Here are the major methodologies we use.
Online fraudsters tend to commit bad acts repetitively. That’s where our massive fraud intelligence database comes in. It includes IP and forensic reputation data updated in real time for reliable information about just about every visitor. What’s unique about our database is that it’s always learning from our clients and evolving through the work of our data science team. It’s there to make the online ad world a safer place for all of us.
Our innovative technology allows us to detect if ads are seen by real humans and determine for how long they are exposed. Beyond simple viewability, we can tell you when an ad has been intentionally hidden behind other ads or has been placed outside a website’s bounds. Our solution overcomes the challenges posed by multiple iframes and works with all major browsers to help you optimize well beyond the IAB guidelines for ad viewability.
True URL allows clients to see the URL of the top browser window even when the ad has been iframed multiple times or the publisher is not reporting an accurate URL to the exchange. It helps address the problems of botnet traffic, brand safety and inaccurate targeting.
Forensiq’s expertise in automated fraud detection has enabled us to fight ad fraud more effectively. Through our global presence on billions of impressions every day and sophisticated machine learning algorithms we can accurately identify traffic from botnets, hijacked devices, malicious script injection and other automated means. This means better detection plus the ability to stay steps ahead of the bad actors.
Our proxy unmasking technology uncovers proxies, compromised computers and botnets; all clear signs of fraud. These tactics are used frequently to generate fake impressions, clicks and conversions. We have an incredible wealth of experience looking at IP addresses and users based on their historical characteristics and behavior. We can see behind proxies to identify bad behavior before it affects your business in a negative way.
Fraudsters often use operating systems and browser manipulation to spoof their real identity and simulate real traffic. We look for anomalies within traffic to identify instances of device manipulation, where a fraudulent user or bot would display a specific pattern within the HTTP request and JavaScript characteristics of the browser.
Fraudulent partners are able to steal credit for conversions through forced clicks generated using hidden iframes, browser toolbars and pop-under windows. These illicit tactics divert revenue from high-quality referring partners and force advertisers to pay for organic traffic, which leads to inaccurate attribution and negative ROI. Our algorithms consider behavioral recognition, anomaly detection and time patterns on the site.