Building AI to Implement, Inspect and Improve super forecasting among organizations

Do we really need to keep on collecting all this "DATA" into sheets or tables or data-frames using misguided platforms? What would data collection lead to? Nested if-else's or Ctrl-Shift-L across the sheets and then dash-boarding?

Few months back, we started building AI/ML based algorithms that could automatically make science out of the data created without a need of managed service or a platform provider that keeps on collating this data in a unified manner. Remember, collecting data isn't important, driving science out of it is, and keep on doing it for different data silos is required, that is what will unleash the power of super forecasting aka artificial intelligence.

When it comes to advertising, banking, healthcare etc. Data means privacy, it means human's choices and believes, and in no circumstances we should be leading the industry into a perspective of collecting all these data silos under a single hood.

Building this super forecasting capability to detect fraud in advertising industry was one of the first problems we chose to solve, I mean look at the data being generated in the industry, it's huge.

Look at the below use-cases our algorithms have been intelligently learning on.

Segregating Fake CPM traffic (Brand Campaign)

This particular CPM campaign was being executed over a fake google play application which had an inventory of less than 5% humans. A systematic approach to create fake impressions was visualised using machine learning algorithm.

2. Segregating Fake CPI/CPA traffic (Performance Campaign)

This particular bot operation is way to smart, it would mimic and execute human behaviour, falling inside the right ( time to install), create fake attribution events to evade fraud detection. Using state of the art ML algorithm, we were able to bust this operation which was costing advertiser over 15,000 USD for 100,000 performance installs/events.

We are in constant quest to create solutions to the most pressing problems arising out to growing cyberspace. We need to constantly evaluate platforms not on the capability to collect and dash board large amount of data, but by their capability to build algorithms that can aid the building of true artificial intelligence.

"An unsophisticated forecaster uses statistics as a drunken man uses lamp-posts - for support rather than for illumination. "