Problem: Click fraud is done to increase fake attribution or organic hijacking. It either is a sudden injection of click just before the app opening for receiving last click attribution or spamming the campaign with multiple clicks from the same device to claim last click attribution

Solution Traffic pattern analysis to check the last click vs true click for the conversion, based upon multiple variables like TTI, Referral URLs, bursts, relative comparisons (google, FB, search, etc.), helps in highlighting the issue of injection or spamming

Problem: Networks/ publishers use bots, vpns and simulators to drive traffic & engagement to your campaigns.

Solution mFaaS detects virtual private networks, bots and simulators basis device based traffic analytics, pattern analytics and through it's various integration 3rd party data systems.

Problem: Publishers use fake devices, simulators, etc. to trigger fraud intent signals - impressions, clicks, conversions, events, etc. These are done through sophisticated machines which are able to dupe the last click attribution mechanism of the MMPs through SDK Spoofing, etc.

Solution mFaaS is able to read through multiple attributes of the devices - Google Ad ids, Apple idfas, Android ids, imeis, etc. along with other user agent based properties to detect the genuinity of the device/ user. mFaaS SDK can detect motion, angle, usage of device screen, etc. through gyroscopic features of devices. it's ML models detects cyclical & non-cyclical behavioural anomalies.

Problem: Publishers drop old obsolete APKs on users devices on redirect or pop-up/pop-under traffic without users' consent or know how. These are unintentional app packet drops which they use to claim false attributions.

Solution Detection of app version behavior plus patterns across multiple organic & programmatic traffic sources and comparing it with network channels. The unintentional app packet drops have higher densities from obsolete app packages.

Problem: Publishers drive traffic from the non-targeted geos on a campaign

Solution Checking traffic pattern inbound for its geo (where it is coming from) vs where it was intended from.

Problem: Mixing of them incent traffic with non-incent traffic by publishers to meet the campaign objectives

Solution mFaaS use various ML based clustering algorithms to highlight incent mixing.