Anti-Fraud Solutions and How They Work
Let's kick off with rules-based solutions. For some time, these were the preferred choice for most advertisers as the options were very limited. Rules-based solutions employ techniques that identify when key characteristics and events exceed or fall below specific parameters.
But the catch is that these solutions only perform well when you know every single thing that plays a role in a specific type of fraud. There is a lot of observation that goes into creating a new rule since the rule must be thoroughly instructed to recognize very specific scenarios so it can confidently discern valid traffic from fraudulent traffic. And the issue with this type of approach is the high percentage of false positives/negatives it can lead to.
On top of all this, rules-based solutions are very easy to reverse-engineer by fraudsters given the fact that all rules are exhaustively predefined and they do not self-learn, making it extremely difficult to find new or smart fraud types.
For years now, many believed that a traditional solution provided enough coverage and protection against fraud - and to this day, unfortunately, many app developers and marketers still operate under the illusion that by keeping fraud levels low with the use of a rules-based solution, they are protected.
But the reality is that nowadays, fraud has become much more complex and sophisticated making it virtually impossible for traditional, rules-based solutions to meet the expectations and the level of protection that app developers require to fully safeguard their product.
One of the best approaches is to use intelligent technologies such as machine learning. Machine learning is a notably powerful tool on its own, but it's even better when it's coupled with deep expertise, continuous development of the technology itself, and different combinations of algorithms.