How machine learning is a ‘requisite’ for ad fraud detection

By Abhinav Bangia

Marketers/advertisers are bundled with data today. They are collecting data behind every touchpoint the consumer makes, right from click data, install data, engagement data, etc.

In today’s world, there are 2 major activities marketers are involved in:

• Using click and install data, marketers keep investigating different forms of campaigns to drive bigger volumes down the digital funnel
• Using engagement data, marketers study channels of engagement and message throughout the lifecycle of a digital consumer to enable a higher LTV

However, this isn’t enough to study whether the incoming data is attributed correctly or not. Vanity matrices today are merely numbers on a digital dashboard, but the correctness is immensely suspected throughout.

Finding attribution manipulation can be problematic and estimating an analogical behavior of the traffic to a constant is merely impossible. For the same, because of the problem and the largeness of the data, it requires machine led understanding of the data over time.

Usage of filters, boundary conditions, threshold, etc gives a good descriptive statistical understanding of the data in hand and can estimate rule-based anomaly finding. However, this misses on predictive and prescription data science.

AI In Advertising Fraud

In order to build a true machine learning model, one must look at the data very closely and build a homogeneous learning model that only injects consumer journey behavior as a learning variable.

Examples of Common Ad Fraud Schemes in which ML helps :

Finding attribution manipulation is not easy. The objective behind the mere click manipulation is to hijack the last-click attribution model for monetary gains. Anyone with mere contacts of these adware/malware enabled apps can help one in growing the business in no time which in return fuels corruption, fake news, tax evasion and cross-border cyber warfare.

Detection for these kinds of traffic sources is a must, as it incentivizes the above, but also affects digital consumers and the country in the long run.

The author is a founder at Com Olho

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