The digital advertising market is evolving faster than ever. With the development of AI and the rise of fraudulent activities, the requirements for user behavior analysis are becoming stricter, forcing advertisers to adapt to new threats. Fraud detection accuracy is now determined not only by basic indicators but also by complex behavioral metrics that analyze user interactions with ads. Let’s explore the trending metrics for 2025 and how Kaminari Click helps the industry counter these threats.


Global Advertising Fraud Statistics in 2025

The problem of advertising fraud remains relevant on a global scale. According to Juniper Research, in 2020, the damage from ad fraud amounted to approximately $68 billion, with 24% of traffic being generated by bots used by fraudsters for scams and theft. Advertisers in the U.S. suffered the highest financial losses, reaching $23 billion.

WARC forecasts indicate that by 2025, global digital ad spending will exceed $1 trillion, with 75% of these funds directed to the digital segment. However, as investments in digital advertising grow, so does the risk of fraud, highlighting the need for effective anti-fraud measures.

In response to these threats, companies are increasing investments in anti-fraud solutions, real-time traffic analytics, and AI-based tools to detect suspicious activity in campaigns. Fighting fraud is becoming one of the key challenges for the programmatic advertising market in Europe and the U.S.


Key Trends in Behavioral Analysis in 2025

In-Depth Behavioral Analysis (Behavioral Fingerprinting) Fraudsters are increasingly using masking techniques and human behavior emulation. Therefore, analyzing traditional metrics (IP, User-Agent, Cookies) no longer guarantees protection. In 2025, special emphasis is placed on:

  • Content interaction patterns (mouse movements, time on page, scroll depth);
  • Reaction to triggers (click on an ad, cursor movement before a click, delays before actions);
  • Correlation of device and network data (dynamic IPs, mobile device emulation, spoofing attempts);

AI vs. AI: The Battle Against Next-Generation Bots AI has become not only a protection tool but also a foundation for creating new generations of bots. Fraudsters use AI algorithms to automate clicks, bypass anti-fraud systems, and simulate human behavior. Methods of combating fraud in 2025:

  • Generative models for bot detection – real-time behavior analysis to identify anomalies;
  • AI-based anomaly detection models – identifying inconsistencies in action sequences;
  • Hybrid anti-fraud systems – combining ML algorithms with manual moderation of suspicious traffic.

New Detection Methods: Beyond CPA & CPC Traditional CPC and CPA models have become vulnerable to fraud. Therefore, in 2025, advertisers are focusing on:

  • Post-click analysis metrics (analyzing real actions after a click: likes, comments, purchases);
  • Cohort analysis models (comparing user activity based on behavior before and after conversion);
  • Neural network predictive models (determining the likelihood that a user is real before engaging with an ad).

Bots in Advertising: New Threats and Protection Methods

Bot Classification

Let’s examine the main types of bots attacking ad networks:

  • Spoofing – falsification of user data (IP, browser data, device);
  • Crawlers – automated scanners that bypass websites and click on ads;
  • Ad Fraud Bots – simulate interactions with ads, inflating impressions and clicks;
  • Click Fraud Bots – create artificial CTR to drain competitors' ad budgets;
  • Retargeting Fraud – bots generating activity to be included in retargeting audiences;
  • SDK Spoofing – falsification of app installs and post-installation activity.

Example: In 2024, a major affiliate program discovered a bot network that mimicked mobile app installs. Data analysis showed that the average session duration was ten times lower than usual, and IP addresses were frequently changed via VPN services.

How Kaminari Click Fights Bots Using Behavioral Analysis

Kaminari Click employs multi-layer detection, combining pre-bid, behavioral analysis, and post-bid filtering. Key methods include:

Bot Detection Based on Behavioral Patterns

  • Action timing analysis – delay before a click, time between events;
  • Session-wide analysis – detecting illogical transitions and activity spikes;
  • Micro-analysis of movements – checking the naturalness of mouse and touch screen movements.

Fraudulent Traffic Segmentation

  • Grouping suspicious devices and IPs – detecting abnormal clusters;
  • Correlating actions with historical data – analyzing similar behavioral patterns;
  • Using AI for predictive analysis – forecasting the likelihood of fraud based on historical data.

Advanced Post-Bid Filtering

  • Logging all suspicious actions for further analysis;
  • A flexible reporting system – the ability to prove to advertisers that specific traffic is low quality;
  • Integration with ad networks and affiliate programs – automatic notifications about detected fraud.

Case Study: In one case, Kaminari identified a massive bot attack on push traffic. Analysis showed that 80% of clicks came from a narrow range of IPs, and user actions did not match real behavior patterns (no delays before clicks, no interaction with content after transitions). As a result, the client disabled this traffic source and reduced the fraud rate in their network by 65%.

Conclusion

In 2025, comprehensive AI protection against bots and fraud is becoming the key trend in behavioral analysis. Simple solutions no longer exist – ad networks, affiliate programs, and media buyers must use hybrid anti-fraud mechanisms. Kaminari Click remains at the forefront of this battle, offering intelligent detection algorithms and protection against new types of attacks. By investing in behavioral analysis and AI, advertising companies can minimize losses, protect their reputation, and enhance campaign efficiency.