Media buying faces significant threats from fraudsters who use bots, click farms, and other manipulation schemes to tamper with advertising budgets. The digital advertising industry loses billions of dollars each year due to fraud, making an effective anti-fraud strategy not just an option but a necessity. Let's explore key approaches to protecting campaigns from manipulation and fraud.
Types of Fraud in Media Buying
- Bot-Driven Ad Fraud – Automated scripts that mimic user behavior, clicking on ads and filling out lead forms.
- Click Spam – Fraudsters generating large amounts of clicks on ads with no genuine interest in the product.
- Install Hijacking – Intercepting mobile app installs with attribution manipulation, causing advertisers to pay for non-existent users.
- Fake Leads & Fake Conversions – Generating fake registrations and purchases that don't contribute to actual business growth.
- Viewability Fraud – Placing ads in hidden or invisible areas of a page to inflate impressions without actual audience engagement.
Anti-Fraud Technologies and Tools
- Behavioral Data Analysis Machine learning and AI can detect abnormal behavioral patterns typical of bots. For example:
- High click-through and website navigation speed.
- Lack of mouse movements during website interactions.
- Repetitive actions at identical intervals.
- IP and Geolocation Analysis
- Blocking suspicious IP addresses. Using blacklists to identify IPs with high activity density.
- Detecting VPN and proxy usage. Bots often hide their location via VPNs, proxies, or mobile networks.
- Identifying discrepancies between stated and actual user locations.
- Fingerprinting and Device Tracking Device fingerprinting technology tracks users’ devices and browsers, even if they use different IP addresses or clear their cookies. This helps:
- Detect abnormal matches in mass registrations.
- Eliminate duplicate users in CPA campaigns.
- Using Third-Party Anti-Fraud Solutions The market offers many anti-fraud platforms, such as:
- FraudScore – Traffic evaluation based on machine learning.
- Forensiq – Real-time monitoring of suspicious activity.
- Moat by Oracle – Protection against bot traffic and fraud based on viewability.
- Post-Click Metrics Analysis Key performance indicators (KPIs) help detect suspicious activity. Important parameters to analyze include:
- Average session duration (bots typically spend minimal time on a site).
- Percentage of repeat visits (unnatural spikes in activity may indicate fraud).
- Conversion to actual target actions (discrepancies between clicks and purchases).
Methods to Protect Campaigns from Fraud
- White- and Blacklists of Sources Creating lists of trusted and suspicious traffic sources helps filter out unreliable partners and platforms.
- Captcha and Other Validation Methods Using reCAPTCHA or honeypot techniques (hidden form fields) helps identify automated bot registrations.
- Optimizing Traffic Payment Conditions Transitioning to pay-per-action models (CPL, CPS) instead of pay-per-impression or pay-per-click.
- Validating leads before paying affiliates.
- Regular Audits of Partners and Traffic Periodically auditing ad networks, affiliates, and traffic sources reduces the likelihood of working with fraudsters.
