“Fraud filtering” gets mentioned in almost every UA pitch, but the phrase usually stays vague on purpose. The specifics matter, because different fraud techniques require genuinely different detection methods — a check built to catch one pattern will miss another entirely. Here’s what’s actually happening on the other side of a fraudulent install, and what stops it.
The main patterns
Click flooding. A source fires an enormous volume of clicks — sometimes thousands per device — attributed to real, organic users who were always going to install the app anyway. Because installs are happening for genuine reasons, one of the flooded clicks statistically lands within the attribution window and steals credit for an install the source did nothing to generate. This is less “fake install” and more “stolen credit for a real one,” which makes it harder to catch with a single check.
Click injection. A malicious app already on a device detects when another app is being downloaded and fires a fraudulent click in the seconds before install completes, winning attribution ahead of the actual source that drove the download. Click-to-install time is unnaturally short and telltale here — a legitimate user rarely clicks an ad and installs within a second or two.
Device farms. Rooms of physical phones (or increasingly, cloud-based device emulators) running scripts that install apps, generate a few in-app events to look plausible, then uninstall and repeat. The devices are real, which used to make this pattern hard to catch on device ID alone — but behavioral patterns give it away: identical session lengths, impossible usage rhythms, and device fingerprints that show far more app-install churn than any real phone would see.
SDK spoofing. Instead of touching a real device at all, fraudulent traffic fabricates the attribution SDK’s data package directly — sending an MMP the request that looks like a legitimate install without any install, click, or device ever existing. This is the most purely synthetic fraud pattern, and it’s why signal-matching against real device and network data matters more than trusting any single reported event at face value.
Bots and emulators. Simple automated scripts simulating installs and basic events at scale, usually the crudest and easiest pattern to catch — but only if anyone is actually looking for it.
What actually catches each of these
No single check catches all five patterns, which is exactly why fraud prevention has to be layered rather than a single filter bolted onto a dashboard.
- Device and IP signal validation catches device farms and much of the bot traffic — genuine devices have plausible usage histories; farmed and emulated ones show patterns that don’t occur in normal phone use.
- Click-to-install timing analysis is the primary defense against click injection — a click that’s followed by an install in under a couple of seconds, consistently, is not a coincidence.
- Click volume and duplicate-click analysis catches click flooding — a source sending far more clicks than plausible ad impressions justify is a visible pattern once you’re looking at volume, not just conversions.
- Cross-referencing against your MMP’s own fraud suite catches SDK spoofing, because the MMP’s SDK is the thing being spoofed — matching what a source reports against what the SDK independently observed closes that gap.
- Post-install behavioral analysis catches all five patterns retroactively — genuine users have session patterns, retention curves, and event sequences that fabricated or farmed installs consistently fail to replicate, even when everything upstream looked clean.
Why this has to happen before billing, not after
A lot of “fraud protection” in this industry is really fraud refunding — junk traffic gets billed, flagged weeks later during a reconciliation pass, and credited back if you noticed and asked. That’s a fundamentally different guarantee than validation that happens before an install ever reaches an invoice.
The difference matters for a simple reason: refund-based fraud handling puts the burden of detection on the advertiser. You have to notice the anomaly, build the case, and request the credit — and most teams don’t have the bandwidth to audit every source every month closely enough to catch a subtle pattern like click flooding. Pre-billing validation puts that burden where it belongs: on whoever is running the campaign.
What to ask before you trust a fraud claim
- What specific checks run, and against what data? “We use AI-powered fraud detection” is not an answer. Device signals, IP monitoring, and MMP-level validation are.
- Does invalid traffic get filtered before or after billing? This is the single most important question, and the one vaguest answers try hardest to avoid.
- Can you show me what got rejected? A source with genuinely strong traffic quality won’t mind you seeing what didn’t make the cut — it’s proof the filter is real.
At RVM Ads, this layering runs on every campaign as a baseline, not an upsell: device, IP, and MMP-signal validation before an install is ever counted, click-timing and volume analysis to catch injection and flooding, and post-install behavioral checks to catch what slips past the first pass. Junk traffic gets rejected before it reaches your bill — and the report shows exactly what was cut, so it’s yours to audit, not just take our word for.
