Weather it is referred to as ad fraud, click fraud or even mobile click fraud, the crime itself is a costly one. Tens of billions of dollars are lost every year through fraudulent clicks and ad impressions. In the fight against ad fraud, many experts turn to a practice known as fraud scoring.
FraudBlocker.com is a company that develops and maintains a leading ad fraud protection solution by the same name. Fraud scoring is one of the features that comes with its software by default. Through fraud scoring, the software can more accurately determine the likelihood of fraudulent traffic and activity. Highly suspicious activity can lead to automated IP address and device ID blocking.
For all intents and purposes, fraud scoring is a predictive modeling technique that looks at a variety of characteristics and assigns a score accordingly. The higher the score, the greater the chances that a particular activity or source of traffic is fraudulent. Both human security experts and fraud protection software are trained to respond to unusually high scores.
In order to be effective, predictive modeling requires at least some measure of deep machine learning and artificial intelligence. Data needs to be gathered, crunched, and analyzed using a variety of comparative data sets and historical data patterns. A good scoring solution should become more accurate with time simply because it has more data to work with.
Fraud scoring is built on four key elements, or components if you will. These are:
- Data Collection – The heart and soul of fraud scoring is data. The more data, the better. Both software and human security experts look at everything from IP addresses to traffic sources and transactional data. No piece of data is left behind.
- Data Analysis – Collected data needs to be analyzed to make it useful. Data is crunched and then divided into relevant variables and attributes. From there, the data can be combined to facilitate scoring.
- Data Modeling – Data analysis ultimately leads to data modeling, a scenario in which models are created for the purposes of determining which types of traffic and behaviors constitute the most serious risks.
- Model Training and Validation – The best data models are then trained and validated to ensure optimal results. An optimized model becomes a particularly good predictive tool that leads to more accurate scores.
To be clear, none of this happens overnight. Developing an automated tool capable of effective fraud scoring requires a ton of data and a lot of time to develop, maintain, and validate models. But once those models are deployed, they tend to do a particularly good job.
It is also worth noting that fraud scoring is only one aspect of comprehensive fraud protection. Scores should be utilized in a very intentional way. For example, setting up ad fraud prevention software to automatically block IP addresses based on score could be either good or bad.
Set the score threshold too low and you could end up blocking very valuable but legitimate traffic. Set the threshold too high and you could still be allowing too much bad traffic to get through. With that in mind, it’s wise to have human security experts monitor fraud scoring.
Thanks to deep learning and artificial intelligence, fraud scoring works pretty well. More anti-fraud software developers are including it in their packages, knowing that it can make identifying and stopping fraud easier. How about your ad fraud protection software? Does it utilize state-of-the-art fraud scoring? If not, it might be time to look for a new package.