In 2025, fraudsters have become as advanced as arbitrageurs. Nowadays, these are not just bought clicks or proxy botnets – they are deep botsthat mimic human behavior to cursor movements; fake devices that pass fingerprint verification; and smart rotation that changes IP faster than you can refresh the dashboard.
According to the estimates of Ukrainian affiliate networks, up to 18-25% of traffic in affiliate programs can be fraud. For large arbitrage teams, this means losses from $5,000 to $30,000 per month, which literally disappear in reports under the guise of “active users.”
Manual checking doesn’t work anymore. Fraud has become smarter, faster, and disguises itself as the best campaigns. But AI is no longer on the side of fraudsters – now it is the one that catches them, analyzing behavior, click patterns, and data with an accuracy that people could only dream of.
5 signals that indicate fraud
Abnormal conversion rate
If you get dozens of registrations or subscriptions in a few minutes, this is not always a sign of successful creativity. Often, it’s a signal that the traffic is not quite “live.” In a typical situation, 300 clicks in 5 minutes from one region may look like a viral surge, but user behavior reveals artificiality: all transitions occur at almost the same time, with the same CTR and no real activity after registration.
In reports, this situation manifests itself as uniform traffic without fluctuations, instant subscriptions after a click, or repeated time intervals between conversions. For a live audience, this is atypical – users react at different times, with delays, browse pages, and return later.
AI systems analyze the chronology of clicks and identify so-called time-based anomalies. The algorithm is trained on real user behavior patterns and notices suspicious “spikes” – when conversions come too quickly or evenly. Such models allow you to automatically cut off suspicious traffic segments before they reach the final reports.
For an arbitrageur, this means a simple action: view statistics not only by the number of leads, but also by time. If the traffic curve looks too perfect, you are probably looking at a perfectly tuned fraud.
Non-synchronous GEO or device patterns
Another common fraud signal is a gap between the source of the click and the place where the conversion comes from. When traffic officially comes from Ukraine, but registrations suddenly appear from Vietnam or Nigeria, it’s not a coincidence. Such discrepancies usually indicate location spoofing, VPNs, or proxy farms.
Fraudsters create entire schemes to mask the source. They run botnets on rented servers, use mobile emulators that “pretend” to be different phone models, or replace browser and system data to look like a new user. As a result, the affiliate sees beautiful statistics from different countries, although the real traffic may come from the same IP network.
AI analytics helps to identify such asynchronies. Algorithms compare the chain of events – clicks, views, conversions – and record when the geolocation, device, or connection type changes too dramatically. The systems learn to recognize the behavioral sequence of real users: how they navigate between pages, how they change devices, how much time they spend between actions. When this logic is violated, AI marks traffic as suspicious.
Arbitrageurs should regularly check the consistency of GEOs, IP addresses, and device types in reports. If registrations suddenly “leave” for another country or all conversions come from the same provider, it is almost always a signal that a fraud is already working in your campaign.
Same behavioral patterns
Real users behave randomly. They scroll through the page, pause, go back, and click on different elements. It is this unpredictability that distinguishes live traffic from bots. When all clicks in reports have the same session duration, the cursor follows the same trajectory, and users click at the same points on the screen, this is a typical example of automated behavior.
Heatmap analysis helps to see this literally. If the heatmap shows that hundreds of visitors “click” on the same coordinates or leave the page after exactly the same time, it means that a script or bot script is working. Fraudsters create such template patterns specifically to bypass basic affiliate filters.
AI systems have learned to recognize these repetitive patterns in seconds. The algorithms analyze behavioral metrics: cursor speed, click order, scroll depth, time to conversion, and compare them with the average performance of a live audience. If the match exceeds a certain threshold, the system automatically classifies traffic as suspicious.
To detect such activity, the most commonly used services are ClickCease, FraudScore, 24metrics and TrafficGuard. They integrate with trackers, track user behavior in real time, and generate reports indicating the sources of suspicious clicks.
The practical conclusion is simple: if user behavior seems too “neat”, the same speed, the same transitions, the same movements – this is not traffic discipline, but a well-configured fraud.
Dubious quality of leads
The most dangerous type of fraud is the one that looks like a successful campaign. All metrics are growing, registrations are stable, but after a week, the lead source “sags”. There’s minimal activity in CRM, users don’t take any action, and LTV for 7 days is zero. Such leads are formally verified, but have no real value for the business.
Fraudsters have learned how to create “live” users who behave convincingly for the first few hours: open emails, click on banners, add products to the cart. But then there is silence. For an advertiser, this looks like weak engagement, although in fact these are algorithmically generated accounts.
AI models help to distinguish such fake users before they spoil analytics. Behavioral prediction systems analyze thousands of parameters, such as form filling speed, time between registration steps, device type, and repeatability of actions, and generate a lead health score, i.e., a lead health score. If the score is below a certain threshold, the lead is automatically marked as risky.
Many affiliate platforms have already built similar algorithms into their dashboards. Machine learning allows not only filtering suspicious users but also predicting the quality of traffic from specific sources. This helps arbitrageurs to quickly adjust campaigns, reducing the cost of low-quality leads and increasing ROI.
If your numbers look good, but sales aren’t growing, check if you’re feeding your analytics with bots instead of collecting real customers.
Inconsistency of UTM tags and parameters
UTM tags are the language that a campaign uses to communicate with analytics. When these parameters start to “lie”, the system sees something completely different from what is actually happening. One of the most invisible types of fraud is tracking manipulation. An affiliate can consciously or automatically replace UTM tags, duplicate parameters, change the source or campaign to show more traffic, or take credit for other people’s conversions.
In reports, this looks like chaotic or illogical data: different campaign names lead to the same landing pages, user IP addresses are repeated, and the time of transitions does not match the tracker data. In some cases, affiliates connect third-party redirects to “rewrite” the parameters to their ID. Such schemes are difficult to detect manually, especially when a campaign operates with a large volume of clicks.
AI models automatically analyze the structure of UTM tags, detecting anomalies in the relationships between sources, IPs, timestamps, and user agents. The algorithm compares the expected sequence of events with the real one, looking for duplicates or suspicious parameter matches. For example, if the same user appears in different campaigns with different UTM sources, the system immediately signals a possible manipulation.
The use of AI for tracking validation allows partners to avoid budget leaks, block suspicious sub-partners in a timely manner, and maintain data integrity. For an arbitrageur, it is not just a matter of analytics, but a matter of trust in their own numbers. If the tags do not match, you cannot be sure that the ROI is really yours and not generated by the fraud.
What’s next: trends for 2025-2026
Fraud does not stand still. If earlier proxies and botnets were the main weapon, today fraudsters are already testing AI bots with behavior as similar to humans as possible. They can imitate random clicks, scroll pages at different speeds, take breaks between actions, and even simulate mouse movements. Such systems are trained on real user patterns and are able to deceive basic anti-fraud filters.
That is why the industry is entering a new phase – AI vs AI. Now, analytical systems do not just detect data deviations, but are engaged in a real “arms race” with fraud algorithms. Artificial intelligence learns to recognize the smallest inconsistencies: microsecond delays in clicks, unnatural routes between pages, repeated patterns of activity time.
In 2026, analytics will become not just a control tool but a mandatory layer of every funnel. Those who integrate AI models at the stage of traffic collection and processing will gain a competitive advantage. The rest risk being left with “beautiful” numbers that have nothing to do with real users.
The future of arbitrage is not about more clicks, but about smarter data. And the winners will be those who learn to catch fraud before it eats a part of the profit.