Prevent Spam & User Abuse

Stop Large-Scale spam Attacks and Abusive Users

Bad actors use fake accounts, app cloners, and spoofers to send spam and abuse good users. Given that these types of attacks severely damage both user trust and a platform's reputation, businesses must know which tools the fraudsters are using, and be able to detect each abusive user and their device with unparalleled accuracy.

Create a Safer User Environment

Protect users from harmful content and abusive messages

Build Customer Trust and Loyalty

Enhance your platform experience to encourage customer retention

Reduce Unnecessary Friction

Reduce verification time and improve user experience

Case Study

A major portal for buying and selling cars had some concerns regarding some of their ad traffic and suspected that some of their website visitors were fake.

Immediately after integration, SHIELD’s real-time ad fraud prevention detected that over 80% of its paid traffic was not legitimate. Fraudulent advertisers had been returning fake clicks to meet their monthly quotas.

With this intelligence, the portal was able to make an informed decision as to the performance of certain advertisers. Within the first month of implementation, this then led to a 200% increase in ROAS.

Clear Insights, Comprehensive Analytics

We provide unparalleled visibility into the fraud tools harming your campaign in real time.

Complete Customisation For Every Campaign

Every business sees fraud differently, which is why our customisable risk thresholds allow you to dictate what bad means to you.

How It Works

SHIELD protects you from fraudulent ad clicks and fake app installs so you can reach real users and improve your advertising ROI.

We scan clicks and installs in real time to determine whether any malicious tools or techniques are being used. Our proprietary AI and machine learning technology, combined with our global network of over 1 billion user accounts, enables us to differentiate between real users and bots.

Stop Large-Scale spam Attacks and Abusive Users