AI-powered ad fraud detection and protection provided by mFilterIt serves as a robust defense for advertisers. The suite conducts real-time pattern analysis of events and engagements to identify and eliminate fraudulent activities on app and web platforms. Unlike traditional anti-ad fraud methods that rely on post-mortem analysis, mFilterIt's solution uses advanced data science techniques, machine learning, and AI to detect ad fraud in real-time.

The integration process is simplified, with options for JavaScript integration or SDK integration, depending on the deployment platform (app or web) and the level of protection required. SDK integration offers a data-rich solution, enabling unique event analyses. mFilterIt's solution excels in ad fraud detection, detecting 22% more ad fraud compared to industry benchmarks, as per internal analyses conducted in collaboration with key customers.

The solution performs over 70 checks to uniquely identify the origin of an event, distinguishing between human and bot-generated engagements. The layer of artificial intelligence, built on proprietary algorithms, plays a crucial role in the solution's architecture. It analyzes data patterns to differentiate between genuine and fake engagements, providing cognitive power to the suite.

In version 2.0, the solution has evolved to a proactive stage, allowing the validation of click integrity and preemptive blocking before an event occurs. This proactive approach is a game-changer, especially for advertisers heavily relying on non-affiliate marketing where prepayment is common, and recovering funds after detecting fake engagements is time-consuming.

With click integrity capabilities, advertisers can make informed decisions on where to allocate their advertising budget. mFilterIt, ad fraud detection solution, with its cognitive power based on an artificial intelligence engine, has been a key differentiator in the company's five-year journey, saving over $500 million for satisfied customers worldwide. The cognitive power enables the solution to think ahead and distinguish between genuine, unique human engagements and bot-generated interactions.

Source:- https://www.backlinkget.com/blog/using-artificial-intelligence-to-detect-real-humans/