each market, then see where they agree and disagree. The conflict becomes useful signal, not a problem to solve away.
How are you handling cross-market disagreements in your fraud detection? Are you normalizing everything into one score, or keeping market signals separate?
This is a signature problem of global marketing automation—you’re trying to apply a universal rule to contexts that aren’t universal. I’ve seen this play out on the performance side too: what optimizes spend for US audiences tanks in emerging markets, and vice versa. The answer isn’t a better algorithm, it’s acknowledging that you need separate models with shared governance. Here’s what we do: we maintain country-specific risk thresholds, but we feed them the same data pipeline. That way, when a creator gets flagged differently across markets, we can actually see what changed between the model runs. It’s usually feature engineering—what counts as ‘suspicious’ engagement in one market is normal in another. Document those differences explicitly, and suddenly you can explain decisions to clients. The AI becomes transparent instead of mystical.
We keep the models separate, honestly. Russian influencer landscape is different enough—different platforms, different creator economics, different fraud patterns—that trying to force one model felt like false precision. We built a Russia-specific model trained on Russian data and a US-specific model trained on US data. When we work with clients on cross-market campaigns, we present both scores and explain the difference. Clients actually respect that. It shows we understand the nuance. The overhead is real—more models to maintain—but the credibility is worth it. Plus, when you have separate models, your expert validation layer knows exactly what to look for in each market.
I’m just gonna say—it sounds like a lot of complexity for what might be a simpler problem. Like, are you sure the algorithm is the issue, or is it just that different platforms and creator communities genuinely work differently? I post differently on TikTok than Instagram. My followers have different expectations. An algorithm that doesn’t account for that isn’t smart, it’s broken. Maybe instead of normalizing everything, you actually need to respect the differences?