How Antifraud Algorithms are Trained: What They See in Account Behavior and How to Adapt to It

Blog » How Antifraud Algorithms are Trained: What They See in Account Behavior and How to Adapt to It

If you work in affiliate marketing, you know that ad platforms like Meta and Google are sharply against users having multiple accounts. The reason is simple — their goal is to gather as many ?‍♂️ unique users as possible with predictable behavior.

That means any activity that looks suspicious or artificial automatically falls ? under the antifraud system’s radar. These systems are not just a set of filters but full-fledged machine learning models that study user behavior day by day and learn to distinguish real users from fakes, genuine accounts from farms. The more data they get, the smarter they become. And they generate a huge amount of data.

A huge list of parameters you can find with one click on the Device Info website.

It’s important to understand that one weird click doesn’t pose any risk, but if user behavior deviates from the usual pattern — the chance of ❌ getting banned grows rapidly. The “ban first, ask questions later” approach is still in effect. Usually, it’s hard or even impossible to prove you’re right after a ban. So before you drive traffic through another batch of accounts, it’s important to understand ? what exactly the platform sees and how its algorithms are trained. Also — how to adapt to stay one step ahead.

What Kind of User Data Platforms Collect

To train antifraud algorithms in a more effective way, Meta, Google, and others collect everything they can about users — even before they click the first button. Here are the types of data that interest them most:

? Technical metadata. This includes IP address, device fingerprint, system language, timezone, browser user agent and many other parameters that don’t mean much separately but when combined give a unique digital fingerprint. Even if you use a new browser, the platform can still figure out “this user has been here before”.

? Behavioral patterns. Click speed, how you move your cursor, how much time you spend on the page, how you navigate it and between sections. The behavior of a real person and a farm account differs, and antifraud systems catch that too.

Average daily time spent on Facebook by age group, according to eMarketer. 

? Integration level. If you logged in somewhere via SSO from Google or Meta, they start tracking how you behave on other sites, what apps you install, how often you change devices. The deeper the integration, the closer the monitoring.

?️ Tracking via invisible tech. Canvas, WebGL, LocalStorage, cookies, APIs — these are tracking tools that are hard to bypass. Even if you think you cleared your traces, the browser still leaves a “fingerprint” that identifies you.

? Cross-platform and ID linking. If you logged in from phone, then desktop, then via proxy — the platform will try to link all those sessions into one profile. It is done on the basis of dozens of parameters — from IP and time to behavior. Even if accounts differ, the system can guess the same person owns them.

Thus, ad platforms collect far more data than just an email for login. Every click, every mouse move, even device specs — ? all data goes to their servers. So hoping to escape suspicion by changing IP or creating a new account is naive. A better spoofing is needed here.

How Antifraud Algorithms Detect Greyhat Accounts

When you launch a new account, it might seem “clean” as there is no generated data yet. But that’s enough for antifraud. Trained algorithms analyze dozens of parameters — and if something is unusual, the system notices.

⚠️ Untypical behavior. If an account owner rushes through funnels, clicks needed buttons instantly, works on a timer — that’s suspicious. Real people behave less predictably: pause somewhere, get distracted or even quit a session. Too precise and consistent activity can look automated.

? Repetitive actions. If several accounts do the same thing from one device, one network, or at the same time — that stands out. Especially if they use identical templates: style, similar names, site navigation. The algorithm marks the group as potentially belonging to one user.

? Geography and time mismatches. The situation when one account is launches from Germany, another a minute later from Vietnam, a third 10 minutes later from Brazil — all with identical behavior — looks very unrealistic. The same applies to timezones, system time, language settings — mismatches raise suspicion.

In the Dolphin Anty antidetect browser timezone, language, and geolocation can be set automatically or manually.

? Links to banned accounts. You might think you start fresh, but if you reuse proxies, cookies or behaviors from previously banned accounts, the algorithm can see the pattern. Your new account may get banned before you even start driving traffic.

? Template content and actions. Platforms analyze not just technical data but also texts, page names, ad campaigns and even action sequences. Copying old launch behaviors gets noticed too.

? Weak proxy and VPN masking. Just turning on a proxy isn’t enough. Using a suspicious or widely flagged IP, unstable connection or poor configuration (mismatched language/timezone, no deep emulation) will get detected. Sometimes external databases aren’t needed — algorithms can identify suspicious routing and behavior on their own.

You’re not just a new login to the algorithm, but a complex set of features. If just some of them are “off”, antifraud will spot it. Greyhat accounts get banned not by “bad luck” but because the system sees patterns it has learned to recognize.

How to Adapt: Principles of Bypassing Antifraud in Affiliate Marketing

When you realize how much ad platforms track, it’s clear: incognito mode or just putting on a proxy isn’t a bypass, it’s just a way to get on a blocked site. To ? really fool antifraud, you need to build a new digital identity — carefully and thoughtfully.

? Why proxies don’t work anymore. Cheap proxies often end up in suspicious IP databases or are used by hundreds of people and already banned. Antifraud has access to these databases. Big tech companies have their own huge databases too. 

Even Pixelscan easily detects proxies. Google and Meta have far more technical capabilities.

? Unique environment — the basis for long account life. Platforms pay attention to everything: device model, OS, system language, screen size, timezone, font list, video card, even small parameters like WebGL or touchpad. Each account should live in its own environment — unique separate characteristics.

? Creating a real account. Fake behavior is easy to spot. If you speed through funnels, click with perfect accuracy and launch ads 5 minutes after registration — that’s  not normal. Adding “digital noise”, varying step sequences, deeper page exploration make a real difference. Imitating a real user is half the success.

⚙️ Smart automation. If you use scripts, avoid template actions. Randomization and manual steps increase account longevity. Platforms are getting better and better at spotting repetitive behavior.

Each profile in Dolphin Anty can have its own — unique — action scenario.

? Budget and account age equals trust. A fresh account with small spendings raises fewer questions. The longer it lives, the more it looks like a real user. Even if one parameter looks suspicious, the account’s reputation can save it from ban.

Antidetect Browser — The Tool to Bypass Antifraud

Real comprehensive antifraud protection is a ? antidetect browser. It lets you manually set each parameter of your digital “self”: language, user agent, timezone, WebRTC, Canvas, AudioContext, fonts, extension list and much more.

Only a small part of digital fingerprint parameters can be set via Dolphin Anty antidetect browser.

Yes, launching and properly configuring each profile takes time. Yes, manual setup is slow. But that’s exactly what gives ? the so much needed uniqueness, so antifraud perceives your accounts as real people, not clones from one original pattern.

Conclusion

Antifraud systems get smarter every day. Platforms ? invest millions to spot greyhat users — and do it better and faster. The bigger and richer the platform, the harder it is to stay unnoticed.

But if an account gets banned — it’s not the end of the world. In affiliate marketing, don’t get used to profiles but rather treat them as expendables. One gets banned — launch a new one immediately, with a different environment and behavioral patterns. Especially since antidetect browsers let you do this not only quickly but conveniently.