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Fraudulent devices were detected using AppsFlyer’s DeviceRankTM technology,
the industry's first fraud prevention solution to identify and exclude fraud at
the device level (unlike the current IP or app-level solutions). DeviceRankTM
leverages a proprietary big data-powered algorithm to build an anonymized,
multidimensional rating of every mobile device. Ratings are built from over
a dozen points of metadata, including device details, engagement history,
historical fraud modeling scoring, IP address and geolocation, transactional
data, verified transaction data and more. DeviceRankTM has already rated 98% of
all mobile devices across the globe.
Learn more at www.DeviceRank.me
With the explosion of mobile usage, advertisers have dramatically increased their mobile
ad spend. And with money pouring in, fraudsters have followed suit. But the data on
mobile ad fraud is still scarce. The IAB estimated that it cost advertisers $1.3 billion
worldwide, but this number is centered on analyzing fraudulent and invalid traffic driven by
brand marketing with little focus on performance app marketing.
As mobile app install spend is significantly rising, the impact of fraud on this growing
segment of mobile advertising has not been properly documented. Until now.
In this industry-first report, we have mapped out the scope of mobile app install and post-
install app engagement fraud, including a breakdown by country, operating system and
OS version. The data is based on AppsFlyer’s DeviceRankTM
technology, which accurately
detects fraudulent devices (read more on DeviceRankTM
below).
Mobile app install and engagement
fraud will cost advertisers across the
globe up to $350 million in 2016. This
figure is based on the DeviceRank
algorithm, which separates between
verified fraud ($100M in loss) and
suspected fraud ($250M in loss)
depending on the number of signals
it receives.
This forecast accounts for scenarios
such as mis-attribution due to
fraudulent click data, paid installs
from fraudulent devices, fraudulent
and simulated in-app events and the
impact of fraudulent data on lookalike
targeting and retargeting. Forecast
data is based on normalized ad spend
and install trends from January 2015
to September 1, 2016.
in 2016
$100MILLION
$250MILLION
VERIFIEDFRAUD
SUSPECTEDFRAUD
$350MILLION
Learn more at: www.DeviceRank.me

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The State of Mobile App Install & Engagement Fraud

  • 1.
  • 2. Fraudulent devices were detected using AppsFlyer’s DeviceRankTM technology, the industry's first fraud prevention solution to identify and exclude fraud at the device level (unlike the current IP or app-level solutions). DeviceRankTM leverages a proprietary big data-powered algorithm to build an anonymized, multidimensional rating of every mobile device. Ratings are built from over a dozen points of metadata, including device details, engagement history, historical fraud modeling scoring, IP address and geolocation, transactional data, verified transaction data and more. DeviceRankTM has already rated 98% of all mobile devices across the globe. Learn more at www.DeviceRank.me With the explosion of mobile usage, advertisers have dramatically increased their mobile ad spend. And with money pouring in, fraudsters have followed suit. But the data on mobile ad fraud is still scarce. The IAB estimated that it cost advertisers $1.3 billion worldwide, but this number is centered on analyzing fraudulent and invalid traffic driven by brand marketing with little focus on performance app marketing. As mobile app install spend is significantly rising, the impact of fraud on this growing segment of mobile advertising has not been properly documented. Until now. In this industry-first report, we have mapped out the scope of mobile app install and post- install app engagement fraud, including a breakdown by country, operating system and OS version. The data is based on AppsFlyer’s DeviceRankTM technology, which accurately detects fraudulent devices (read more on DeviceRankTM below).
  • 3. Mobile app install and engagement fraud will cost advertisers across the globe up to $350 million in 2016. This figure is based on the DeviceRank algorithm, which separates between verified fraud ($100M in loss) and suspected fraud ($250M in loss) depending on the number of signals it receives. This forecast accounts for scenarios such as mis-attribution due to fraudulent click data, paid installs from fraudulent devices, fraudulent and simulated in-app events and the impact of fraudulent data on lookalike targeting and retargeting. Forecast data is based on normalized ad spend and install trends from January 2015 to September 1, 2016. in 2016 $100MILLION $250MILLION VERIFIEDFRAUD SUSPECTEDFRAUD $350MILLION
  • 4.
  • 5.
  • 6.
  • 7.
  • 8.
  • 9. Learn more at: www.DeviceRank.me