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adjust Releases Fraud Prevention Suite
adjust's solution seeks to actively prevent user acquisition fraud before it is accepted and paid for by advertisers.
Posted Feb 17, 2016
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adjust has added to its mobile attribution and analytics system with the Fraud Prevention Suite, a set of tools that analyze and intervene in hundreds of millions of user acquisition workflows to prevent fraudulent activity from claiming payouts.

Early beta results of the Fraud Prevention Suite indicate a substantial improvement in campaign activity with an estimated $1.7 million identified within 17 days, based on recent CPI rates of fraudulent charges.

"The Fraud Prevention Suite gives our clients a completely new way to proactively mitigate potential fraudulent charges and optimize their user acquisition spend across their entire user acquisition stack," Paul Müller, adjust's co-founder and chief technology officer, said in a statement. "We're uniquely positioned to offer this kind of solution in the market because our technology is directly embedded in all the advertising our clients run, so we can analyze every interaction as it happens and change how that data is interpreted on the fly. This is unlike reactive, after-the-fact analysis tools that are unable to prevent corrupted datasets and illegitimate payouts."

adjust's Fraud Prevention Suite specifically targets the following three typical approaches by fraudulent publishers:

  • Anonymous IP Filtering is the first implementation to actively reject pay-outs for simulated traffic originating from data centers or other illegitimate locations.
  • Purchase Verification synchronously vets and verifies purchases, allowing app publishers to block cost-per-acquisition payouts or revenue shares from faked purchases.
  • Click Spam Distribution Modelling analyzes the aggregate distribution of users acquired to rapidly prevent apps from faking background clicks and thereby claiming organic traffic as paid.

In January, adjust ran initial beta tests of its product suite with customers, like San Francisco-based HotelTonight, by sampling a traffic flow that included more than 400 million installs to generate an overview of fraudulent activities. This initial test targeted only the portion of paid user acquisition from major U.S. and European brands that were generated from anonymous IPs, a strong indication that the traffic had been tampered with or falsified.

"Controlling the quality of our traffic, and thereby preventing fraudulent activity, is a key concern for us. adjust's Fraud Prevent Suite is the first solution we've found that is able to proactively intervene, and we're really excited about using adjust for that purpose," said Brian Han, head of growth at HotelTonight, in a statement.


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